RESERVOIR SIMULATION OF COMBINED WIND ENERGY

AND COMPRESSED AIR ENERGY STORAGE

IN DIFFERENT GEOLOGIC SETTINGS

by

Jessica L. Neumiller

A thesis submitted to the Faculty and the Board of Trustees of the Colorado School of

Mines in partial fulfillment of the requirements for the degree of Master of Science

(Petroleum Engineering).

Golden, Colorado

Date ______________

Signed: ________________________Jessica L. Neumiller

Approved: _____________________ Dr. Ramona M. Graves Thesis Advisor

Golden, Colorado

Date _______________

______________________________

Dr. Craig W. Van Kirk Professor and Head,

Department of Petroleum Engineering

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ABSTRACT

To meet the inevitable decline in hydrocarbon resources, renewable energy

sources, such as wind energy, should be implemented. This is a promising, but

intermittent energy source. In order to account for wind’s intermittency, large scale

energy storage can exist in combination with wind turbines. One of most advantageous

forms of large scale storage is Compressed Air Energy Storage (CAES). CAES is

designed to store off-peak energy to make it available for use during peak demand

periods. Currently, CAES plants are located in caverns, which are uncommon in

occurrence. In order to make CAES wind farms a reliable energy source, other

geological structures, such as depleted hydrocarbon reservoirs, need to be considered as a

storage option.

This study has used the ECLIPSE 100© black oil simulator to model CAES in its

typical cavern setting, in a hypothetical reservoir setting, and in a potential CAES wind

farm area in Wyoming. The cavern setting is modeled after the Huntorf CAES facility in

Germany. The purpose of the model is to obtain a pressure match based off of the

injection and production schedule of the CAES operations at various permeabilities. This

was done by decreasing the production rates of the facility after confirming that the given

rates were maximums. To model CAES in a reservoir, EZGEN was used to generate an

anticlinal structure with 20% porosity and an original permeability of 100 md. Using the

same rate schedule as with the Huntorf pressure match model, it was determined that a

100 md permeability is unreasonable with these high rates. Permeability had to be

increased to 1,000 md to obtain the modified Huntorf rates. In order to model a more

realistic scenario, wind speed and resulting power data were taken from the Medicine

Bow Wind Project site, near Medicine Bow, Wyoming. Using porosity, permeability,

and injectivity information from the surrounding Greater Green River Basin, four models

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were constructed simulating combined wind energy and CAES with different

geographical locations and geological properties. The higher porosity and permeability

models (Moxa 1 and Baxter 1) could obtain higher injection and production rates and

therefore, higher power outputs than the lower porosity and permeability models (Moxa 2

and Baxter 2).

The study showed that CAES can be used in actual reservoir settings. All four

GGRB basin models have high enough power outputs to validate the use of CAES. The

use of ECLIPSE 100© for CAES applications is also confirmed based on the successful

model results for three different settings. Finally, the GGRB has good potential for

combined wind energy and CAES. This study provided a sound first step, but future

work in this area needs to be done. This should include a more intensive reservoir model

with a detailed reservoir characterization. Additionally, the effects of leakoff in the

reservoir, water saturation, fracture effects, and the use of multiple wells and some

horizontal wells should be explored.

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TABLE OF CONTENTS

ABSTRACT.......................................................................................................................iiiLIST OF FIGURES...........................................................................................................viiLIST OF TABLES............................................................................................................xivACKNOWLEDGEMENTS..............................................................................................xvi

CHAPTER 1........................................................................................................................1INTRODUCTION...............................................................................................................1

1.1 Energy from Wind.....................................................................................................21.1.1 History of Wind Energy......................................................................................41.1.2 Wind Turbine Design..........................................................................................81.1.3 U.S. Wind Farm Examples................................................................................13

1.2 Scope of Research....................................................................................................151.3 Research Objectives.................................................................................................171.4 Application from Petroleum Industry......................................................................17

CHAPTER 2......................................................................................................................20LITERATURE REVIEW..................................................................................................20

2.1 Benefits of Large-Scale Energy Storage..................................................................212.1.1 Fuel Cells..........................................................................................................232.1.2 The Flywheel.....................................................................................................252.1.3 Superconducting Magnetic Energy Storage.....................................................282.1.4 Supercapacitors................................................................................................302.1.5 Underground Thermal Energy Storage............................................................312.1.6 Pumped Hydroelectric Energy Storage............................................................342.1.7 Compressed Air Energy Storage......................................................................36

2.2 Advantages of CAES over Other Storage Technologies.........................................432.3 Fundamentals of Reservoir Simulation...................................................................48

CHAPTER 3......................................................................................................................52MODEL STUDY OF CAVERN STORAGE....................................................................52

3.1 Description of Huntorf Facility...............................................................................523.2 Cavern CAES Inputs................................................................................................553.3 Cavern CAES Sensitivities and Results..................................................................703.4 Objective Functions.................................................................................................81

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3.5 Discussion of Cavern CAES Models.......................................................................83CHAPTER 4......................................................................................................................84VERIFICATION OF MODEL USE FOR RESERVOIR STORAGE..............................84

4.1 EZGEN grid input and Model Setup.......................................................................844.2 Reservoir CAES Sensitivities and Results..............................................................884.3 Reservoir CAES Model Comparison.......................................................................954.4 Discussion of Reservoir CAES Models.................................................................105

4.4.1 Comparison of Cavern Models and Reservoir Models...................................1054.4.2 Comparison of Reservoir Models...................................................................106

CHAPTER 5....................................................................................................................108CAES SIMULATION OF THE GREATER GREEN RIVER BASIN...........................108

5.1 Geology of the Greater Green River Basin............................................................1105.2 Model Inputs..........................................................................................................1155.3 Model Results........................................................................................................122

5.3.1 Moxa 1 Model Results....................................................................................1225.3.2 Moxa 2 Model Results....................................................................................1335.3.3 Baxter 1 Model Results...................................................................................1385.3.4 Baxter 2 Model Results...................................................................................141

5.4 Power Analysis......................................................................................................1475.4 Discussion of GGRB Models and Power Implications.........................................150

CHAPTER 6....................................................................................................................153CONCLUSIONS.............................................................................................................153

6.1 Major Results.........................................................................................................1536.2 Model Comparisons...............................................................................................1566.3 Model Conclusions................................................................................................1576.4 Recommendations for Future Work......................................................................1586.5 Final Discussion.....................................................................................................160

NOMENCLATURE........................................................................................................162REFERENCES................................................................................................................163APPENDICES..................................................................................................CD in Pocket

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LIST OF FIGURES

Figure 1.1 Wind Resource Map...........................................................................................3

Figure 1.2 Representative Size, Height, and Diameter of Wind Turbines..........................6

Figure 1.3 Decrease in cost of wind-generated Electricity..................................................7

Figure 1.4 Rotor Configurations of the HAWT...................................................................9

Figure 1.5 HAWT displaying major components..............................................................10

Figure 1.6 Sketch of CAES for a Wind Farm....................................................................16

Figure 1.7 Locations of natural gas storage facilities within the U.S................................19

Figure 2.1 Conceptual electricity chain currently used.....................................................20

Figure 2.2 Conceptual electricity chain with the addition of storage................................21

Figure 2.3 Benefits of energy storage along the electricity value chain............................22

Figure 2.4 Load profile of a large-scale energy storage facility........................................23

Figure 2.5 Composition of a fuel cell................................................................................24

Figure 2.6 Cross-section of a typical flywheel..................................................................25

Figure 2.7 A composite flywheel.......................................................................................27

Figure 2.8 SMES system design........................................................................................29

Figure 2.9 Principles behind a capacitor............................................................................30

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Figure 2.10 Conceptual design of ATES...........................................................................32

Figure 2.11 Conceptual design of a DTES system............................................................33

Figure 2.12 Operation of a pumped hydroelectric storage facility....................................35

Figure 2.13 Operations of a CAES facility........................................................................37

Figure 2.14 Aerial view of the Huntorf plant....................................................................40

Figure 2.15 Comparison of Huntorf and McIntosh facilities............................................41

Figure 2.16 Power ratings of major storage technologies.................................................43

Figure 2.17 Cost and performance of major storage technologies....................................44

Figure 2.18 Levelized Annual Cost of Bulk Storage Options...........................................45

Figure 2.19 Capital cost of major storage technologies....................................................46

Figure 2.20 Regions of the United States suitable for CAES............................................47

Figure 3.1 Components of the Huntorf facility.................................................................55

Figure 3.2 Daily Power Production and Associated Pressure Response...........................56

Figure 3.3 Actual dimensions of the Huntorf salt caverns................................................58

Figure 3.4 Cross-sectional view of the Huntorf Facility...................................................59

Figure 3.5 Plan view of the Huntorf Facility.....................................................................59

Figure 3.6 Relative permeability curves for water and air used in ECLIPSE 100© input.......................................................................................................................61

Figure 3.7 Pressures, temperatures, and air flow when emptying the caverns..................62

Figure 3.8 Comparison of actual and modeled data for initial model runs.......................70

Figure 3.9 Pressure Match with Varying Pore Volumes and 10,000 md Permeability.....72

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Figure 3.10 The injection rate for actual data and modeled data for different pore volumes..................................................................................................................73

Figure 3.11 The production rate for decreased pore volumes for well P-1.......................73

Figure 3.12 The production rate for decreased pore volumes for well P-2.......................74

Figure 3.13 The pressure match obtained with lower production rates and the original pore volumes..........................................................................................................75

Figure 3.14 Change in pressure between modeled and actual values for lower production rates and 10,000 md permeability.......................................................76

Figure 3.15 Percent difference of actual and modeled pressure values for lower production rates and 10,000 md permeability.......................................................77

Figure 3.16 Pressure match with changing production and injection rates and different permeabilities..........................................................................................79

Figure 3.17 Change in actual and modeled pressure for changing injection and production rates with 10,000 md permeability......................................................80

Figure 3.18 Percent difference in actual and modeled pressure for changing injection and production rates with 10,000 md permeability................................80

Figure 4.1 Reservoir structure created with EZGEN for use in ECLIPSE 100© base reservoir model......................................................................................................87

Figure 4.2 Pressure Response for model runs with varying pore volume and 100 md permeability in a reservoir setting.........................................................................89

Figure 4.3 Production rates with varying pore volumes and 100 md permeability for Well P-1 in a reservoir setting..........................................................................89

Figure 4.4 Production rates with varying pore volumes and 100 md permeability for Well P-2 in a reservoir setting...............................................................................90

Figure 4.5 Percent difference of the total production rate for 100 md permeability with varying pore volumes....................................................................................91

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Figure 4.6 Pressure response for the 1,000 md model with varying pore volumes in a reservoir setting...................................................................................................92

Figure 4.7 Production rates of model runs with 1,000 md permeability and varying pore volumes for Well P-1 in a reservoir setting...................................................93

Figure 4.8 Pressure response with original pore volume and 10,000 md permeability in a reservoir setting...............................................................................................94

Figure 4.9 Pressure response of the three permeabilities with the original pore volumes in a reservoir setting................................................................................95

Figure 4.10 Production rate comparison for Well P-1 for the three permeabilities with the original pore volumes in a reservoir setting............................................96

Figure 4.11 Production rate comparison for Well P-2 for the three permeabilities with the original pore volumes in a reservoir setting............................................97

Figure 4.12 Percent difference of the total production rate for 100 md and 1,000 md permeability with the original pore volume...........................................................98

Figure 4.13 Pressure response of the 100 md and 1,000 md models with two times the original pore volume in a reservoir setting......................................................99

Figure 4.14 Production rates for Well P-1 for 100 md and 1,000 md permeabilities with two times the original pore volume in a reservoir setting...........................100

Figure 4.15 Production rates for Well P-2 for 100 md and 1000 md permeabilities with two times the original pore volume in a reservoir setting...........................100

Figure 4.16 Percent difference of the total production rate for 100 md and 1,000 md permeability with two times the original pore volume........................................101

Figure 4.17 Pressure response of the 100 md and 1,000 md models with three times the original pore volume in a reservoir setting....................................................102

Figure 4.18 Production rates for Well P-1 for 100 md and 1,000 md permeabilities with three times the original pore volume in a reservoir setting.........................103

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Figure 4.19 Production rates for Well P-2 for 100 md and 1,000 md permeabilities with three times the original pore volume in a reservoir setting.........................104

Figure 5.1 Map of the Greater Green River Basin with reserve information on existing oil and gas fields Source: (Kirschbaum and Roberts 2005)...................111

Figure 5.2 Major structures within the Greater Green River Basin.................................112

Figure 5.3 Subsurface depth of the Frontier formation within the Greater Green River Basin..........................................................................................................114

Figure 5.4 Location of Tip Top field and Baxter Basin South within the Greater Green River Basin................................................................................................116

Figure 5.5 Reservoir structure created with EZGEN for use in Moxa 1 and Moxa 2 reservoir models...................................................................................................119

Figure 5.6 Reservoir structure created with EZGEN for use in Baxter 1 and Baxter 2 reservoir models...................................................................................................120

Figure 5.7 Moxa 1 injection rates for the 100 MMscf/day injection and 400 MMscf/day production model for one day..........................................................123

Figure 5.8 Moxa 1 production rates for the 100 MMscf/day injection and 400 MMscf/day production model for one day..........................................................124

Figure 5.9 Moxa 1 injection rates for the 100 MMscf/day injection and 400 MMscf/day production model for three days......................................................125

Figure 5.10 Moxa 1 pressure response for the 100 MMscf/day injection and 400 MMscf/day production model for three days......................................................126

Figure 5.11 Zoomed in Moxa 1 pressure response for the 100 MMscf/day injection and 400 MMscf/day production model for three days.........................................126

Figure 5.12 Moxa 1 injection rates for the 100 MMscf/day injection with 1 injection period of 75 MMscf/day and 200 MMscf/day production model for three days.......................................................................................................127

Figure 5.13 Moxa 1 injection rates for the actual initial schedule of 100 MMscf/day injection with 1 injection period of 75 MMscf/day and 200 MMscf/day

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production for three days compared to the new schedule with 3 injection periods of 75 MMscf/day for three days..............................................................128

Figure 5.14 Moxa 1 pressure response for the 100 MMscf/day injection and 200 MMscf/day production model for three days with 2 different injection schedules..............................................................................................................129

Figure 5.15 Zoomed in Moxa 1 pressure response for the 100 MMscf/day injection and 200 MMscf/day production model for three days with 2 different injection schedules...............................................................................................129

Figure 5.16 Moxa 1 pressure response for the 50 MMscf/day injection and 200 and 100 MMscf/day production model for three days.........................................130

Figure 5.17 Zoomed in Moxa 1 pressure response for the 50 MMscf/day injection and 200 and 100 MMscf/day production model for three days...........................131

Figure 5.18 Zoomed in Moxa 1 pressure response for the 1 MMscf/day injection and 4 and 2 MMscf/day production model for three days...................................132

Figure 5.19 Moxa 2 injection rates for the 100 MMscf/day injection and 400 MMscf/day production model for one day..........................................................134

Figure 5.20 Moxa 2 production rates for the 100 MMscf/day injection and 400 MMscf/day production model for one day..........................................................135

Figure 5.21 Zoomed in Moxa 2 pressure response for the 100 MMscf/day injection and 400 MMscf/day production model for one day............................................135

Figure 5.22 Zoomed in Moxa 2 pressure response for the 20 MMscf/day injection and 80 and 40 MMscf/day production model for three days...............................136

Figure 5.23 Zoomed in Moxa 2 pressure response for the 1 MMscf/day injection and 4 and 2 MMscf/day production model for three days...................................137

Figure 5.24 Baxter 1 pressure response for the 100 MMscf/day injection and 400 and 200 MMscf/day production model for three days.........................................139

Figure 5.25 Baxter 1 pressure response for the 50 MMscf/day injection and 200 and 100 MMscf/day production model for three days.........................................140

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Figure 5.26 Baxter 1 pressure response for the 1 MMscf/day injection and 4 and 2 MMscf/day production model for three days......................................................140

Figure 5.27 Baxter 2 production rates for the 100 MMscf/day injection and 400 MMscf/day production model for three days......................................................142

Figure 5.28 Baxter 2 pressure response for the 100 MMscf/day injection and 400 MMscf/day production model for three days......................................................143

Figure 5.29 Baxter 2 production rates for the 25 MMscf/day injection and 100 MMscf/day production model for three days......................................................144

Figure 5.30 Baxter 2 pressure response for the 25 MMscf/day injection and 100 MMscf/day production model for three days......................................................145

Figure 5.31 Baxter 2 pressure response for the 25 MMscf/day injection and 50 MMscf/day production model for three days......................................................146

Figure 5.32 Baxter 2 pressure response for the 1 MMscf/day injection and 4 and 1 MMscf/day production model for three days......................................................147

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LIST OF TABLES

Table 1.1 Installed wind capacity for world regions...........................................................4

Table 2.1 Properties of various flywheel building materials.............................................26

Table 3.1 Specifications of the Huntorf CAES facility.....................................................53

Table 3.2 Comparison of actual Huntorf data to Cavern CAES base model data.............57

Table 3.3 Gas-deviation factor values for various pressures.............................................65

Table 3.4 Gas Formation Volume Factors for various pressures......................................66

Table 3.5 Lee et al. viscosity calculations.........................................................................67

Table 3.6 Daily schedule for the Huntorf facility..............................................................68

Table 3.7 Conversion factors for oilfield units to metric units..........................................69

Table 3.8 Schedule of injection and production rates with the best pressure match.........78

Table 3.9 Objective functions for Cavern CAES base model and the described sensitivities............................................................................................................82

Table 4.1 Injection and production rates for each well used in Sensitivity C and for Reservoir CAES.....................................................................................................85

Table 4.2 Summary of maximum percent difference between actual production rates and modeled production rates for the three permeability models in a reservoir setting.................................................................................................104

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Table 5.1 Predicted energy values based on average wind speeds for Historical Data (1987 – 1992) and 2004 Data and Actual Energy values collected on wind turbines.......................................................................................................109

Table 5.2 Model inputs for the Moxa and Baxter models...............................................118

Table 5.3 Moxa 1 modeled injection and production rates.............................................122

Table 5.4 Moxa 2 modeled injection and production rates.............................................133

Table 5.5 Baxter 1 modeled injection and production rates............................................138

Table 5.6 Baxter 2 modeled injection and production rates............................................141

Table 5.7 Generated power and from the various production rates of the Moxa and Baxter models...............................................................................................149

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Table 5. 8 Comparison of CAES daily power output to amount of natural gas and coal necessary to achieve the same power over the five-hour production period...................................................................................................................149

xvi

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. Ramona Graves, for taking me in under not

very ideal circumstances. Thank you for understanding my situation and being willing to

jump right in and participate in my ongoing research. Your knowledge and guidance

have allowed me to produce a thesis that I can be proud of. The members of my

committee also deserve thanks. Thank you all for working with me and providing

valuable insight. Dr. Mark Miller, thank you for sticking with me through this journey

and being willing to talk with me about all facets of my research. Dr. John Curtis, thank

you for being an inspiration in the classroom and sparking my interest in the geologic

side of the petroleum industry.

I would also like to thank my remaining committee member, Dr. Dag Nummedal,

and the entire Colorado Energy Research Institute (CERI) organization. Thank you for

taking interest in my research and providing funding for the duration of my work.

Without your assistance, this project would not have been possible.

John Fanchi also deserves my appreciation for providing me with the idea for this

research and for helping me through the initial stages.

I am also grateful to Fritz Crotogino for all the insight you provided into the

operations of the Huntorf CAES facility. Without your help, I would have been lost in

the data.

Finally, a big thanks go out to my friends and family, especially Mike for

understanding when I wasn’t in the best of moods. Now I fully understand what you

were going through during the final weeks of your thesis writing.

xvii

CHAPTER 1

INTRODUCTION

The need for sustainable energy is an ever-increasing concern. The topic of peak

oil is under constant debate, leaving the energy supply for the future uncertain. The use

of fossil fuels also comes with the problem of a negative impact on the environment

during their extraction and use. In order to compensate for the inevitable decline in

hydrocarbon production and the detriment to the environment, alternative energy sources

should be considered. A promising renewable energy source is the ability to harness

wind through wind turbines and wind farms. Turbines have become highly efficient over

the years and can generate energy at a cost that is comparable with other sources.

However, wind is still an intermittent energy source. A process needs to be in place that

can help increase the efficiency of the wind itself. Excess wind needs to be exploited and

energy must still be accessible when the wind is not blowing.

Large scale energy storage systems offer solutions to accomplishing this task.

One of the most promising forms of large scale storage is a process known as

Compressed Air Energy Storage (CAES). CAES is designed to store off-peak energy to

make it available for use during peak demand periods. During the off-peak periods, a

motor can consume power to compress and store the air in subsurface structures. Then

during peak load periods, the process is reversed allowing the already compressed air to

return to the surface and drive turbines as the air is slowly heated and released. No

additional compression is necessary to drive the turbines because the enthalpy is already

included in the compressed air. Currently, CAES plants are located in caverns, either

1

mined rock caverns or solution-mined salt caverns. This type of setting is ideal for the

use of CAES, but these structures are also low in occurrence. In order to make CAES

wind farms a reliable energy source, other geological structures must be explored, such as

aquifers and reservoirs similar to those found with hydrocarbon production.

1.1 Energy from Wind

In order to understand the use of CAES in collaboration with wind farms, a brief

overview of the energy source is necessary. The potential of the wind, a history of wind

energy and the turbines associated with it, a look at the design of wind turbines and their

main components, and wind farm examples will be examined.

To create economical energy from the wind, strong frequent winds are ideal.

Figure 1.1 shows the wind potential for the U.S. The National Renewable Energy Lab

(NREL) took measurements at monitoring stations at various times throughout the year.

An annual average wind speed was then calculated and used to determine the amount of

energy that could be created based on the rotor of a wind turbine per square meter of area.

This value, expressed as watts per square meter, is used to determine a wind power class

ranging from one to seven. If a site is classified as three or higher, then this area is

suitable for wind farm development. A Class two or higher site has the potential for

running small wind generators (National Renewable Energy Laboratory 2000).

2

Figure 1.1 Wind Resource Map Source: (National Renewable Energy Laboratory 2000)

About 90 % of this available wind energy is located in the Great Plains. By the

year 2015, the U.S. wind capacity is expected to reach 12,000 MW according to the

Energy Information Administration. Worldwide, wind-generated energy is estimated to

be more than 100,000 MW by 2010. Just from 1993 to 2003, a 30% growth has been

observed in the industry. Table 1.1 shows the wind capacity in different parts of the

world and the increases in capacity that have been made (Patel 2006).

3

Table 1.1 Installed wind capacity for world regionsSource: (Patel 2006)

1.1.1 History of Wind Energy

The concept to harness the wind for practical uses has been around since 900 AD,

when the Persians constructed the first windmills. Windmills can still be seen across the

world to aid with mechanical applications, such as pumping water. The use of wind to

generate electricity first surfaced towards the beginning of the 20th century with the

production of small wind electric generators. Like many of today’s wind turbines these

small turbines, most notably the Brush turbine, had rotors with three blades and airfoil

shapes. The Jacobs turbine also surfaced towards the turn of the century and is

considered to be a direct resemblance of some of the modern small turbines. The most

significant U.S. built turbine, came in the late 1930s with the construction of the Smith-

Putnam machine. It was the largest turbine built during that time period and for a number

of years after. It featured a 53.3 m diameter and a power rating of 1.25 MW. The Smith-

4

Putnam was ahead of its time; too little was know about a turbine this size and,

consequently, it suffered a blade failure in 1945 and funding for the project was halted.

After the Smith-Putnam, U.S. interest in wind lost its appeal, but Denmark was busy

designing its own turbines and employing concepts such as aerodynamic stall and an

induction generator. These concepts helped Denmark establish its strong presence in the

wind industry during the 1980s, where it is still a dominant force today. Meanwhile,

during the 1950s, Germany’s Ulrich Hütter founded modern aerodynamic principles that

are still applied today (Manwell, McGowan and Rogers 2002).

Based on growing environmental concerns associated with fossil fuels, the idea of

wind energy re-emerged in the late 1960s. This trend didn’t really catch on in the U.S.

until the Oil Crises of the mid 1970s. The Carter administration pushed for alternative

sources of energy, including wind energy. New turbines were being designed by

government agencies and companies, such as NASA and Boeing, respectively. The

Public Utility Regulatory Policy Act of 1978 created incentives allowing wind turbines to

connect to the grid and forcing utilities to pay for the “avoided cost” associated with each

kWh generated. With these new turbines and incentives, wind energy was becoming

economically feasible (Manwell et al. 2002). In California, wind farms were beginning

to take form; however, many of the machines in these farms were still prototypes and

could not provide the desired energy. Since an investment tax credit was in place, instead

of a production tax credit, faulty machines could be used without any consequence of

economic loss. However, these tax credits were withdrawn by the Reagan administration

in the early 1980s, putting a halt on the wind rush (Gipe 1995). U.S. wind turbine

manufacturers began to go out of business and culminated with the downfall of the

largest U.S. manufacturer, Kennetech Windpower, in 1996. The Danish and German

producers designed turbines of better quality than the U.S. manufacturers, leading to the

complete move of turbine production to Europe. The size of commercial wind turbines

has increased from 50 kW to 2 MW over the past 25 years. Figure 1.2 below displays the

specifications of wind turbines over the years (Manwell et al. 2002).

5

Figure 1.2 Representative Size, Height, and Diameter of Wind TurbinesSource: (Manwell et al. 2002)

The major contributors to the acceleration of wind power use and technology according

to Patel are:

High-strength fiber composites for constructing large, low-cost blades

Falling prices of the power electronics associated with wind power

systems

Variable-speed operation of electrical generators to capture maximum

energy

Improved plant operation, pushing the availability up to 95%

Economies of scale as the turbines and plants are getting larger in size

Accumulated field experience (the learning-curve effect) improving

the capacity factor up to 40%

(Patel 2006)

Figure 1.3 demonstrates item 2 of Patel’s reasoning. Since 1980, the cost of using wind

to generate electricity has decreased significantly, making wind comparable to the cost of

other energy sources, even without tax incentives.

6

Figure 1.3 Decrease in cost of wind-generated Electricity Source: (Patel 2006)

The potential to keep wind on the increase as an alternative energy source is

apparent not only in the U.S. but also worldwide; wind resources could be even more

practical and efficient if issues such as intermittency could be made obsolete with energy

storage options.

1.1.2 Wind Turbine Design

One of the most important components of designing wind turbines is the

understanding of aerodynamics. Obviously, the wind moves the blades of a turbine, but

7

more specifically, it is the concept of lift. As wind travels along the blade, the wind on

the upper surface moves faster than the wind on the lower surface. This creates a lower

pressure on the upper surface and enables the blade to the lifted. The angle at which the

blade is placed determines whether the blade will experience lifting forces or stall. At

increasing angles, the air begins to flow in an irregular vortex or the air becomes

turbulent. This causes the low pressure area on the upper surface to disappear. When

this occurs, the blade is experiencing stall. This scenario is desired during high wind

speeds that the blade is not capable of handling. If stall is not achieved during periods of

high wind speed, then damage to system components can occur.

The wind turbine design that is used almost exclusively today is the horizontal

axis wind turbine (HAWT). The name arose from the concept that the axis of rotation is

parallel to the ground. The HAWT can be designed to have its rotors orientated upwind

or downwind (Figure 1.4).

Besides orientation, HAWT rotors are also classified by their hub design (rigid or

teetering), rotor control (pitch vs. stall), the number of blades (typically two or three), and

how the blades are aligned with the wind (free yaw or active yaw). The basic

components of a HAWT (Figure 1.5) consist of

1. The rotor and the hub used for support of the turbine

2. The drive train, which mainly consists of the gearbox, shafts, coupling,

mechanical brakes, and the generator

3. The turbine electrical switch boxed and control

4. The main frame and Yaw system, which provide a cover for the drive train

and its controls and the support for the upper machinery, respectively

5. The tower and foundation

6. A detached electrical system (grid connection) that supplies all the

components necessary to transfer electricity

8

Figure 1.4 Rotor Configurations of the HAWTSource: (Manwell et al. 2002)

9

Figure 1.5 HAWT displaying major components Source: (Patel 2006)

An overview of each major component will now be provided to supply a basic

understanding of the HAWT. Further details can be found in Manwell et al. (2002) and

Patel (2006).

1) The rotor is oftentimes considered to be the most important part of a turbine, in

terms of cost and performance. It consists of the hub and the blades of the turbine. The

blades utilize airfoils to generate the mechanical power. The width and length of the

blades are determined by the preferred aerodynamic performance, the maximum desired

rotor power, the airfoil properties, and the strength concerns. Most of the models in

10

production today utilize the three-blade design; however, some manufacturers can offer

two-blade models. Single-blade units were once produced, but are no longer in

production. In addition to the three blades, most models operate with the rotors in the

upwind position; however, models can be found that orientate their rotors downwind

(Manwell et al. 2002). Rotors are generally placed upwind because of the turbulence

created by the air current behind the tower. Turbines are typically designed to operate at

their maximum output with wind speeds of 15 m/s (33 mph). They are generally not

designed to operate at higher speeds because of the lack of reliable wind. Therefore it is

uneconomical to design turbines for increased maximum outputs. When the wind does

blow at speeds greater than the maximum output, the turbine must deal with the excess

wind in order to avoid damage (Gipe 1995). Pitch control and stall control are the two

methods available for accomplishing this task. On a pitch control turbine, the electronic

controller checks the power output of the turbine every few seconds. If the power output

becomes too high, then the rotor is signaled to turn slightly out of the wind. When the

wind returns to safe levels, the rotor assumes it original position. A stall controlled

turbine uses the concept of stall to manage high wind scenarios. Its blades are connected

to the hub at a fixed angle. The turbine has been aerodynamically designed so that when

the wind speed becomes too strong, turbulence is created on the non-wind facing side of

the motor blade. This prevents the lifting force from acting on the rotor, creating stall

(Danish Wind Industry Association 2004).

2) The drive train of the HAWT contains the rotating parts of the turbine. It

consists of a low-speed shaft, a gearbox, a high-speed shaft, support bearings, couplings,

a mechanical brake, and the generator (Manwell et al. 2002). The low-speed shaft

connects the hub of the rotor to the gearbox. It also contains pipes for the hydraulic

system that allow for the operation of the aerodynamic brakes. With a typical 1000 kW

turbine, the rotational speed of the low-speed shaft is between 19 – 30 rpm. The purpose

of the gearbox is to force the high-speed shaft to operate about 50 times faster than the

low-speed shaft. The high-speed shaft drives the electrical generator and has an

11

operational speed of around 1,500 rpm. It also contains an emergency mechanical disc

brake to be used during the failure of the aerodynamic brake or when the turbine is being

repaired (Danish Wind Industry Association 2004). The generator used on a HAWT is

either an induction or synchronous generator. Both of these designs allow the generator

to operate at nearly constant rotational speeds when it is connected to a utility grid. Most

turbine manufactures opt to use an induction generator because they are inexpensive and

can be simply connected to a grid. This generator type runs within a limited range of

speeds that are slightly faster than the synchronous generator.

3) The control system is imperative in the areas of turbine operation and

production of power. It consists of sensors, controllers, power amplifiers, and actuators.

Traditional control engineering principles are applied in the design of control systems for

turbines. Three guidelines must be followed and kept in balance when considering

turbine control systems:

1. Setting upper bounds on and limiting the torque and power experienced by the

drive train.

2. Maximizing the fatigue life of the rotor drive train and other structural

components in the presence of changes in the wind direction, speed (including

gusts), and turbulence, as well as start-stop cycles of the wind turbine.

3. Maximizing the energy production.

(Manwell et al. 2002)

4) The mainframe and yaw system provide the housing and orientation systems

necessary for turbine operation. More specifically, the mainframe contributes the

mounting and the proper alignment for the drive train components and the yaw

orientation system properly aligns the rotor with the wind, referred to as yawing

(Manwell et al. 2002). An electronic controller is responsible for the operation of the

yaw mechanism; it senses the wind direction using the attached wind vane. An

anemometer and a wind vane measure the speed and direction of the wind. These

measurements are transmitted to the electronic controller, which tell the turbine to start

12

the blades spinning at a minimum speed and stop their movement at the maximum speed.

The yaw system will then correct for the wind, but it will only yaw a few degrees at a

time (Danish Wind Industry Association 2004). Also included in this category is the

nacelle, which is a weather protecting cover for the above machinery.

5) The tower and foundation of a turbine support the nacelle and rotor. The

primary types of towers used today are the free standing type composed of steel tubes

(tubular towers), lattice or truss towers, and concrete towers. Smaller turbines also have

the option of using guyed towers (Manwell et al. 2002). Tubular towers are safer for

personnel because the ladder inside of the tube provides the access to the top of the

tower. The advantage of the lattice design is its cheap construction. Tower height is

typically 1 to 1.5 times the rotor diameter, but is usually at least 20 m (66 ft). A 1,000

kW turbine will normally have a tower 50 to 80 m (150 to 240 ft) high. Generally, it is

advantageous to have a taller tower because of the increase in wind speeds higher from

the ground (Danish Wind Industry Association 2004).

6) The final major component of a HAWT turbine is the electrical system. It

consists of a number of electrical components responsible for general operation and for

the transmission of electricity to a utility grid. Some examples are cables, switchgear,

transformers, power electronic converters, power factor correction capacitors, and yaw

and pitch motors (Gipe 1995).

1.1.3 U.S. Wind Farm Examples

Although a number of countries exploit wind energy on a much larger scale than

the U.S., the U.S. still has some large scale wind energy projects in operation or the

planning stages. An onshore and offshore wind example will be explored in this

subsection. The U.S. only has two offshore examples, one is Cape Wind, and the other is

off the coast of South Padre Island, both of which are still in the permitting phase. The

13

proposed Cape Wind facility will be explored for this research. The onshore wind

project, Foote Creek Rim, is the largest in the intermountain West. It is located near

Arlington, Wyoming and was the first of its kind in the state. Foote Creek Rim is a

treeless plateau bordering I-80 between Rawlins and Laramie. It has one of the highest

wind speeds in the country, with average wind speeds around 25 mph. This speed is 25 –

70 percent faster than most good wind sites. The turbines of Foote Creek Rim are

designed to operate at wind speeds between 8 and 65 mph. Above 65 mph, the turbines

automatically shut down; this is an important feature because winds in this area can reach

125 mph (Bureau of Land Management 2006). The turbines are also manufactured to

withstand the cold temperatures of the area, which can plummet to 30ºF below zero

(American Wind Energy Association 1999). The first phase of the project opened on

Earth Day, April 22, in 1999. Upon opening, the wind farm had an output of 85 MW

through the operation of 69 600-kW wind turbines, enough energy to supply power to

27,000 homes. After the addition of more phases to the project, Foote Creek Rim now

has an energy capacity of 134.7 MW through 183 turbines.

Cape Wind is the first proposed offshore wind farm in the U.S. The farm will be

located miles from the nearest shoreline off the coast of Nantucket Sound on Horseshoe

Shoal. Upon completion, Cape Wind will consist of 130 turbines that will be capable of

producing 420 MW. The Cape Cod and Nantucket Island areas have seen electric prices

double in the last five years. The installation of the Cape Cod facility will help to reduce

prices and supply the areas with ¾ of their energy needs. Permitting is expected on the

project until 2007 and the Cape Wind wind farm is predicted to be operational by the end

of 2009 (Cape Wind 2006).

14

1.2 Scope of Research

This research investigates the potential to implement CAES systems in porous

media and compares this option to the use of CAES in a more conventional cavern

setting. Using the ECLIPSE 100©, Black Oil Simulator, from GeoQuest Schlumberger,

the base case cavern model was constructed using data from the first CAES plant, the 290

MW E.N. Kraftwerke plant, located in Huntorf, Germany. This plant utilizes 2 mined

salt caverns for the storage of compressed air. This is designated as the base model

because the caverns represent a 100% porosity scenario. After calculating PVT

properties, such as gas formation volume factor and viscosity for air, necessary for the

ECLIPSE 100© input file, a history match of the pressure response associated with the

compression and expansion schedule of the Huntorf facility could be obtained through

the modeling of various sensitivities in Eclipse. These sensitivities include different

permeabilities, alternate pore volumes, changing production rates, and changing injection

rates. The main objective of these sensitivities is to obtain a pressure match and to ensure

that the specified rates are achieved in the model runs. The quality of the match was

verified with the calculation of different objective functions.

This optimal base model could then be converted to a model representing a

hypothetical reservoir to provide a sound judgment for the practicality of utilizing CAES

in porous media. The porous model was constructed using EZGEN, a grid simulator for

ECLIPSE 100©. The volume of the reservoir is determined by using the Huntorf model

volume as the reservoir pore volume and then calculating bulk volume with a 20%

porosity. The rate schedule decided on in the cavern modeling is carried over to the

reservoir model. Various permeabilities are used to create different grids for reservoir

model sensitivities. A base reservoir dataset can then be created in ECLIPSE 100© using

an EZGEN file with 100 md permeability. Figure 1.6 demonstrates the scenario

simulated for porous media.

15

Wind Power

Reservoir Production

well Injection

well

Figure 1.6 Sketch of CAES for a Wind Farm

The injection well is responsible for injecting the compressed air into the reservoir, while

the production well allows the compressed air to return to the surface. To satisfy the

Huntorf rate requirements, additional modeling of various sensitivities, such as different

permeability and alternate pore volumes are necessary.

In order to provide a real world application of CAES in porous media, different

injection and production rates are examined with a range of porosities and permeabilities

consistent with the Greater Green River Basin (GGRB). This area was chosen for study

because of its location in the Rocky Mountain region and its proximity to the Foote Creek

Rim wind farm described in Section 1.1.3. Additionally, parameters such as porosity and

permeability could be obtained for study in this region. With the combination of wind

energy and CAES, an additional energy source is possible that is both environmentally

friendly and cost competitive with existing energy options.

16

1.3 Research Objectives

The main objectives of this research are to determine if CAES can be used in a

reservoir setting and if ECLIPSE 100© is the appropriate tool to model this storage

technology. In order to accomplish these objectives, various ECLIPSE 100© models were

constructed in various geographic locations and geologic settings. By using known

pressure data from an injection and production schedule at Huntorf and achieving an

adequate pressure match, this model could be modified to a hypothetical reservoir model.

After matching the same injection and production rates as the Huntorf facility, this

idealized reservoir model could be applied to actual reservoirs within the GGRB. An

optimal injection and production schedule could be found based on the formation

properties and calculated power output based on modeled production rates.

Some other research objectives include determining whether or not the GGRB

would be a good location for the implementation of combined wind energy and CAES.

The ability for model comparison between the three main models is the final research

objective.

1.4 Application from Petroleum Industry

The process of CAES has a direct application to the petroleum industry. As

previously stated, hydrocarbon resources are on the decline and sustainable energy

sources need to be researched to ensure a smooth transition in the future. A petroleum

engineer is the ideal candidate to explore CAES. The same hydrocarbon reservoir issues,

such as fluid PVT properties, porosity, permeability, relative permeability, and

saturations, exist in the geologic structures associated with CAES. The same tools can be

used to evaluate these properties and reservoir simulators, such as Eclipse, can be used in

17

both instances. The combination of these abilities demonstrates the practicality of

applying petroleum engineering to the CAES process.

The idea of storage is not new to the petroleum industry; in fact it has been a

staple in the industry for a number of years through the concept of natural gas storage.

The first successful implementation of natural gas storage occurred in 1915, in Weland

County, Ontario, Canada. A depleted natural gas well was converted into a successful

storage facility. The first U.S. application of natural gas storage was located near

Buffalo, NY. The idea increased in popularity shortly after WWII and by 1979 a total of

7.5 Tcf of storage was present in 26 states. This amount of storage could be accounted

for in more than 399 pools (Katz and Tek 1981). The concepts behind natural gas storage

and CAES are quite similar and can be summarized as the storage of excessive energy in

the form of natural gas or air for use at a later date. The objectives of natural gas storage

and large scale energy storage are the same. These include compensating for

shortcomings in the energy market, such as peak demand, price volatility, weather, and

grid stabilization. Natural gas storage also relies heavily on natural gas prices. When

prices are low gas can be stored until prices increase to the desired level (NaturalGas.org

2004).

Natural gas storage occurs in depleted reservoirs, mines, aquifers or salt caverns

(NaturalGas.org 2004). Until 1950, natural gas storage only used depleted reservoirs as

its storage devices. Depleted reservoirs are the method of choice for storage because of

their low cost and the fact that hydrocarbons have already been produced out of the

reservoir. Porosity and permeability are the main factors driving natural gas storage in a

depleted reservoir. Porosity determines how much gas can be stored and permeability

monitors the flow rates of injection and production. If storage is desired in locations, not

close to a depleted reservoir, then an aquifer can be used. This is the least desirable

scenario because the water has to be removed from the aquifer, which can be costly.

Additionally, the geologic properties have to be determined since these are typically

unknown; this adds to the increased cost as well. The salt cavern is ideal for natural gas

18

storage, but as previously stated, these kind of structures are hard to come by

(NaturalGas.org 2004). Figure 1.7 shows the location of the existing natural gas storage

facilities within the U.S.

Figure 1.7 Locations of natural gas storage facilities within the U.S.Source: (National Energy Technology Laboratory 2004)

At the moment, CAES mainly occurs in salt caverns, which is ideal for the storage

of air and natural gas, but the expansion of CAES into depleted reservoirs would open the

door for a number of additional facilities. The idea behind natural gas storage and CAES

is the same. Therefore, CAES could exploit the same geologic structures as natural gas

storage. This research will explore the option of using CAES in a reservoir setting,

similar to that of natural gas storage.

19

CHAPTER 2

LITERATURE REVIEW

The electricity chain is currently thought of as having 5 key steps (Figure 2.1).

However, in order to create a competitive market and compensate for shortcomings such

as peak demand, grid stabilization, and price volatility, energy must be stored. In other

commodities, storage is presented as one of the steps on their commodity product

timelines. Electricity itself cannot be stored for future use, but the energy necessary to

create it can be, by means of fuel energy, potential energy of a stored fluid, mechanical

energy, and chemical energy. Figure 2.2 shows an adjusted electricity chain with the

addition of storage (Makansi 2001).

Figure 2.1 Conceptual electricity chain currently usedSource: (Makansi 2001)

20

Figure 2.2 Conceptual electricity chain with the addition of storageSource: (Makansi 2001)

2.1 Benefits of Large-Scale Energy Storage

The benefits from adding storage to the electricity chain can be seen in every step

of the process. Figure 2.3 conveys these benefits along with the industry challenges

associated with them.

21

Figure 2.3 Benefits of energy storage along the electricity value chainSource: (Makansi 2001)

Storage makes sense from both a consumer and supplier standpoint. With

storage, any excess energy produced from a variety of sources can be stored for later use

instead of being wasted. Similarly, if a period of peak demand is encountered, then

stored energy can be used to compensate. This allows power facilities to be designed for

high demand instead of peak demand, helping to decrease costs. Storing energy for use

during peak demand also allows transmission and distribution systems to operate at their

full capacities. This eliminates the need for newer or upgraded lines. Storing energy is

independent of weather; therefore shortages should be minimized with the addition of a

storage component. Additionally, storing energy for shorter time periods can aid in the

smoothing of small peaks and sags in voltage. If efficiency and cost savings can be

improved on the supplier end, then these improvements can be passed on to the consumer

in the form of decreased costs. The benefits of using storage to meet demand on a diurnal

basis are shown in Figure 2.4 (Makansi 2001).

22

Figure 2.4 Load profile of a large-scale energy storage facilitySource: (Makansi 2001)

Storing energy uses baseload generation more efficiently and eliminates the requirement

of peaking facilities. By storing energy during low demand periods and releasing this

energy during peak demand periods, significant price reductions can also be realized.

The need for storage is definitely apparent, especially for large-scale applications.

Large-scale storage can be thought of as being of the same scale as current energy

production methods. The following subsections will provide an introduction to the

methods available or currently under development for large-scale energy storage.

2.1.1 Fuel Cells

Fuel cells are an example of using stored chemical energy for conversion into

electrical energy. In most cases, a gaseous fuel is used as the energy source. Instead of

burning the fuel, it is reacted with oxygen in the atmosphere. This allows the energy to

be directly converted into electricity, thus increasing its efficiency and eliminating

23

pollutants. The most common and preferred fuel for fuel cells is hydrogen. The

combustion of hydrogen in oxygen only produces water and the energy density of

hydrogen is quite high when compared to other fuels. Hydrogen has an energy density of

38 kWh/kg, while the next highest of available energy options is gasoline, with an energy

density of 14 kWh/kg (Cheung, Cheung, De Silva, Juvonen, Singh and Woo 2006).

The composition of a fuel cell includes an anode and a cathode that are separated

by an electrolyte (Figure 2.5). Hydrogen is passed through the anode and oxygen is

passed through the cathode. This leads to the formation of hydrogen ions and electrons at

the anode. These hydrogen ions travel to the cathode by means of the electrolyte and the

electrons travel to the cathode through an external circuit. Water is formed at the cathode

through the combination of hydrogen and oxygen. The current of the fuel cell is supplied

by the flow of electrons through the external circuit.

Figure 2.5 Composition of a fuel cellSource: (Cheung et al. 2006)

24

Various types of fuel cells exist including alkaline, polymer electrolyte

membrane, molten carbonate, and solid oxide cells. These different types of fuel cells all

operate according to Figure 2.5, but vary in their electrolyte composition, operating

temperature, and efficiency. Additionally, regenerative fuel cells are in the development

stage. Just like traditional fuel cells, regenerative fuel cells use stored chemical energy

for the conversion to electricity. Their difference comes in how the energy is stored.

Instead of using hydrogen and oxygen to store energy, an electrolytic solution is

employed. Fuel cells are very efficient and do not produce any pollutants. Conversion

from gasoline and diesel engines to fuel cells in automobiles is a practical and viable

option (Cheung et al. 2006).

2.1.2 The Flywheel

Flywheels have been used for a number of years in steam boats and windmills as

a means for transferring energy. Simply put, a flywheel is a spinning disc with a hole in

the center for rotation (Research Reports International 2004). Figure 2.6 shows the

components of a typical flywheel.

Figure 2.6 Cross-section of a typical flywheelSource: (Cheung et al. 2006)

25

Initially, flywheels were composed of metal. The idea was to increase the mass of

the flywheel in order to increase the rotary inertia and subsequently store kinetic energy.

Flywheels can be designed to release a large amount of energy in a short period of time

or a small amount of energy in a longer time period. The first flywheels were designed

for long lasting energy storage, but this caused a decrease in rotational speed and an

energy output of just 5% of the total available energy of the spinning flywheel. Rectifiers

were added that increased the usable energy to 75%. This addition added an increase in

price and caused aerodynamic friction since these models operated in air. These

traditional flywheels were composed of steel, a low specific strength material when

compared to composites used in flywheel design (Table 2.1), and could only operate at

low speeds. Steel flywheels can produce 1,650 kW of power, released over a few

seconds (Cheung et al. 2006).

Table 2.1 Properties of various flywheel building materials Source: (Cheung et al. 2006)

Table 2.1 demonstrates that materials such as a carbon fibre composite can allow

for higher speeds due to its low density and high specific strength. For this reason, the

invention of composite flywheels (Figure 2.7) was justified. Composite flywheels are

smaller in size and capable of storing large quantities of energy for a short period of time.

26

A prototype composite flywheel reached speeds of 100,000 rpm with the speed at the tip

exceeding 1,000 m/sec, but most commercial models operate around 68,000 rpm

(Research Reports International 2004). 750 kW of power can be released over 20

seconds, or 100 kW can be released over an hour. One of the downsides of composites

designs is that mechanical bearings cannot be used because of the high rotational speeds.

Therefore magnetic bearings are utilized in composite designs. Magnetic forces are used

to levitate the rotor and eliminate frictional losses from rolling elements and fluids. The

heat produced by the ohmic losses in the flywheel becomes trapped because of the partial

vacuum created by aerodynamic losses. Additionally, advanced control systems are

necessary to operate the levitation system and the bearings have lower specific strengths

than the composite flywheels themselves causing a decrease in maximum flywheel speed

(Cheung et al. 2006).

Figure 2.7 A composite flywheel Source: (Cheung et al. 2006)

27

The current application of flywheels of all designs is to provide high output

voltage assistance to components or machines during a power surge or a shutdown.

Research is being conducted for the use of flywheels in other disciplines. For example,

flywheels are being used in the starting and braking of locomotives and as a battery

replacement in electric vehicles. There is still a lot of work to be done in perfecting the

flywheel design, but the concept is sound and their effectiveness has already been proven

in many applications (Cheung et al. 2006).

2.1.3 Superconducting Magnetic Energy Storage

A superconducting magnetic energy storage (SMES) system is designed to store

and instantaneously discharge large amounts of power. The flow of DC in a

superconducting coil that has undergone cryogenic cooling creates a magnetic field

(Research Reports International 2004). A SMES system utilizes this magnetic field to

store energy (Figure 2.8). Storage is made possible because of the superconducting

material of the coil. This material type enhances storage capacity because in a low

temperature environment, their electric currents experience a minimal amount of

resistance. This need for a low temperature environment creates a less than desirable

situation. The ability to maintain minimal resistance without the temperature restraint is

currently being investigated. The idea of a SMES system first arose courtesy of Ferrier in

1969, but the first system was not built until 1986, a 5 MJ system. For several years now,

SMES systems have been used to improve industrial power quality and to service

applications prone to voltage fluctuations (Cheung et al. 2006).

28

Figure 2.8 SMES system designSource: (Cheung et al. 2006)

Currently, SMES systems are capable of storing up to 10 MW and research

groups have been able to produce systems that can store hundreds of MW. Some

researchers believe that SMES systems have the potential of generating up to 2,000 MW

of power, but this has yet to become a reality. In theory, a 150-500 m radius coil can

handle a load of 5,000 MWh at 1,000 MW. The energy loss associated with SMES

systems is small at about 0.1% per hour; this loss is necessary for cooling of the system.

The minimal loss is due to the fact that the energy is stored directly within the magnetic

field generated by the coil. SMES systems are also environmentally friendly because

superconductivity does not yield a chemical reaction and no toxins are created (Cheung et

al. 2006).

29

2.1.4 Supercapacitors

Capacitors ability to hold DC voltages are one of the main components of electric

circuits. In the past, it was believed that capacitors could only store as much energy as

what is found in a normal battery. However, research has shown that this energy storage

method could be utilized on a much larger scale. Conceptually, capacitors could be used

to store energy for extended periods of time. Capacitors are composed of two conductive

parallel plates that are separated by a dielectric insulator (Figure 2.9). To create an

electric field, the plates hold opposite charges (Research Reports International 2004).

Unlike batteries that store energy chemically, capacitors stores energy in an electric field.

Figure 2.9 Principles behind a capacitorSource: (Cheung et al. 2006)

30

The first supercapacitor was not developed until 1997. Researchers at CSIRO

discovered that a significant amount of charge could be stored if the dielectric layer was

composed of thin film polymers and the electrodes were constructed out of carbon

nanotubes. A normal capacitor has an energy density of 0.5 Wh/kg, but these

supercapacitors can store four times the amount of energy. Supercapacitors are a

practical replacement for the traditional battery. Even though they have a larger energy

density than batteries, supercapacitors are not plagued with many of the disadvantages

associated with batteries. Batteries have a limited number of charge/discharge cycles and

take time to charge and discharge because of the chemical reactions necessary for the

process. The acidity of batteries is also harmful to the environment when the lifetime of

the battery has expired. Supercapacitors have an unlimited number of charge/discharge

cycles. They can discharge in milliseconds and can produce significant amounts of

currents. The lifetime of a supercapacitor is extremely long and no hazardous substances

are produced that could harm the environment. Currently, the main uses of

supercapacitors are within hybrid vehicles and handheld electronic devices (Cheung et al.

2006).

2.1.5 Underground Thermal Energy Storage

Using the subsurface to store energy is an efficient and aesthetically pleasing

option. Instead of constructing a large power plant on the surface, underground thermal

energy storage (UTES) can be used. The whole operation is invisible to the human eye

and even with extensive research, no drawbacks have been found with UTES (Cheung et

al. 2006).

One type of UTES is aquifer thermal energy storage (ATES), in which aquifers

are used for the storage process. Figure 2.10 shows the concept behind ATES.

31

Figure 2.10 Conceptual design of ATESSource: (Paksoy 2005)

With ATES, two wells are used, one for warm water and the other for cold water. During

the winter months, warm water is cooled and transported to the cold well. A heat

exchanger can then collect the energy for heating purposes. In the summer a reversal

occurs and the cold water is utilized for cooling. After the water is heated, it is stored in

the cold well (Paksoy 2005). ATES is environmentally safe since the water always

travels within the system. Additionally, no net loss of water occurs within the

32

subsurface. The only drawback of ATES is geologically based; only areas that are above

aquifers can utilize this storage method. Europe and Asia have implemented ATES the

most. By 1984, China had 492 cold storage wells to help cool down machinery and the

same type of system was constructed in Sweden, but on a smaller scale. Fossil fuel

consumption can be reduced by 80-90% with the use of ATES (Cheung et al. 2006).

Duct thermal energy storage (DTES) is a much more complex operation than

ATES (Figure 2.11).

Figure 2.11 Conceptual design of a DTES systemSource: (Paksoy 2005)

33

With DTES, holes are drilled to accommodate heat exchangers; these holes are

typically 50-200 m (164-656 ft). The efficiency of a DTES system is dependant on a

number of parameters, such as the ground temperature, the operational temperature of the

storage area, the conditions of the groundwater, and the thermal properties of the ground.

All of these parameters vary with location and can have a significant impact on efficiency

(Paksoy 2005). The first application of a large-scale DTES system was in Lulea,

Sweden. 120 holes at 60 m (197 ft) were drilled for the purpose of storing warm thermal

energy of about 70 ºC (158 ºF) to heat the local university. The largest DTES system in

operation today is located in Fort Polk, LA, U.S. It consists of 8,000 holes used to

provide warm and cool thermal energy to residents (Cheung et al. 2006).

Both UTES applications are environmentally friendly. UTES reduces the amount

of electrical energy necessary for heating or cooling. For cooling purposes, UTES

diminishes the use of mechanical or chemical cooling, which produces hazardous

substances that are responsible for damage to the ozone layer and rivers and lakes. A

reduction in cost is also seen because of the more accurate performance of UTES. For

heating applications, UTES replaces conventional heating methods that produce harmful

gases such as carbon dioxide and nitrogen oxide. UTES is a clean system due to the fact

that there is no net water loss. It is a practical alternative to countries that have low fossil

fuel reserves and it is also a viable option for storing solar energy for future use (Cheung

et al. 2006).

2.1.6 Pumped Hydroelectric Energy Storage

Pumped hydro is the oldest energy storage option, being in use since 1929.

Currently, 90 GW of power worldwide can be attributed to pumped hydro; this accounts

for 3% of the global energy capacity (Research Reports International 2004). The idea

behind a pumped hydro plant is relatively simple. Two large reservoirs are located at

34

different elevations. When energy is needed, water is released from the upper reservoir.

The water travels through high-pressure shafts, turbines, and eventually ends up in the

lower reservoir. When the demand for energy is low, water can be pumped back up into

the upper reservoir for future use. The general operation of a pumped hydro facility can

be viewed in Figure 2.12.

Figure 2.12 Operation of a pumped hydroelectric storage facilitySource: (Cheung et al. 2006)

The greater the vertical distance between the two reservoirs, the higher the head of the

system will be. A higher head means more energy; therefore, reservoirs with a large

vertical separation are desired (Cheung et al. 2006).

One of the best known pumped hydro plants is the Dinorwig plant located in

Wales. Construction began in 1976 and concluded in 1982. Upon completion, Europe

had its largest manmade cavern. The facility consists of six large pump turbines capable

35

of generating 317 MW each. Together the turbines can produce 1800 MW of power from

a water volume of 6 million m3 (212 million ft3) and an operating head of 600 m (1,969

ft). The system response is impressive with any of the turbines being capable of full

power in 10 seconds, if it is already spinning. Even if the system is at a complete

standstill, full power can be obtained in one minute.

Pumped hydro is one of the most effective forms of large-scale energy storage. It

has a storage capacity of over 2,000 MW and can store its energy for over half a year.

After half a year, leakoff and sealing properties become an issue. The response time to

get a system up and running is minimal and its operating cost is quite low due to its

straightforward design. Additionally, no environmental hazards are created from the use

of pumped hydro. This type of storage does have some disadvantages. The main setback

is the lack of suitable geologic formations. Two large reservoirs have to be present with

enough vertical distance to establish a usable head. This type of scenario is uncommon

and usually occurs in mountain settings where connection to a grid is unlikely and

construction is quite cumbersome. The capital cost of building a pumped hydro facility is

quite large due to the construction of dams and very large underground pipes. There are

parts of the world where the potential for pumped hydro exists. However, in the U.S.

most of the areas suitable for this technology have already been developed (Cheung et al.

2006).

2.1.7 Compressed Air Energy Storage

More emphasis will be placed on this energy storage technology since it is the one

chosen for continued research. A CAES system uses air pressure as its means for energy

storage; the air’s energy is readily available for extraction for future power generation.

Figure 2.13 shows the components and cycle of a CAES system. In principle, a CAES

system is a modification of a standard gas turbine generation cycle. In a typical cycle, the

36

turbine is connected directly to an air compressor. Consequently, whenever gas is

combusted in the turbine, about 2/3 of the energy output has to be used for compression.

CAES separates the combustion and generation cycle from the compression cycle. This

allows for air to be compressed using off-peak energy before it is required for energy

usage. This compressed air is stored in a subsurface reservoir, ready for extraction.

Typically, a solution-mined salt cavity, a mined hard rock cavity, or an aquifer is used for

underground storage. Above ground storage in tanks is also an option, but this alternative

is quite costly. Since the air is already compressed, a CAES turbine can generate three

times the amount of electricity as a simple cycle turbine that requires connection to the

air compressor (Ridge Energy Storage & Grid Services L.P. 2005).

Figure 2.13 Operations of a CAES facilitySource: (Ridge Energy Storage & Grid Services L.P. 2005)

37

In a CAES cycle, air is collected by the compressor, compressed, and then

injected into a reservoir via an injection well. Existing CAES technology requires the air

to be cooled during compression to allow for storing near ambient temperature. When

energy is demanded, the compressed air is extracted by means of a production well. The

air must be heated to avoid freezing the system components during air expansion in the

turbine. Currently, this is done through a recuperator and the burning of a fuel such as

natural gas. The air and natural gas are then expanded in the turbine to generate

electricity. This means that a traditional CAES plant is not completely pollution free;

CO2 emissions are still present (Greenblatt, Succar, Denkenberger, Williams and

Socolow 2006). It has been shown that carbon-neutral fuels are an option (Denholm,

Kulcinski and Holloway 2005) and, theoretically, it is feasible to store the heat generated

by compression separately from the air itself (Bullough, Gatzen, Jakiel, Koller, Nowi and

Zunft 2004). This would completely eliminate the need for fuel in a CAES facility. The

compressed air that enters the turbine does take the place of gas that would have been

used in the generation and compression processes, which significantly decreases CO2

emissions.

The principal equipment of a CAES facility can be split into four components:

“(i) the power island, (ii) the compression island, (iii) the underground portion, and (iv)

the balance of the plant (Ridge Energy Storage & Grid Services L.P. 2005). The power

island contains the turbine, the generator, and the recuperator. CAES designs typically

have two turbines, one high pressure (HP) air turbine and a low pressure (LP) gas turbine.

The HP turbine reduces risk by moderating pressure, temperature, and airflow upon

entrance into the LP turbine. These parameters are altered to values that the LP turbine

would experience if a compressor was still attached. The compression island provides

the required air volume to increase the pressure from atmospheric to the desired pressure

in the underground storage reservoir. A typical compression cycle for a CAES plant

contains a train of axial and centrifugal compressors. The compressors are connected to

the underground storage system. The subsurface facilities vary depending on the scope

38

of the project. In order to produce more power, a reservoir with a larger volume is

necessary. Typically, caverns are sized to store up to 50 hours of power and operate

between 950 – 1,250 psig. The balance of the plant contains the remaining equipment

that is pertinent to the operation of a typical power plant. This includes the cooling

tower, the switchgear, the substation, the plant distribution and controls, and the step-up

transformers. The main difference between the balance of a typical power plant and the

balance of a CAES plant is the auxiliary transformer. This component deals with the

power entering the plant from the grid. An auxiliary transformer in a CAES plant is

designed to accommodate 200 MW of power from compression. Also included in the

balance of the plant is the control room, maintenance facilities, fuel metering and control

valves, water treatment and related facilities, such as pumps and tanks (Ridge Energy

Storage & Grid Services L.P. 2005).

CAES has the advantage of being able to operate on a very large scale. It has a

very high storage capacity around 50-300 MW. It has the largest storage capacity of the

energy storage technologies due to its minimal losses. A CAES system is capable of

storing energy for up to one year due to the high quality seal that salt caverns create.

However, after a year’s time, pressure leakoff becomes a concern. CAES also has the

advantage of a fast start up time. If an emergency start is necessary, then 9 minutes is

needed to get everything up and running. Under normal conditions, a start-up time of 12

minutes can be expected. Conventional turbine plants require 20 to 30 minutes for a

normal start-up. Additionally, since all of the storage is beneath the surface, huge

expensive installations are unnecessary and the storage is invisible to the human eye.

The main drawback of CAES is its reliance on geologic structures. Underground

caverns are the only structures currently in use and these are quite rare in occurrence. An

objective of this research is to show that CAES can be conducted in other geologic

settings.

Currently, there are only two CAES plants in operation today; they are the 290

MW Huntorf facility, located in Huntorf, Germany (Figure 2.14) and the 110 MW

39

McIntosh facility, located in McIntosh, AL. E.N. Kraftwerke currently operates the

Huntorf facility; it was opened in 1978 and has been in successful operation since its

commencement. Upon opening, the main purpose of the Huntorf plant is as an

emergency reserve in the case of failure of surrounding power plants. Now, the plant

also serves as a supplemental energy source for the growing number of wind farms in

northern Germany (Crotogino, Mohmeyer and Scharf 2001). The Huntorf facility will be

explained in greater detail in the beginning of Chapter 3.

Figure 2.14 Aerial view of the Huntorf plantSource: (Crotogino et al. 2001)

The McIntosh facility was built in 1991 by Dresser-Rand and is currently owned

by the Alabama Electric Corporation. It made several improvements on the Huntorf

design. One of these improvements incorporated a recuperator (an air-to-air heat

exchanger) to recover the exhaust heat to preheat the cavern air upon entry into the

turbines, thus improving the heat rate and reducing fuel usage by 25%. It uses a roughly

cylindrical salt cavern about 300 m deep and 80 m in diameter (total volume of 5.32

40

million m3). Pressures range from 45 to 74 bar (653 – 1,073 psia) and the plant can

supply power for 26 hours. Start up times range from 9 to 13 minutes. Figure 2.15

provides a comparison of the specifics of the Huntorf facility to those of the McIntosh

facility (Ridge Energy Storage & Grid Services L.P. 2005).

Figure 2.15 Comparison of Huntorf and McIntosh facilitiesSource: (Ridge Energy Storage & Grid Services L.P. 2005)

Other projects include a 25 MW R&D facility, named Sesta, which was in

operation in Italy during the early 1990s, but has since been decommissioned. A wind-

CAES facility is in the works in Iowa. It already has some investors for the project

development and is currently seeking funding for capital purchases. Air will be stored in

41

an aquifer instead of the typical cavern (Holst 2005). Based on projected economics, the

plant will have a CAES energy capacity of 200 MW and a wind energy capacity of 100

MW (Research Reports International 2004). The plant is scheduled to open in April 2010

The largest CAES facility ever to be constructed, as well as, the largest energy storage

system in the U.S. is being developed in Norton, OH. It is a 2,700 MW plant consisting

of 9 turbines; the facility will be able to compress air to 104 bar (1,508 psia) in a man-

made limestone mine approximately 670 m (2,200 ft) beneath the surface. The plant has

an underground capacity of 9.5 million m3 (338 million ft3) (Holst 2005). The Norton

CAES facility is currently in the permitting stage of the project. The plan is to build the

facility in phases over a five year period. Upon completion, the facility will be able to

provide 2,700 MW of energy with the emissions comparable to a 600 MW gas-powered

combustion plant (Research Reports International 2004).

Ridge Energy Storage has two planned projects, one in Markham, Texas and the

other entitled Pierce Junction in Houston, Texas. The Markham plant plans on utilizing

four 135 MW turbines (540 MW total power) and four 100 MW units for compression.

Several caverns are available at the site for storage. Permitting has been obtained for

both the caverns and the air regulations (Ridge Energy Storage & Grid Services L.P.

2005). In spite of these advances, in a May 21, 2006 summary updating electricity

generating facilities for the state of Texas, the Markham CAES project was listed as

cancelled. Pierce Junction has designed for two 135 MW turbines (270 MW total power)

and two 100 MW compression units. One cavern is currently permitted and available for

storage. If an additional cavern was added along with the CAES equipment, the total

power output would be either 405 MW or 540 MW. The above summary has no word on

the Pierce Junction facility (State of Texas 2006).

42

2.2 Advantages of CAES over Other Storage Technologies

Although the aforementioned storage techniques can be effective at a large scale,

only pumped hydroelectric storage and CAES are proven to be cost-effective at the large

temporal scales, consisting of several hours to days (Greenblatt et al. 2006). Figure 2.16

shows the electricity generating capacity of the major storage technologies. This figure

demonstrates that pumped hydroelectric storage and CAES are also the only technologies

capable of storing high amounts of energy for future electricity conversion. Figure 2.17

shows the installed costs of major storage technologies.

Figure 2.16 Power ratings of major storage technologiesSource: (Makansi 2001)

43

Figure 2.17 Cost and performance of major storage technologiesSource: (Research Reports International 2004)

Simply looking at installed costs does not provide an accurate representation of

the associated variable costs or the amount of energy that can be produced with a

technology. Therefore, a balanced annual cost for each technology must be examined.

This annual cost takes into account amortized installed cost of the system, the operating

and maintenance cost, fuel cost, and replacement costs. Figure 2.18 shows the annual

cost for the same technologies as in Figure 2.17. Figure 2.18 can then be divided by the

hours of operation per year to provide further detail.

44

Figure 2.18 Levelized Annual Cost of Bulk Storage OptionsSource: (Australian Greenhouse Office 2005)

In Figure 2.17, CAES is shown to be one of the more costly options. However,

when a balanced annual cost is considered CAES provides the largest amount of storage

at the lowest cost. Figure 2.19 displays the capital costs of major energy storage options.

Based on this figure, CAES has one of the lowest capital costs per unit energy and capital

costs per unit power. CAES also has the advantage of providing a large amount of stored

energy for an extended period of time at low cost. Some of the other storage

technologies can provide one or two of these services, but only CAES can supply all

three.

45

Figure 2.19 Capital cost of major storage technologiesSource: (Research Reports International 2004)

The previous figures demonstrate the practicality of CAES; the only real

challenger to CAES is pumped hydroelectric. However, CAES has the upper hand over

pumped hydroelectric for a couple of reasons. Firstly, public and environmental pressure

is making the building of above ground storage facilities quite difficult, to the point that

future facilities may be prevented from being constructed. Even if pumped hydroelectric

is conducted in the subsurface, CAES is considered to be about 20% more efficient and

around 1/3 of the cost of pumped hydroelectric (Makansi 2001).

In addition to being cost effective, CAES can be utilized throughout the U.S. A

1990s DOE study found that approximately 85% of the land in the U.S. would be

accessible and of a suitable geology for CAES development (Ridge Energy Storage &

46

Grid Services L.P. 2005). Figure 2.20 shows the areas of the U.S. that have the potential

for CAES.

Figure 2.20 Regions of the United States suitable for CAESSource: (Ridge Energy Storage & Grid Services L.P. 2005)

This map shows huge potential for the combination of CAES and wind farms.

Throughout the northwestern and midwestern regions, suitable geology exists for CAES.

These locations are also where a majority of class 3 or higher wind sites exist (Figure

1.1).

47

2.3 Fundamentals of Reservoir Simulation

Models have been a part of human life for thousands of years. Simply put, a

model is “used to obtain a better understanding of the environment and to predict the

behavior of physical phenomena under the constraints of nature’s laws” (Thomas 1982, p.

1). When examining reservoirs, one cannot physically view the reservoir; therefore a

model has to be employed to simulate the reservoir’s behavior. In order to solve

reservoir engineering problems within the oil industry, reservoir simulation has become

the method of choice. “Reservoir simulation is the art of combining physics,

mathematics, reservoir engineering, and computer programming to develop a tool for

predicting hydrocarbon reservoir performance under various operating strategies” (Abou-

Kassem, Farouq Ali and Islam 2004, p.1). Reservoir simulators are primarily based off

of the material balance equation (MBE); Schilthuis first introduced the MBE in 1936

(Schilthuis 1936). He envisioned a reservoir as a sealed tank with uniform properties

throughout. Therefore, the net change in volume is simply the subtraction of the volume

of fluids leaving the tank from the volume of fluids entering the tank. For this reason, it

is also called the tank model. This original form of the MBE has some major deficiencies

that limit its use in reservoir simulators. It does not allow for spatial variation of

reservoir parameters, such as rock and fluid properties. The MBE also does not take the

actual reservoir geometry into consideration and fluid movement within the reservoir is

ignored. To correct for these shortcomings, an alternative form (Equation 2.1) was

introduced (Thomas 1982).

Rate of Fluid In – Rate of Fluid Out = Net Change in Fluid Rate (2.1)

With this model, Darcy’s Law (shown for a single-phase flow in a porous media

in Equation 2.2) can be used and the dynamic behavior of fluid movement can be

correctly applied. If the reservoir is split into a collection of small individual blocks, then

48

fluid properties can be defined differently for each block. This method allows for the

deficiencies of the MBE to be overcome and can be seen in 1-D models. The method can

be extended to 2-D and 3-D simulators, where more detailed rock and fluid property

distribution and flow discretization are possible.

(2.2)

where = fluid velocity, = the permeability tensor, = viscosity, and = the

gradient of the potential function = h , where = fluid density and h = the flow

potential (Thomas 1982).

Reservoir simulators typically fall into three groups: gas reservoir simulators,

black oil simulators, and compositional reservoir simulators. The simulator used for this

investigation, ECLIPSE 100©, is a black oil simulator. With black oil simulators, all

three phase (gas, oil, and water) can be represented in all proportions. The effect of gas

going in and out of solution with oil can also be simulated. More specifically, “ECLIPSE

100 is a fully-implicit, three phase, three dimensional, general purpose black oil simulator

with gas condensate options” (Schlumberger 2004 p. 23). ECLIPSE 100 was written

using FORTRAN77 and can operate on any computer with an ANSI-standard

FORTRAN77 compiler and sufficient memory. Since ECLIPSE 100 uses the fully-

implicit method, stability can be achieved over long time periods. Usually, the fully-

implicit method cannot be applied on a large scale, but this limitation is ramified by

making use of the Nested Factorization. ECLIPSE 100 has the option to simulate one,

two, or all three phases. With two phases, the model is ran as a two component system

which saves time and computer storage. When establishing the gridding of the reservoir,

more conventional block-center geometry can be used, or corner-point geometry can be

chosen (Schlumberger 2004). Block-center geometry makes calculations at the center of

49

each gridblock, while corner-point geometry can conduct calculations at gridblock

corners.

An input file for Eclipse is created in a free format, using a program such as

Notepad, with the appropriate keywords. These keywords define everything necessary

for a successful simulation, such as the rock (reservoir dimensions, structure tops, net-to-

gross ratios, porosities, permeabilities) and fluid properties (relative permeability,

saturations, formation volume factors, viscosities, fluid densities). The first step in

creating an Eclipse input file for simulation involves dividing the reservoir into

gridblocks. Each cell within the reservoir will have its own x, y, and z coordinate. Using

the appropriate keywords, the above rock and fluid properties can be distributed

throughout the gridblocks. Once these physical parameters are established, keywords are

used to enter wells and any of their available flow rates and pressures into the model.

Production or injection rates can be entered into the model, based off of bottom hole

pressure requirements or dependant on water saturation limitations (Schlumberger 2004).

The time in between calculations (time steps) are also entered with the well information.

This allows for the user to establish a production schedule, an injection schedule, or a

schedule with periods of injection and production.

The purpose of reservoir simulators is to match modeled data to known data.

Results from a model run are compared to previously recorded field data for the modeled

wells. If the results do not provide a satisfactory match, then physical parameters, such

as permeability, relative permeability, saturations, porosity, etc. are changed until a match

can be obtained (Thomas 1982). For instance, if the pressure of a production and

injection schedule is to be modeled over a certain time period, then all of the necessary

rock and fluid parameters are entered into an input model. After running the model, the

pressure curve generated by the simulator can be compared to some known recorded

pressure values. If the pressures match, then the model is a good representation of the

data. If the pressure match is not satisfactory, then the model parameters need to be

adjusted until a match can be obtained. Once this “history match” has been achieved, the

50

model can be used to predict future performance. It is not certain that the model will

accurately predict reservoir performance, but the more data available for model input, the

better the representation of the actual reservoir. This means that having a long time

period of data available for the history match will lead to a more reliable model for

prediction.

51

CHAPTER 3

MODEL STUDY OF CAVERN STORAGE

As previously mentioned, the Huntorf facility was chosen for modeling CAES in

a cavern setting. The plant was chosen for modeling because of its use of infinite

permeability and porosity excavated salt caverns and because of its longstanding

operation. Huntorf’s cavern geology provides an optimal base case for comparison with

varying cavern geologies. This chapter will begin with a detailed description of the

Huntorf facility, followed by the inputs that were needed to construct the model and how

these parameters were justified. The dataset for the ECLIPSE 100© modeling is located

in Appendix A and is referred to as Cavern CAES. This dataset is the base case model

for the study; other sensitivities were explored to obtain the desired history match and to

monitor system response. A discussion of the results from the base model, as well as the

results from various sensitivities will be discussed towards the end of the chapter.

3.1 Description of Huntorf Facility

To begin looking at the Huntorf plant, the specifics for the facility are presented

in Table 3.1.

52

Table 3.1 Specifications of the Huntorf CAES facilitySource: Adapted from (Crotogino et al. 2001)

  Metric Units English Unitsoutput    turbine operation 290 MW (≤ 3hrs)compressor operation 60 MW (≤ 12 hrs)  max air mass flow rates    turbine operation 417 kg/s 919.4 lb/scompressor operation 108 kg/s 238 lb/sair mass flow ratio in/out 1/4 1/4Max air volumetric flow rates    turbine operation 29.411*106 m3/day 1.039*107 Mscf/daycompressor operation 7.617*106 m3/day 1.345*105 Mscf/daynumber of air caverns 2 2single air cavern volumes 140,000 m3 4,944,053 ft3

  170,000 m3 6,003,493 ft3

total cavern volume 310,000 m3 10,947,547 ft3

location of caverns - top 650 m 2,133 ft - bottom 800 m 2,625 ftmaximum diameter 60 m 197 ftwell spacing 220 m 722 ftcavern pressures    minimum permissible 1 bar 14.5 psiaminimum operational (exceptional) 20 bar 290 psiaminimum operational (regular) 43 bar 624 psiamaximum permissible & operational 70 bar 1,015.3 psiamaximum pressure reduction rate 15 bar/h 218 psia/h

The two caverns used in the plant design are underground salt caverns that have

been excavated for storage. The total volume could have been obtained with just one

cavern, but two were decided upon for the following reasons. Redundancy was desired

during maintenance or cavern shut-down, two caverns allow for easier cavern refilling

when it is necessary to reduce the pressure in one cavern down to atmospheric, and the

start up procedure for the plant requires that a minimum of 13 bar (189 psia) be obtained

in at least one of the caverns. The depth of the caverns was chosen to allow stable

53

storage for several months and to ensure the specified maximum pressure of 100 bar

(1,450 psia).

An important design in the Huntorf facility was the construction of the cavern

wells. The wells needed to be able to withstand high withdrawal rates of 417 kg/s (919

lb/s), as well as, low pressure losses. These parameters led to the selection a 20”/21”

production string and a 24 ½” casing string. Since a packer was not included in the

design to hold the production string inside the casing, corrosion could occur from moist

air traveling through the annulus. This was deterred by the injection of dry air into the

annulus. Brine also had to be evacuated with a submersible pump because of an

insufficient maximum pressure and an excessive flow rate in the compressor and

unacceptable air velocities in the production string. In order to minimize costs, the

production string was initially composed of structural steel and hung 80 m (263 ft) in the

cavern without support. This was done in order to prevent the entry of salt dust into the

turbines. However, after a few months of operation, serious corrosion problems occurred

with the materialization of a large amount of rust in the filter upstream of the gas turbine.

After much consideration, a fiberglass reinforced plastic (FRP) steel string was selected

as a replacement. The FRP string experienced 20 years of problem free operation, but is

now experiencing corrosion problems itself. Investigations for pipe replacements are

being conducted. Unlike the production string, the casing cannot be replaced. Therefore

extra care was taken when designed this portion of the plant. Cleaning of the string has

been carried out, but no surface corrosion or pitting has been observed (Crotogino et al.

2001).

The CAES cycle of the Huntorf plant is the same as in Figure 2.12, except that the

Huntorf facility does not utilize a recuperator leading to a poorer heat rate (Crotogino

2006). A photograph of its equipment is detailed in Figure 3.1. Overall, the operation of

the Huntorf facility has been very successful. The plant has accumulated over 7,000

starts in its 28 years of operation. It has shown 90% availability and a 99% starting

reliability (EA Technology 2004).

54

Figure 3.1 Components of the Huntorf facilitySource: (Gonzalez, O Gallachoir and McKeogh 2004)

3.2 Cavern CAES Inputs

For the modeling of Huntorf, the parameters contained in Table 3.1 were

represented as closely as possible in the model. Some values required adjusting, but the

model is a realistic representation of the facility. The goal of the Cavern CAES model is

to match pressure for a daily operation schedule of the Huntorf facility. Pressures were

obtained for the match by reading hourly values from the pressure response to a daily

power production curve based on energy demand, shown in Figure 3.2.

55

1 bar = 14.5 psiaFigure 3.2 Daily Power Production and Associated Pressure Response

Source: (Crotogino et al. 2001)

This was the only pressure response that could be obtained for Huntorf. A

personal communication with F. Crotogino, who has been involved with Huntorf since its

opening and is the author of the paper used for data collection (Crotogino et al. 2001),

determined that additional pressure data has not been collected because the plant is

operating problem free. Reading pressures of a graph leads to some error associated with

the pressure values. Various sensitivities were examined to show model reliability and

obtain the best pressure match. To begin with a base case model was used that

maintained the original Huntorf parameters as closely as possible. Table 3.2 shows a

comparison of the actual Huntorf parameters to those used in the Cavern CAES base

model.

56

Table 3.2 Comparison of actual Huntorf data to Cavern CAES base model dataSource: Adapted from (Crotogino et al. 2001)

  Huntorf Parameters Model Parameters  Metric Units English Units Metric Units English Unitsoutput        turbine operation 290 MW (≤ 3hrs)   290 MW (≤ 3hrs)  compressor operation 60 MW (≤ 12 hrs)   60 MW (≤ 12 hrs)  max air mass flow rates        turbine operation 417 kg/s 919.4 lb/s 417 kg/s 919.4 lb/scompressor operation 108 kg/s 238 lb/s 108 kg/s 238 lb/sair mass flow ratio in/out 1/4 1/4 1/4 1/4Max air volumetric flow rates        turbine operation 29.411*106 m3/day 1.039*107 Mscf/day 29.411*106 m3/day 1.039*107 Mscf/daycompressor operation 7.617*106 m3/day 1.345*105 Mscf/day 7.617*106 m3/day 1.345*105 Mscf/daynumber of air caverns 2 2 2 2single air cavern volumes 140,000 m3 4,944,053 ft3 150,000 m3 5,116,800 ft3

  170,000 m3 6,003,493 ft3 150,000 m3 5,116,800 ft3

total cavern volume 310,000 m3 10,947,547 ft3 300,000 m3 10,233,600 ft3

location of caverns - top 650 m 2,133 ft 650 m 2,133 ft - bottom 800 m 2,625 ft 800 m 2,625 ftmaximum diameter 60 m 197 ft 60 m 197 ftwell spacing 220 m 722 ft 220 m 722 ftcavern pressures        minimum permissible 1 bar 14.5 psia 1 bar 14.5 psiaminimum operational (exceptional) 20 bar 290 psia 20 bar 290 psiaminimum operational (regular) 43 bar 624 psia 43 bar 624 psiamaximum permissible & operational 70 bar 1,015.3 psia 70 bar 1,015.3 psiamaximum pressure reduction rate 15 bar/h 218 psia/h 15 bar/h 218 psia/h

The actual cavern shapes (Figure 3.3) could not be duplicated because of the lack

of necessary information and model practicality. Therefore the dimensions of the Cavern

CAES base model are based on the layouts shown in Figure 3.4 and Figure 3.5. Each

cavern is represented as a cuboid, according to the dimensions established in Crotogino et

al. (2001). The two caverns were split into equal volumes for the scope of this study.

Since the maximum diameter of the caverns is 60 m (197 ft), this value had to be

57

converted into x and y coordinates for model input. It was decided that each cavern

would have an x dimension of 40 m (130 ft) based on the given dimension data. The

provided thickness of 150 m (492 ft) was used for the z dimension. The y dimension was

calculated as 25 m (80 ft), using the total model cavern volume of 300,000 m3

(10,233,600 ft3).

Figure 3.3 Actual dimensions of the Huntorf salt cavernsSource: (Crotogino et al. 2001)

58

Figure 3.4 Cross-sectional view of the Huntorf Facility

Figure 3.5 Plan view of the Huntorf Facility

59

These figures were also the basis for the gridding used in the model. The 25 m

(80 ft) y dimensions are split into 5 gridblocks, 5 m (16 ft) in height. The total distance

in the x direction is 300 m (980 ft); this includes both caverns and the 220 m (720 ft)

spacing between them. The model uses a total of 20 gridblocks in the x direction; for the

areas where the caverns are located, the gridding consists of 5 gridblocks per cavern, 8 m

(26 ft) each. The area between the caverns consists of 10 gridblocks, all 22 m (72 ft)

long. For the cavern thickness, 15 layers exist, all 10 m (33 ft) thick. This gridding

scheme was selected in order to obtain the desired level of information without

unnecessary processing. Emphasis was placed on the actual cavern locations and not on

the area between the caverns.

The top of the caverns is 650 m (2,133 ft) and is constant along each cavern and

in the spacing between the caverns. The actual porosity of the caverns is 100%, but

because of the limitations of ECLIPSE 100©, a porosity of 99% had to be used. The

porosity between the caverns is 0% since this area does not impact the pressure response

of CAES. Likewise, the net to gross ratio of the model is one where the caverns are

located and zero in the space between them. The actual permeability for the excavated

caverns is infinite, but once again due the limitations of ECLIPSE 100© some sort of

number is required. For the Cavern CAES base model, a permeability of 10,000 md is

assumed in all directions; a very high permeability was selected to assure that this

parameter did not have an effect on the model response.

Relative permeability of the model is the simplest form of relative permeability

curves since the brine has been removed from the caverns. Although some small amount

of residual brine does exist in the caverns, the vast majority of the structures are filled

with air. Therefore, no residual water or gas saturation exists and relative permeability

for water or gas is the same as the corresponding saturation (Figure 3.6).

60

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Water Saturation (fraction)

Rel

ativ

e Pe

rmea

bilit

y (fr

actio

n)

krw

krg

Figure 3.6 Relative permeability curves for water and air used in ECLIPSE 100© input

An approximation was used for the PVT properties for the water phase since an

analysis of any produced water from the field was unavailable. The water formation

volume factor was calculated according to,

(3.1)

where Bw = water formation volume factor, = change in volume during the pressure

reduction, and = change in volume due to the reduction in temperature (McCain Jr.

1990). and are both found using Figures 16-6 and Figures 16-7, respectively

of McCain Jr. (1990). Assuming a cavern temperature of around 35 ºC (95 ºF) and an

average cavern pressure of 69 bar (1,000 psia), a of 0.008 and a of -0.0005

61

were determined. Entering these values into Equation 3.1, yielded a Bw of 1.0075

reservoir bbl/stb. The coefficient of isothermal compressibility was also needed for

model input. An average compressibility of 3.1*10-6 psi-1 was determined from Figure

16-12 of McCain Jr. (1990), using the same cavern temperature and pressure. The cavern

temperatures and pressures were determined from the initial values along the cavern line

in Figure 3.7. The initial values were used since these profiles represent the response

when emptying the caverns.

Figure 3.7 Pressures, temperatures, and air flow when emptying the caverns

62

The PVT properties for air required more intensive calculations. In order to

calculate the air formation volume factor (Bg) necessary for model input, the following

equation was used:

(3.2)

where VR = cubic foot of reservoir volume, Vsc = standard cubic foot of gas, z = gas-

deviation factor, T = temperature in Rankine, p = pressure in psia and 5.03676 converts

Bg from rcuft/scf to Rbbl/Mscf; this is the unit that is necessary in ECLIPSE 100© when

running models in “field” units. Before this equation could be used, the gas-deviation

factor had to be calculated. Calculations of the gas deviation factor require the use of

critical pressure and temperature. Since the gas is air, no pseudocritical calculations were

necessary. According to Table 5.2 in Towler (2006), the critical pressure and

temperature for air are 546.9 psia and -221.4 ºF, respectively. Based on pressures

ranging from 14.7 to 4,014.7 psia, reduced pressures and temperatures were calculated

with Equations 3.3 and 3.4:

(3.3)

(3.4)

where pc = critical pressure and Tc = critical temperature.

With these values known, the Dranchuk and Abou-Kassem equation of state

(Towler 2006) could be employed to find the gas-deviation factor (z-factor). This

relationship has an average absolute error of 0.486% and a standard deviation of 0.00747

63

when the reduced temperatures and pressures are the following: 0.2 < < 30, 1.0 < <

3.0 and < 1.0, 0.7 < < 1.0. The Dranchuk and Abou-Kassem equation of state is

(3.5)

where and the constants A1 through A11 are the following: A1 =

0.3265; A2 = -1.0700; A3 = -0.5339; A4 = 0.01569; A5 = -0.05165; A6 = 0.5475; A7 = -

0.7361; A8 = 0.1844; A9 = 0.1056; A10 = 0.6134; and A11 = 0.7210 (Towler 2006). This is

an iterative equation that was solved with the various reduced temperatures and pressures

for the pressure range given above. This hydrocarbon relationship was validated for use

with air by examining values typically seen with air. Excel was used to calculate the gas-

deviation factors and the results are shown in Table 3.3.

The and values shown in Table 3.3 are within the ranges specified for better

accuracy when applying the Dranchuk and Abou-Kassem equation of state. Therefore,

this equation provides more accurate gas-deviation factors.

64

Table 3.3 Gas-deviation factor values for various pressures

Pressure Temp Tr Pr ρr Z factorpsia Rankine  14.7 520 2.1824 0.0269 0.0033 0.9993

264.7 520 2.1824 0.4840 0.0606 0.9881514.7 540 2.2663 0.9411 0.1142 0.9822764.7 550 2.3083 1.3982 0.1671 0.9786

1,014.7 560 2.3503 1.8554 0.2180 0.97791,264.7 570 2.3922 2.3125 0.2664 0.97991,514.7 580 2.4342 2.7696 0.3121 0.98421,764.7 590 2.4762 3.2267 0.3552 0.99052,014.7 600 2.5182 3.6839 0.3956 0.99852,264.7 610 2.5601 4.1410 0.4333 1.00792,514.7 620 2.6021 4.5981 0.4685 1.01832,764.7 630 2.6441 5.0552 0.5014 1.02963,014.7 640 2.6860 5.5123 0.5320 1.04163,264.7 650 2.7280 5.9695 0.5605 1.05413,514.7 660 2.7700 6.4266 0.5871 1.06703,764.7 670 2.8119 6.8837 0.6119 1.08014,014.7 680 2.8539 7.3408 0.6352 1.0934

In order to obtain the temperature profile, values were read from Figure 3.7 that

corresponded with the appropriate pressure values. This gives an approximation of the

temperature response with a change in pressure; Figure 3.7 was the only temperature

profile available in the supplied data (Crotogino 2006). With the gas-deviation factor

calculated, Equation 3.2 was used to find Bg at the same range of pressures and

corresponding temperatures (Table 3.4).

Table 3.4 Gas Formation Volume Factors for various pressures

65

Pressure Temp Z factor Bgpsia Rankine Rbbl/Mscf14.7 520 0.9993 178.0439

264.7 520 0.9881 9.7766514.7 540 0.9822 5.1901764.7 550 0.9786 3.5449

1,014.7 560 0.9779 2.71831,264.7 570 0.9799 2.22451,514.7 580 0.9842 1.89821,764.7 590 0.9905 1.66802,014.7 600 0.9985 1.49782,264.7 610 1.0079 1.36732,514.7 620 1.0183 1.26452,764.7 630 1.0296 1.18173,014.7 640 1.0416 1.11383,264.7 650 1.0541 1.05713,514.7 660 1.0670 1.00923,764.7 670 1.0801 0.96824,014.7 680 1.0934 0.9328

Another necessary PVT parameter is the viscosity as a function of pressure. In

order to calculate viscosities, the Lee et al. analytical method (Towler 2006) was used

within an Excel spreadsheet. This method requires pressure, temperature, z factor, and

molecular weight (the molecular weight of air is 28.964 lb-mole). The Lee et al.

equations are designed for use with specified units; these units are listed with the

equations below:

(3.6)

66

where , ,

, and and where µg = gas viscosity, cp; ρ = gas

density, g/cm3; p = pressure, psia; T = temperature, ºR; and Mg = gas molecular weight.

The Lee et al. analytical method has an average standard deviation of 2.7%, with a

maximum standard deviation of 9%. The distribution of variables used in developing the

method is as follows: 100 psia < 8,000 psia, 100 < T(F) <340, 0.90 < CO2 (mol%) < 3.20

and 0.0 < N2 (mol%) < 4.80. Table 3.5 displays the results from the Lee et al. equations.

Table 3.5 Lee et al. viscosity calculations

Pressure Temp Z factor ρ K1 X Y µpsia Rankine g/cc cp14.7 520 0.9993 0.0012 0.0092 5.6858 1.2628 0.0093264.7 520 0.9881 0.0223 0.0092 5.6858 1.2628 0.0097514.7 540 0.9822 0.0420 0.0096 5.6156 1.2769 0.0106764.7 550 0.9786 0.0615 0.0098 5.5824 1.2835 0.0115

1,014.7 560 0.9779 0.0802 0.0100 5.5504 1.2899 0.01241,264.7 570 0.9799 0.0980 0.0102 5.5195 1.2961 0.01341,514.7 580 0.9842 0.1148 0.0104 5.4896 1.3021 0.01441,764.7 590 0.9905 0.1307 0.0106 5.4608 1.3078 0.01552,014.7 600 0.9985 0.1455 0.0108 5.4330 1.3134 0.01662,264.7 610 1.0079 0.1594 0.0110 5.4060 1.3188 0.01772,514.7 620 1.0183 0.1724 0.0112 5.3800 1.3240 0.01892,764.7 630 1.0296 0.1844 0.0114 5.3547 1.3291 0.02003,014.7 640 1.0416 0.1957 0.0115 5.3303 1.3339 0.02113,264.7 650 1.0541 0.2062 0.0117 5.3066 1.3387 0.02233,514.7 660 1.0670 0.2160 0.0119 5.2836 1.3433 0.02343,764.7 670 1.0801 0.2251 0.0121 5.2613 1.3477 0.02454,014.7 680 1.0934 0.2337 0.0123 5.2396 1.3521 0.0256The viscosity values complete the inputs for the air PVT section in ECLIPSE

100. The rock compressibility of the base model is 3*10-6 psia-1 and average densities of

water and air are 1,000 kg/m3 (62.4 lb/ft3) and 1.297 kg/m3 (0.081 lb/ft3), respectively.

67

Measurements of salt contamination were conducted at the Huntorf facility for two

withdrawl cycles of 365 kg/s (805 lb/s). The results of each test showed salt content to be

less than 1 mg (salt) / kg (air). Therefore, fresh water density values described above are

used for the model input. Table 3.6 shows a diurnal schedule for the compression and

expansion of air.

Table 3.6 Daily schedule for the Huntorf facility

Flow Type Time (hrs)nothing 0nothing 1injection 2injection 3injection 4nothing 5nothing 6injection 7injection 8injection 9injection 10nothing 11

production 12production 13

nothing 14nothing 14.5injection 15.5injection 16.5injection 17

production 18production 19production 20injection 21injection 22nothing 23nothing 24

Table 3.6 was created by looking at the compression and expansion schedule given in

Figure 3.2. The Cavern CAES base model makes use of four wells, consisting of a

production and injection well for each cavern; the production wells are labeled as P-1 and

P-2 and the injection wells are I-1 and I-2 for caverns 1 and 2.

68

The production and injection rates for each well could not be entered into

ECLIPSE 100© as mass flow rates; therefore an air density had to be selected to convert

the mass flow rates into volumetric flow rates. A temperature of 15 ºC (59 ºF) and an

atmospheric pressure were chosen as the near surface conditions, yielding an air density

of 1.225 kg/m3 (0.0765 lbm/ft3). This density differs from the previously stated density

because the density of 1.225 kg/m3 (0.0765 lbm/ft3) represents near surface conditions

and the previous density of 1.297 kg/m3 (0.081 lbm/ft3) is an average density. Using the

density and mass flow rates of 417 kg/s (919.4 lb/s) and 108 kg/s (238 lb/s) for

production and injection, respectively, volumetric rates were obtained. For input into

ECLIPSE 100©, the rates were converted into units of Mscf/day; these rates were then

divided by two to compensate for an injection and production well per cavern. The total

production rate for the system is 1,038,649 Mscf/day and 519,324.5 Mscf/day for each

cavern production well. The total injection rate is 269,003 Mscf/day and 134,501.5

Mscf/day for each cavern injection well. The model is comprised mostly of one hour

time steps in which the system is injecting, producing, or remaining static. This set up

allows for a direct comparison to actual Huntorf data.

Table 3.7 Conversion factors for oilfield units to metric units

Oilfield Unit Conversion Factor Metric Unitbbl x 1.589873 E-01 m3

ft x 3.048 mgal x 3.785413 m3

lbm x 4.535924 E-01 kgpsia x 1.751268 E+02 Pa

deg F x (deg. F-32)/1.8 deg. CMscf/day x 2.832 E+02 m3/day

Since ECLIPSE 100© was set up to perform model runs in oilfield units, the

remainder of this chapter will only show oilfield units. Table 3.7 shows the conversion

factors for oilfield units to metric units for the applicable parameters.

69

3.3 Cavern CAES Sensitivities and Results

With all of the necessary parameters entered into an ECLIPSE 100© dataset

format, the base case model was run. The results from the base model were compared

with the actual Huntorf pressure data. A simple change to permeability in the model also

allowed for the first model sensitivity of different permeabilities. The initial pressure

match is displayed below in Figure 3.8.

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30

Time (hrs)

Pres

sure

(psi

a)

Actual Pressure

Modeled Pressure w/ 1,000 md perm

Modeled Pressure w/ 10,000 md perm

Modeled Pressure w/ 20,000 md perm

Figure 3.8 Comparison of actual and modeled data for initial model runsFigure 3.8 shows that during the initial stages of injection and remaining static, all

of the model runs are close to the actual values. However, once production begins during

the 12th hour, all of the values begin to deviate from the actual pressures. The same

general shape of the models mimics the actual pressure curve, but with the onset of

production, the modeled pressures fall below the actual values. This can especially be

70

seen in the base model with the 10,000 md permeability and in the 20,000 md

permeability model. The 1,000 md permeability model has a little better pressure

response; however, a 1,000 md permeability is not an accurate representation of cavern

geology. The pressure curve of the 1,000 md permeability model does show that

permeability has an effect on the model with low values. With the higher permeability

models, the pressure curves are almost exactly the same. This is encouraging because it

demonstrates that once the permeability gets to a large enough value, it does not affect

model performance, as should be the case with a cavern environment. However, since

this is a cavern, the 1,000 md permeability should provide similar results, which is not the

case in Figure 3.8.

In addition to the pressure match, the specified rates of 1,038,649 Mscf/day

(519,324.5 Mscf/day per cavern) for production and 269,003 Mscf/day (134,501.5

Mscf/day per cavern) for injection needed to be replicated in the appropriate time step

according to the schedule of Table 3.6. All of the model runs with the different

permeabilities achieved the same injection rate profile as desired; however, none of the

runs could simulate the specified production rates. In order to achieve these rates and

hopefully get a more accurate pressure response, model runs with different pore volumes

were conducted. Using a permeability of 10,000 md, pore volume was adjusted to 50-

90%, 110%, 150 %, and 200% of the original pore volume values of 300,000 m3

(1,0233,600 ft3). These cavern bulk volumes explained in Section 3.2 are also the pore

volumes because of a 100% porosity in a cavern environment. Figure 3.9 shows the

pressure response for varying pore volumes.

71

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30

Time (hrs)

Pres

sure

(psi

a)Original Pore VolumeActual Values90% Pore Volume80% Pore Volume110% Pore Volume150% Pore Volume200% Pore Volume

Figure 3.9 Pressure Match with Varying Pore Volumes and 10,000 md Permeability

Figure 3.9 displays promising results for the lower pore volumes at early time

periods, but these lower pore volumes have the worst match at later time periods. The

opposite trend is true for the larger pore volumes; at early time periods the match is poor,

but at late time periods the match becomes closer. The 50% and 60% values are not

included because the later trends of the prior decreasing pore volumes demonstrate the

undesired response. A change in pore volume created better matches, but these occurred

with different pore volume percentages based on the selected time period. The injection

rate with different pore volumes was as desired (134,501.5 for both wells P-1 and P-2) as

shown in Figure 3.10. Figure 3.11 and Figure 3.12 display the hard to achieve production

rates for wells P-1 and P-2, respectively.

72

0

20000

40000

60000

80000

100000

120000

140000

160000

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 14.5 15.5 16.5 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0

Time (hrs)

Msc

f/d

Figure 3.10 The injection rate for actual data and modeled data for different pore volumes

0

100000

200000

300000

400000

500000

600000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/d

Actual RateOriginal Pore Volume90% Pore Volume80% Pore Volume70% Pore Volume60% Pore Volume50% Pore Volume

Figure 3.11 The production rate for decreased pore volumes for well P-1

73

0

100000

200000

300000

400000

500000

600000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/dActual RatesOriginal Pore Volumes90% Pore Volume80% Pore Volume70% Pore Volume60% Pore Volume50% Pore Volume

Figure 3.12 The production rate for decreased pore volumes for well P-2

Only decreased pore volumes were used to achieve the production rates because

an increase in pore volume leads to a lower pressure and thus a lower production rate.

The results from this figure are inconclusive. With well P-1, a 50% pore volume could

initially achieve 519,324.5 Mscf/day, but was then one of the worst performers. A 90%

pore volume started poorly, but was better towards the end of the schedule. Based on the

results from the pressure match and the production rates, a change in pore volume is not

an appropriate method for achieving the desired pressures and rates.

The next sensitivity to be modeled required a personal communication with F.

Crotogino. The ability to continuously achieve such high production rights seemed

questionable. F. Crotogino confirmed that these rates are maximum achievable rates and

typically the plant did not operate with such high production rates. For this reason, the

next sensitivity to be modeled was a dataset with lower production rates. Different

production rates were modeled until the best pressure match was found with a

74

corresponding ability to achieve the specified rate during the given time period. The

rates that met the above criteria were 500,000 Mscf/d (250,000 Mscf/d per production

well) for the initial production period during the 12th and 13th hours and 600,000 Mscf/d

(300,000 Mscf/d per production well) for the final production period during the 18th – 20th

hours. These production rates were achieved in model runs with 1000, 10,000, and

20,000 md permeability. The pressure match with the lower production rates and the

above permeabilities is shown in Figure 3.13.

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30

Time (hrs)

Pres

sure

(psi

a)

Actual Pressure

Modeled Pressure w/ 1,000 md perm

Modeled Pressure w/ 10,000 md perm

Modeled Pressure w/ 20,000 md perm

Figure 3.13 The pressure match obtained with lower production rates and the original pore volumes

The pressure match obtained with lower production rates is quite an improvement

over the other scenarios and, more importantly, the inputted production rates could be

achieved during modeling. To verify the quality of the pressure match, Figure 3.14 and

75

Figure 3.15 provide a change in pressure (actual – modeled) relationship and a percent

difference ((actual – modeled) / actual) response. Since the pressure response between

the different permeabilities is minimal and 10,000 md permeability was selected for the

cavern permeability, only the 10,000 md permeability is examined in Figure 3.14 and

Figure 3.15.

-5

0

5

10

15

20

25

30

35

0 5 10 15 20 25

Time (hrs)

Pres

sure

(psi

a)

Figure 3.14 Change in pressure between modeled and actual values for lower production rates and 10,000 md permeability

76

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

0 5 10 15 20 25

Time (hrs)

Perc

ent D

iffer

ence

Figure 3.15 Percent difference of actual and modeled pressure values for lower production rates and 10,000 md permeability

Overall, the change in pressure and percent difference are quite small; the only

major discrepancy occurs during the second injection period between the 7th and 9th hours

of the schedule. The modeled pressure follows the same trend, but cannot obtain as high

as values as the actual pressures. For this reason, a final sensitivity with changing

injection and productions rates was examined. Different injection and production rates

were used until the best pressure match could be obtained while still injecting and

producing the specified rates. After a number of model runs, the rates in Table 3.8 were

selected.

77

Table 3.8 Schedule of injection and production rates with the best pressure match

Single Cavern Flow Rate

Total Flow Rate

Flow Type

Mscf/d Mscf/dnothing 0 0nothing 0 0injection 160,000 320,000injection 160,000 320,000injection 160,000 320,000nothing 0 0nothing 0 0injection 160,000 320,000injection 160,000 320,000injection 160,000 320,000injection 160,000 320,000nothing 0 0

production 350,000 700,000production 350,000 700,000

nothing 0 0nothing 0 0injection 160,000 320,000injection 160,000 320,000injection 160,000 320,000

production 300,000 600,000production 300,000 600,000production 300,000 600,000injection 134,501.5 269,003injection 134,501.5 269,003nothing 0 0nothing 0 0

By increasing the rate for each cavern injection well from 134,501.5 Mscf/d to

160,000 Mscf/d for all injection periods except the final period, the production rate also

had to be increased from the values established in the previous sensitivity. Using these

new rates, an ECLIPSE 100© model run was conducted; the pressure match with this

sensitivity is shown in Figure 3.16.

78

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30

Time (hrs)

Pres

sure

(psi

a)Actual Pressure

Modeled Pressure w/ 1,000 md perm

Modeled Pressure w/ 10,000 md perm

Modeled Pressure w/ 20,000 md perm

Figure 3.16 Pressure match with changing production and injection rates and different permeabilities

With an increase in injection rates and a decrease in production rates, a better pressure

match could be realized. Figure 3.17 and Figure 3.18 further exemplify this point.

Figure 3.17 shows a change in actual and modeled pressures and Figure 3.18 displays the

percent difference between actual and modeled pressures; both graphs depict 10,000 md

permeability. The spike in change in pressure and percent difference that is seen around

the 10th hour has been eradicated by the increase in injection rates; the change also

produced lower overall values for these parameters.

79

-10

-5

0

5

10

15

20

0 5 10 15 20 25

Time (hrs)

Pres

sure

(psi

a)

Figure 3.17 Change in actual and modeled pressure for changing injection and production rates with 10,000 md permeability

-1

-0.5

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25

Time (hrs)

Perc

ent D

iffer

ence

Figure 3.18 Percent difference in actual and modeled pressure for changing injection and production rates with 10,000 md permeability

80

3.4 Objective Functions

In order to compare the pressure match obtained with the different sensitivities

several objective functions were calculated. An objective function is “a function

associated with an optimization problem which determines how good a solution is”

(Black 2004). The lower the objective function, the better the solution. Objective

functions (OF) 1 – 6 are displayed below in Equations 3.7 – 3.12.

(3.7)

(3.8)

(3.9)

(3.10)

(3.11)

(3.12)

These objective functions were calculated for the Cavern CAES base model and all of the

sensitivities described in Section 3.3. Following these calculations, OF5 and OF6 were

chosen for comparative purposes because of their integration of change in pressure and

the standard deviation of the actual pressures. By including both of these parameters, a

more informative objective function is created. Table 3.9 shows OF5 and OF6 for all of

the scenarios.

Table 3.9 Objective functions for Cavern CAES base model and the described sensitivities

81

Case Description OF5 OF6

Base Original Pore Volumes, Original Rates, and 10,000 md permeability 96.9727 9.8475Sensitivity

A Original Pore Volumes, Original Rates, and different permeabilities    A.1 1,000 md permeability 37.5114 6.1247A.2 20,000 md permeability 96.9777 9.8477

Sensitivity B Alternate Pore Volumes, Original Rates, and 10,000 md permeability    

B.1 90% of Original Pore Volume 110.5139 10.5126B.2 80% of Original Pore Volume 130.6064 11.4283B.3 70% of Original Pore Volume 161.5500 12.7102B.4 110% of Original Pore Volume 87.5634 9.3575B.5 120% of Original Pore Volume 80.8715 8.9929B.6 150% of Original Pore Volume 69.8266 8.3562B.7 200% of Original Pore Volume 63.8099 7.9881

Sensitivity C

Original Pore Volumes, Different Production Rates, and 10,000 md permeability 2.4219 1.5562

Sensitivity D

Original Pore Volumes, Different Production Rates, and 1,000 md permeability 2.4068 1.5514

Sensitivity E

Original Pore Volumes, Different Production Rates, and 20,000 md permeability 2.4220 1.5563

Sensitivity F

Original Pore Volumes, Different Injection and Production Rates, and 10,000 md permeability 0.4053 0.6366

Sensitivity G

Original Pore Volumes, Different Injection and Production Rates, and 1,000 md permeability 0.3996 0.6321

Sensitivity H

Original Pore Volumes, Different Injection and Production Rates, and 20,000 md permeability 0.4055 0.6368

The base case and sensitivities A and B all have the largest objective functions,

thus verifying the previously stated results. These objective functions prove that a

change in pore volume does not improve the quality of the pressure match. The larger

pore volumes do decrease the objective functions. However, Figure 3.9 shows that

during later time periods the match with these larger volumes becomes poorer and poorer.

The objective functions for the sensitivities with the original pore volumes and a change

in rate confirm a more direct correspondence. Sensitivities C, D, and E all have OF5

values around 2.4 and OF6 values around 1.55. This similarity is desired because it

shows that pressure is not a function of permeability in a cavern setting. With a change

82

in injection and production rates, even smaller objective functions are realized.

Sensitivities F, G, and H have OF5 values around 0.40 and OF6 values around 0.63. The

indirect relationship between pressure responses with a change in permeability can also

be seen with these sensitivities.

3.5 Discussion of Cavern CAES Models

Based solely on the objective functions and the desire to use a permeability of

10,000 md, Sensitivity F would be the obvious choice. However, a personal

communication with Crotogino acknowledged that there is no evidence to justify the

increase in the injection rate. Crotogino stated that the rates provided in Crotogino et al.

(2001) are maximum obtainable rates. Although this sensitivity provides the best

pressure match, an increase in injection rates cannot be justified. Crotogino did confirm

that a decrease in production rates is reasonable because the turbine can not consistently

operate at its maximum rate. In fact, the maximum rate of 1,038,649 Mscf/day can rarely

be obtained in Huntorf’s daily operation. For this reason, Sensitivity C is the best

representation of the diurnal schedule and pressure response for the Huntorf facility.

With OF5 and OF6 values of 2.4219 and 1.5562, respectively, Sensitivity C is still a very

reasonable choice.

The quality of the acquired pressure match and the ability to inject and produce

the desired rates validates the use of ECLIPSE 100© in the modeling of CAES. A widely

used reservoir simulator in the oil industry can also be applied in a storage sense. This

realization could help diminish the gap between traditional energy sources and a

sustainable energy option for the future.

83

CHAPTER 4

VERIFICATION OF MODEL USE FOR RESERVOIR STORAGE

In order to explore the possibility of using CAES in porous media, an ECLIPSE

100© dataset was constructed depicting a shallow reservoir. This reservoir could be a

depleted hydrocarbon reservoir or an excavated aquifer. The composition of the reservoir

is a sandstone with 20% porosity or higher. Sandstone was chosen because of its

availability and the high porosities and permeabilities that can be seen with this structure.

This chapter will detail the creation of an EZGEN grid for input into ECLIPSE 100©, the

ECLIPSE 100© dataset itself, and the results from numerous runs of the model. The

reservoir model uses a 20% porosity and includes runs with varying permeabilities. Just

as with the Huntorf model, there is no residual water saturation in the reservoir. Results

form the varying permeability models are compared and discussed.

4.1 EZGEN grid input and Model Setup

The reservoir ECLIPSE 100© model uses the same rates as Sensitivity C in the

cavern CAES model, as well as, two production wells, P-1 and P-2, and two injection

wells, I-1 and I-2, just as in the Cavern CAES model. A reservoir model with the same

injection and production rates as Sensitivity C and the same number of wells provides a

sound comparison for the use of CAES in a cavern setting versus a reservoir setting. By

keeping the rates and wells unchanged, it can be determined if the same CAES process

84

that is successful in Huntorf could be effectively transferred to a porous medium. Table

4.1 shows the rates for each well that was used in Sensitivity C and will be used for the

reservoir model.

Table 4.1 Injection and production rates for each well used in Sensitivity C and for Reservoir CAES

Injection Rates Production Rates Time I1 Rate I2 Rate P1 Rate P2 Ratehrs Mscf/d Mscf/d Mscf/d Mscf/d0.0 0 0 0 01.0 0 0 0 02.0 134,501.5 134,501.5 0 03.0 134,501.5 134,501.5 0 04.0 134,501.5 134,501.5 0 05.0 0 0 0 06.0 0 0 0 07.0 134,501.5 134,501.5 0 08.0 134,501.5 134,501.5 0 09.0 134,501.5 134,501.5 0 0

10.0 134,501.5 134,501.5 0 011.0 0 0 0 012.0 0 0 250,000 250,00013.0 0 0 250,000 250,00014.0 0 0 0 014.5 0 0 0 015.5 134,501.5 134,501.5 0 016.5 134,501.5 134,501.5 0 017.0 134,501.5 134,501.5 0 018.0 0 0 300,000 300,00019.0 0 0 300,000 300,00020.0 0 0 300,000 300,00021.0 134,501.5 134,501.5 0 022.0 134,501.5 134,501.5 0 023.0 0 0 0 024.0 0 0 0 0

Before building any of the reservoir models in ECLIPSE 100©, a typical reservoir

structure had to be devised. EZGEN was used to construct a dipping anticline, ideal for

hydrocarbon reservoirs, and to avoid the use of a layer-cake model. EZGEN develops

85

grids, distributes reservoir parameters throughout the grid blocks, calculates

petrophysical parameters, and constructs relative permeability tables (Fanchi 2002). For

this reservoir model, the same petrophysical parameters and relative permeability tables

were used as in the Cavern CAES models.

In order to make a comparable reservoir model and cavern model, the volumes of

the two models are directly related. The volume of the Huntorf salt caverns is both the

pore volume and the bulk volume because of its 100% porosity. Therefore when a

reservoir structure with 20% porosity is considered, the Huntorf volume is the effective

pore volume and the bulk volume can be calculated with Equation 4.1,

(4.1)

where = porosity, VP = pore volume, and VB = bulk volume. Using the Huntorf pore

volume of 300,000 m3 (10,594,400 ft3) and a 20% porosity, a reservoir bulk volume of

1,500,000 m3 (52,972,000 ft3) was calculated. Since EZGEN and ECLIPSE 100© utilize

oilfield units (English units), the remainder of this chapter will present information in

these units, refer to Table 3.7 for the conversion factors. A gross thickness of 125 ft and

a net thickness of 100 ft are assumed for the reservoir model with equal distances of 720

ft in the x and y directions that yield an approximate reservoir bulk volume defined

above. The anticlinal crest is 1,500 ft from surface and the deepest point of the reservoir

is 1,775 ft. These parameters allow the anticline to have a dip of 12º. With these values

established, a number of control points were set. A total of 25 control points were used;

each control point contains an x, y, and z dimension, a gross thickness, a net thickness, a

permeability, and a porosity. Based on these 25 control points, EZGEN could then create

a reservoir structure and an ECLIPSE 100© input model for the initial keywords. Using

3DView, the reservoir can be viewed three-dimensionally; Figure 4.1 shows the anticlinal

reservoir.

86

Figure 4.1 Reservoir structure created with EZGEN for use in ECLIPSE 100© base reservoir model

For the initial reservoir model, a 100 md permeability was used. Different

EZGEN files were written for 1,000 md and 10,000 md permeability. The EZGEN

datasets for the 100 md, 1,000 md, and 10,000 md models can be found in Appendix B.1,

B.2, and B.3, respectively. The order of parameters given above for each control point is

the same order for the control points in the Appendices. The output from the EZGEN

datasets includes the gridding of the reservoir and the distribution of reservoir properties.

The EZGEN outputs for the 100 md, 1,000 md, and 10,000 md models can be found in

Appendix B.4, B.5, and B.6, respectively. These output files can be directly inputted into

an ECLIPSE 100© dataset. As previously mentioned, the injection and compression

87

schedule is the same as the one used in Sensitivity C. The water and gas PVT properties

and relative permeabilities are also the same for the reservoir model. This assumes that

any fluids previously occupying the reservoir have been removed. The addition of the

EZGEN output file alters the porosity, permeability, grid dimensions and the distribution

of the petrophysical properties throughout the grid. The original ECLIPSE 100© input

dataset for the three different permeability models is located in Appendix B.7. There is

only one ECLIPSE 100© dataset for all three permeability models. This is because the

only difference between the models is the EZGEN output file. This unique output file is

inserted into the ECLIPSE 100© dataset with an include statement.

4.2 Reservoir CAES Sensitivities and Results

With the 100 md EZGEN file inputted into the reservoir model, an ECLIPSE

100© run was performed. Just as in the cavern modeling, all of the injected air was

realized in the initial reservoir model run; therefore, the injection rate schedule is the

same as in Figure 3.8. The same was not true for the production rates. These rates could

not be achieved during the initial model run with the original pore volumes and 100 md

permeability. Therefore, additional model runs with an increase in the pore volume were

necessary. The pressure response and the rate schedule for Well P-1 and Well P-2 for the

original and increased pore volumes can be seen in Figure 4.2, Figure 4.3, and Figure 4.4,

respectively.

88

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30

Time (hrs)

Pres

sure

(psi

a)Original Pore Volume

5x Pore Volume

100x Pore Volume

500x Pore Volume

1,000x Pore Volume

10,000x Pore Volume

Figure 4.2 Pressure Response for model runs with varying pore volume and 100 md permeability in a reservoir setting

0

50000

100000

150000

200000

250000

300000

350000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/d

Actual RatesOriginal Pore Volume5x Pore Volume100x Pore Volume500x Pore Volume1,000x Pore Volume10,000x Pore Volume100,000x Pore Volume

Figure 4.3 Production rates with varying pore volumes and 100 md permeability for Well P-1 in a reservoir setting

89

0

50000

100000

150000

200000

250000

300000

350000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/dActual RatesOriginal Pore Volumes5x Pore Volume100x Pore Volume500x Pore Volume1,000x Pore Volume10,000 Pore Volume100,000x Pore Volume

Figure 4.4 Production rates with varying pore volumes and 100 md permeability for Well P-2 in a reservoir setting

In Figure 4.2, a change in pressure can be seen with the original pore volume and

the smaller increases in pore volume. However, as the increase in the pore volume

intensifies, the pressure begins to remain almost constant throughout the series of

compression and expansion. This is due to the fact that a pressure response can not be

expected with such a large pore volume. These large increases in pore volume were

necessary to try and obtain the desired production rates. However, even with an increase

of 100,000 times the original pore volume, the production rates could not be achieved.

During the initial production period, both wells under the 100,000 times the original pore

volume constraint could produce 250,000 Mscf/day, but neither well could obtain the

300,000 Mscf/day production rate during the final production stage. Figure 4.5 illustrates

the percent difference of the different pore volumes for the 100 md model.

90

0

10

20

30

40

50

60

70

80

0 5 10 15 20 25 30

Time (hrs)

Perc

ent D

iffer

ence

Original Pore Volumes5x Pore Volume100x Pore Volume500x Pore Volume1,000x Pore Volume10,000 Pore Volume100,000x Pore Volume

Figure 4.5 Percent difference of the total production rate for 100 md permeability with varying pore volumes

Figure 4.5 confirms the fact that an increase in pore volume does help the

production rates become closer to the Huntorf rates. However, even with the 100,000

times increase in pore volume, the second period of production has a 17% difference.

This lack of ability to attain the specified production rates even with an unrealistically

large pore volume, leads to the conclusion that the injection and production schedule used

at Huntorf can not be reproduced in a reservoir with 100 md permeability. Therefore

model runs with larger permeability were deemed necessary.

The 1,000 md EZGEN input file was then entered into ECLIPSE 100©, using the

original pore volumes, and an initial model run was conducted. The results obtained

from this model run are much better than the results produced with the 100 md model.

As with the previous models, the 1,000 md model had no problems matching the

injection rate schedule. However, with the 1,000 md model, the production rate schedule

91

was almost matched perfectly. With the original pore volumes, Well P-2 obtained the

desired production rates and Well P-1 could match the rate schedule except for the last

hour of production. This is because Well P-2 is updip of Well P-1 in the ECLIPSE 100©

input dataset. The pore volume had to be increased three times in order for Well P-1 to

match the pressure rate of 300,000 Mscf/day in the last hour of production. Figure 4.6

displays the pressure response of the 1,000 md model and Figure 4.7 show the production

rates for the 1000 md model with varying pore volumes for Well P-1. Well P-2 obtained

the production rates with the original pore volumes, so a graph of its rates is unnecessary.

0

100

200

300

400

500

600

700

800

900

1000

0.0 5.0 10.0 15.0 20.0 25.0 30.0

Time (hrs)

Pres

sure

(psi

a)

Original Pore Volume

2x Pore Volume

3x Pore Volume

Figure 4.6 Pressure response for the 1,000 md model with varying pore volumes in a reservoir setting

92

0

50000

100000

150000

200000

250000

300000

350000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/dActual Rate

Original Pore Volume

2x Pore Volume

3x Pore Volume

Figure 4.7 Production rates of model runs with 1,000 md permeability and varying pore volumes for Well P-1 in a reservoir setting

The pressure response with the original pore volumes of the 1,000 md

permeability model is similar to the one produced with the 100 md model. However, the

original pore volume response of the 1,000 md model is not as pronounced as it is with

the 100 md model. Since only an increase of three times the pore volume was necessary

to obtain the production rates, some pressure response can still be seen when the desired

rates are achieved. This amount of increase in pore volume is much more reasonable

than the very large increase in the 100 md permeability model, which couldn’t even

reproduce the desired production schedule. Even with the original pore volumes of the

1,000 md permeability model, all of the rates are matched except for the last production

hour of Well P-2. This is a much more promising rate response than the initial 100 md

model. However, one final increase in permeability was needed to produce an exact

93

match in the actual Huntorf rate response and the modeled rate response with the original

pore volume.

For the next simulation, the 10,000 md EZGEN input file was inputted into the

ECLIPSE 100© model and an initial model run was performed. As expected, the results

from this increase in permeability were even better than the 1,000 md model. The

injection rates were once again obtained and unlike the previous reservoir models, a

match in the production rates was produced without an increase in pore volume. Figure

4.8 contains the pressure response for the 10,000 md model.

0

100

200

300

400

500

600

700

0 5 10 15 20 25

Time (hrs)

Pres

sure

(psi

)

Figure 4.8 Pressure response with original pore volume and 10,000 md permeability in a reservoir setting

94

The pressure response of the 10,000 md model is very similar to that of the 1,000 md

model and therefore a less significant response is seen in this model when compared to

the 100 md model.

4.3 Reservoir CAES Model Comparison

Various graphs have been constructed to offer a comparison between the model

runs with different permeability. Figure 4.9 illustrates the pressure response of the three

permeabilities with the original pore volume.

0

100

200

300

400

500

600

700

800

900

1000

0.0 5.0 10.0 15.0 20.0 25.0 30.0

Time (hrs)

Pres

sure

(psi

a)

100 md perm

1,000 md perm

10,000 md perm

Figure 4.9 Pressure response of the three permeabilities with the original pore volumes in a reservoir setting

95

The 100 md permeability has the same response as the other two permeabilities

up until the 12th hour of the production and injection schedule. This is the beginning of

the first section of production. The lower permeability model does not experience as

much pressure loss as the higher permeability models due to the fact that more production

can occur with the higher permeability models. This higher production caused a higher

pressure loss than with the lower production of the 100 md model. This point is further

exemplified with the comparison of production rates of the different permeabilities with

their original pore volumes. Figure 4.10 and Figure 4.11 show this comparison for Well

P-1 and Well P-2.

0

50000

100000

150000

200000

250000

300000

350000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/d

Actual Rate100 md perm1,000 md perm10,000 md perm

Figure 4.10 Production rate comparison for Well P-1 for the three permeabilities with the original pore volumes in a reservoir setting

96

0

50000

100000

150000

200000

250000

300000

350000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/dActual Rates

100 md perm

1,000 md perm

10,000 md perm

Figure 4.11 Production rate comparison for Well P-2 for the three permeabilities with the original pore volumes in a reservoir setting

Because of its low permeability the 100 md model is not even close to achieving

the specified rates. However, if the permeability is increased by a factor of ten, then the

rates are much more obtainable. This small rate of the 100 md model helps demonstrate

why the pressure loss is not as significant as it is with the 1,000 md and 10,000 md

models. For further comparison, percent difference was calculated for the 100 md and

1,000 md models (the 10,000 md model has 0% difference), which is shown in Figure

4.12.

97

0

10

20

30

40

50

60

70

80

0 5 10 15 20 25 30

Time (hrs)

Perc

ent D

iffer

ence

100 md perm

1,000 md perm

Figure 4.12 Percent difference of the total production rate for 100 md and 1,000 md permeability with the original pore volume

The 1,000 md model has 0% difference during the first phase of production and

the first two hours of the last production stage. Its last hour of production couldn’t quite

obtain 300,000 Mscf/day and therefore has a 8% difference. The percent difference of

the 100 md model is quite different. Its percent difference values range from 43% during

the first hour of production to 75% during the last hour of production.

The same kind of analysis was conducted for the remaining corresponding pore

volumes. Since the 10,000 md model achieved the production rates with its original pore

volume, it’s excluded from the analysis. The 1,000 md model obtained the production

rates with three times the original pore volumes. Therefore the remaining analyses will

include the 100 md and 1,000 md model with two and three times the original pore

volume. Figure 4.13 contains the pressure response for two times the original pore

volume.

98

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30

Time (hrs)

Pres

sure

(psi

a)100 md perm

1,000 md perm

Figure 4.13 Pressure response of the 100 md and 1,000 md models with two times the original pore volume in a reservoir setting

Increasing the pore volume in both of the models diminishes the effect that the

production and injection cycle has on the pressure response. Since pressure and volume

have an inverse relationship, this response is expected. The 100 md model pressure

response still has higher overall pressure values than the 1,000 md model because not as

much of the air could be produced out of the system with the 100 md model. Figure 4.14

and Figure 4.15 display the production rates for two times the pore volume for Well P-1

and Well P-2, respectively.

99

0

50000

100000

150000

200000

250000

300000

350000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/dActual Rate

100 md perm

1,000 md perm

Figure 4.14 Production rates for Well P-1 for 100 md and 1,000 md permeabilities with two times the original pore volume in a reservoir setting

0

50000

100000

150000

200000

250000

300000

350000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/d

Actual Rates

100 md perm

1,000 md perm

Figure 4.15 Production rates for Well P-2 for 100 md and 1000 md permeabilities with two times the original pore volume in a reservoir setting

100

The above figures demonstrate that for Well P-1, the 1,000 md model achieves all

of the production rates, except during the last hour of production. The 100 md model

does not show much improvement from the modeling of the original pore volumes. For

Well P-2, the 1,000 md model matches all of the rates, while the 100 md model is still far

from the desired rates. Figure 4.16 shows the percent difference of the total production

rate for two times the pore volume.

0

10

20

30

40

50

60

70

80

0 5 10 15 20 25 30

Time (hrs)

Msc

f/d

100 md perm

1,000 md perm

Figure 4.16 Percent difference of the total production rate for 100 md and 1,000 md permeability with two times the original pore volume

An increase in the pore volume for the 1,000 md model decreases its nonzero

percent difference from 8% to 1%. The minimum and maximum percent difference for

the 100 md model decreases from 43% and 75 % to 40% and 72%. The 1,000 md model

shows better improvement because an increase in pore volume has a greater effect on

101

higher permeability reservoirs. A higher permeability allows for easier flow; with a

lower permeability reservoir an increase in pore volume helps, but is not enough to

greatly improve flow efficiency.

A final analysis was performed with three times the pore volume for the 100 md

and 1,000 md models. Figure 4.17 displays the pressure comparison of the two models.

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30

Time (hrs)

Pres

sure

(psi

a)

100 md perm

1,000 md perm

Figure 4.17 Pressure response of the 100 md and 1,000 md models with three times the original pore volume in a reservoir setting

With three times the original pore volume, the pressure response of the two models

begins to level. Additionally, the pressure of the models is very similar in the early

stages of injection, but once production begins, the 1,000 md model starts exhibiting

lower pressures than the 100 md model. Once again, this can be attributed to the inability

of the 100 md model to produce the desired rates. The production rates of the two

102

models are depicted in Figure 4.18 and Figure 4.19 for Well P-1 and Well P-2,

respectively.

0

50000

100000

150000

200000

250000

300000

350000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/d

Actual Rate

100 md perm

1,000 md perm

Figure 4.18 Production rates for Well P-1 for 100 md and 1,000 md permeabilities with three times the original pore volume in a reservoir setting

At three times the original pore volume and 1,000 md permeability, the

production rate objectives have been satisfied in both Well P-1 and Well P-2. For the

100 md scenario, some improvement can be seen over the two times the original pore

volume rates, but a significant difference still exists between the Huntorf rates and the

100 md rates.

103

0

50000

100000

150000

200000

250000

300000

350000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/dActual Rates

100 md perm

1,000 md perm

Figure 4.19 Production rates for Well P-2 for 100 md and 1,000 md permeabilities with three times the original pore volume in a reservoir setting

For the 100 md permeability model, the minimum and maximum percent difference

decreased to 39% and 70%. Since the 1,000 md model achieved the production rates at

three times the original pore volume, no additional comparisons can be made between the

1,000 md and 100 md models. Table 4.2 summarizes the percent difference findings

from the above analysis.

Table 4.2 Summary of maximum percent difference between actual production rates and modeled production rates for the three permeability models in a reservoir setting

Permeability Model Production Rate Max % DiffOriginal Pore Volume 2x Pore Volume 3x Pore Volume

100 md 75 72 701,000 md 8 1 0

10,000 md 0 0 0

104

4.4 Discussion of Reservoir CAES Models

A comparison of the cavern models to the reservoir models will first be examined

for repeatability confirmation, followed by a comparison of the different reservoir

models.

4.4.1 Comparison of Cavern Models and Reservoir Models

Comparing the pressure response of the reservoir models (Figure 4.9) to the

pressure response of the cavern models (Figure 3.13) reveals the same general pressure

trend between the corresponding permeabilities. The high and low pressure peaks occur

at the same point in time for the reservoir models and the cavern models. The overall

pressures are lower with the reservoir models; this is because the depths of the reservoir

models are less than those of the cavern models. ECLIPSE 100© was set up to record an

average reservoir pressure based on the recorded pressure at each gridblock. The average

pressure at each gridblock will be higher with the Cavern models because of the

increased ambient pore pressures, matrix pressures, and overburden pressures of a deeper

structure. In spite of the differences in depth, the expected pressure increase with

injection and pressure decrease with production can be seen in both models. This similar

pressure response validates the use of ECLIPSE 100© to model CAES in a cavern and

reservoir setting.

As far as the production and injection rates of the cavern and reservoir models,

the specified injection rate could be achieved in all of the models. With the rates of

Sensitivity C, these decreased production rates were emulated in all two corresponding

permeabilities (1,000 md and 10,000 md). Using these same rates in the reservoir model,

only the 10,000 md model could reproduce the rates with the original pore volume. In

order to get the desired production rates into the 1,000 md model, the pore volume had to

105

be increased three times. By adding porosity and a decreased permeability, the reservoir

could not handle the production rates with its original pore volume. This shows that if

lower permeabilities are to be used, lower rates need to be implemented. The formation

properties inhibit these higher rates from being produced from the reservoir.

4.4.2 Comparison of Reservoir Models

Using the same injection and production schedule and rates as Schedule C in the

Huntorf model, the 10,000 md model would be the best choice for implementing CAES

in a reservoir setting. However, this type of permeability is extremely rare, especially in

the continental U.S. For this reason, the 1,000 md model is the model of choice. Simply

using the original pore volume, the 1,000 md model could achieve all of the Huntorf

production rates with Well P-2 and the first period of production rates with Well P-1.

Within the last production set of three hours, only the last hour of production could not be

achieved with Well P-1. This last hour of production could be matched by increasing the

pore volume to three times the original pore volume. An increase in permeability

combined with a small increase in pore volume is a more reasonable and realistic

scenario than using a smaller permeability with an extremely large pore volume, or an

unrealistically large permeability.

A 1,000 md reservoir will effectively support CAES. However, reservoirs with

this large of a permeability are still hard to come by, especially in the Rocky Mountain

region, where a large amount of wind energy is possible and where aging gas fields could

contribute depleted reservoirs for CAES use. A 100 md model would be a better

representation of reservoirs in the Rockies, but based on the above results, the injection

and production rates need to be adjusted to accommodate for the decreased permeability.

The rates and representative volume of the Huntorf facility are not applicable to a 100 md

106

permeability reservoir. Therefore, these parameters need to be adjusted to more realistic

values in order to accommodate CAES in tighter reservoirs.

107

CHAPTER 5

CAES SIMULATION OF THE GREATER GREEN RIVER BASIN

Based on the conclusions reached in Chapter 4, if CAES is to be implemented in a

lower permeability environment, then different rates need to be used in the compression

and expansion schedule. This scenario was explored by modeling a practical, real-life

application of CAES. A combination of wind energy and CAES was modeled for the

Greater Green River Basin (GGRB) to determine an optimal schedule of injection and

production based on different geologic settings. Modeling of CAES focused on the

Frontier formation within the GGRB. The GGRB has been producing oil and gas out of a

number of reservoirs for over 80 years (DeJarnett, Lim and Calogero 2003). Some of the

fields in the basin are aging and seeing decreased production. After the profitable

production period in these fields have been realized, these depleted reservoirs could serve

as storage for CAES. This area of the country also sees high-sustained wind that would

be ideal for wind farms (Figure 1.1). There are already existing wind farms and the

potential exists for others. Foote Creek Rim, described in Section 1.1.3, is located within

the boundaries of the GGRB, as well as, the Medicine Bow Wind Project Site. The

Medicine Bow site has been in operation since the late 1970s. The main purpose of the

site was to analyze wind data to determine future wind farm construction. Today, the site

includes 10 wind turbines of various size and power ratings. The data collected from the

turbines has determined that the Medicine Bow area is a prime location for wind energy

(Table 5.1) A 40 unit, 100 MW wind farm is in the planning phase, after an

108

environmental assessment determined the wind farm would have no adverse effect on the

surroundings (Platte River Power Authority 2006).

Table 5.1 Predicted energy values based on average wind speeds for Historical Data (1987 – 1992) and 2004 Data and Actual Energy values collected on wind turbines

Historical Data (1987-1992) 2004 Data Performance Summary

MonthAvg Wind

SpeedPredicted

Energy

Avg Wind

SpeedPredicted

EnergyActual Energy

% of Hist. Predicted

% of 2004

Predictedmph MWh mph MWh MWh

Jan 27.8 2,551.7 23.2 2,255.9 2,010.1 78.8 89.1Feb 25.0 1,990.4 16.7 1,279.7 1,336.7 67.2 104.5Mar 22.9 2,023.5 20.8 1,917.5 1,917.7 94.8 100.0Apr 17.3 1,264.6 15.5 1,211.4 1,188.9 94.0 98.1May 16.5 1,148.6 19.2 1,794.0 1,758.3 153.1 98.0Jun 16.1 1,073.3 15.7 1,196.0 1,137.8 106.0 95.1Jul 13.0 671.4 14.9 755.5 820.4 122.2 108.6Aug 14.0 825.7 13.1 920.6 821.5 99.5 89.2Sep 13.2 725.3 14.3 1,101.2 1,036.1 142.9 94.1Oct 19.5 1,624.0 17.0 1,505.9 1,492.6 91.9 99.1Nov 25.1 2,226.4 16.8 1,308.7 1,387.4 62.3 106.0Dec 22.3 1,917.2 25.5 2,474.2 2,483.0 129.5 100.4

Totals 19.4 18,042.0 17.7 17,721.7 17,390.5 96.4 98.1

Modeling of the GGRB uses the same Huntorf injection and production schedule,

given in Table 3.6. This is used because it represents compression and expansion based

on daily energy demands. Since one of the goals of combined wind and CAES is to

compensate for peak demand, basing compression and injection off of energy needs is

logical. Different injection and production rates will be modeled to determine the

optimal rates for a given volumetric and geologic setting.

Using these different injection and production rates, the power necessary to run

the compressor and the expected power output of the turbine have been analyzed. A

wind schedule based on the Medicine Bow Wind Project site was compared to the power

109

input necessary to run the compressor for different injection rates. This comparison

showed the available excess energy that could be directly transferred to the utility grid.

Using the different production rates, energy output from the turbine was determined for

the hours in which the turbine was operating. This analysis will show how much energy

is available to consumers from the combination of wind energy and CAES.

This chapter will begin with geologic information on the GGRB and the fields of

study, followed by the necessary inputs for ECLIPSE 100© modeling, and then focus on

the results from these models. A discussion of the various models and their implications

will conclude the modeling portion of the chapter. The last section of the chapter will

concentrate on the power output possible with various production rates. The units in this

chapter will be in oilfield units, refer to Table 3.7 for conversion factors.

5.1 Geology of the Greater Green River Basin

The GGRB is located in the southwestern portion of Wyoming, extending into

portions of Colorado and Utah. Figure 5.1 highlights the basin.

110

Figure 5.1 Map of the Greater Green River Basin with reserve information on existing oil and gas fields Source: (Kirschbaum and Roberts 2005)

The GGRB is composed of a series of depressions, with separation coming from

various uplifts and ridges (Gibson 1997). Its area encompasses 19,700 square miles. The

basin boundaries are determined by major thrust faults or similar structures, including the

Wyoming-Utah-Idaho Overthrust Belt on the western edge of the basin, the Rawlins

Uplift and Park Range Uplift on the basin’s eastern side, the Uinta Mountains and the

Axial Basin Arch on the southern boundary, and the Wind River Mountains on the

northernmost part of the basin (DeJarnett et al. 2003). The GGRB consists of various

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smaller basins, such as the Green River, Great Divide, Hoback, Sand Wash, and

Washakie Basins. Figure 5.2 highlights the major structures of the GGRB.

Figure 5.2 Major structures within the Greater Green River BasinSource: (Kirschbaum and Roberts 2005)

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Various uplifts, the Rock Springs Uplift being the most prominent, as well as, four

significant anticlines, the Cherokee Ridge, Moxa Arch, Pinedale Anticline, and

Wamsutter Arch exist within the GGRB (Kirschbaum and Roberts 2005). Each uplift

transports crystalline Precambrian basement rocks to the surface and the anticlines

contain deep-seated highs that transform the basement rocks, along with the sedimentary

rocks above.

The majority of the reservoirs within the GGRB are sandstones, most of them of

Cretaceous age (Gibson 1997). Since modeling of combined wind and CAES was

conducted within the Frontier formation, its reservoir properties will be further examined.

The lithology of the Frontier is primarily composed of sandstone, siltstone, and

shale, with small amounts of coal and conglomerate (DeJarnett et al. 2003). Its

depositional environment is considered to be shoreface/deltaic, estuarine, and fluvial.

Five members of the Frontier can be identified on an outcrop in the western portion of the

GGRB. These five members include the Chalk Creek, Coalville, Allen Hollow, Oyster

Ridge, and Dry Hollow, in ascending order. The Frontier is segregated into the First

through Fourth Frontier in the subsurface, with the first and second Frontier acting as the

main reservoirs. In the western portion of the GGRB, the Frontier can be over 1000 ft

thick; towards the northeastern portion of the basin, thicknesses of 600-1,000 ft thick can

be expected. Depths of the Frontier can be around 2,000 ft to upwards of 20,000 ft from

the surface (Figure 5.3).

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Figure 5.3 Subsurface depth of the Frontier formation within the Greater Green River BasinSource: (Kirschbaum and Roberts 2005)

The porosity and permeability of the Frontier are quite variable depending on the

location within the GGRB. Along the Moxa arch, the tidal, fluvial, and shoreface

sandstones all have similar porosities, averaging 9.3% to 11 %, with porosities reaching

maximums around 17%. Permeability in this area can reach up to 50 md, but some

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measurements can be as low as 2 md. In areas east of the Moxa arch, the sandstone

porosity ranges from 2.4% to 28% and the permeabilities can be as low as 0.0008 md or

as high as 500 md (Kirschbaum and Roberts 2005).

The regional dip of the GGRB is 10-15 degrees on the west flank of the Rock

Springs Uplift (Figure 5.2) and 5-8 degrees on the uplift’s east flank (Flores and Bader

1999). According to a study conducted by the U.S. Geological Survey, the geothermal

gradient of the GGRB is highly variable. Gradients in the basin range from 1.2ºF/100 ft

to 2.2ºF/100 ft. Areas east of the Rock Springs Uplift typically exhibit the highest

gradients, while the lowest gradients can be found towards the southern boundary of the

Uinta Mountains (Finn 2005). A typical net to gross ratio in the GGRB ranges from 25%

to 40% (Oil and Gas Investor 2005). The net to gross ratio is described as the percentage

of the thickness of oil-bearing or gas-bearing rock to the thickness of entire reservoir

interval.

5.2 Model Inputs

In order to model CAES in the GGRB, a number of different models were

constructed for a range of porosities, permeabilities, injection and production rates, and

location within the basin. Two different basin locations were selected for modeling.

Locations where the Frontier is at shallower depths were chosen for modeling. The

reasonings behind this are as follows. As previously stated, depths of the Frontier in the

GGRB can range from 2,000 ft to greater than 20,000 ft. However, injecting air down to

depths of 20,000 ft is unreasonable for reasons such as high pressures and temperatures

and air compressibility issues. Additionally, the Frontier can become overpressured

around depths of 8,000 to 12,000 ft; the pressure gradients can exceed 0.9 psia/ft close to

these depths and throughout the remaining portion of the formation. Well design in these

regions is difficult and quite costly. Also at these greater depths, the porosities and

115

permeabilities begin to decline, permeabilities are typically less than 0.1 md. Based on

these stipulations, data from the Tip Top oil and gas field (Figure 5.4) and the Baxter

Basin South field (Figure 5.4) were used for the model locations.

Figure 5.4 Location of Tip Top field and Baxter Basin South within the Greater Green River BasinSource: (Kirschbaum and Roberts 2005)

116

The Tip Top field is located along the northern portion of the Moxa arch, and data from

the entire Moxa arch area was used for the first model location. The Baxter Basin South

model focuses on the central portion of the GGRB, and the high porosities and

permeabilities that can be attributed to this area.

The first model location, Tip Top, was discovered in 1928 and has produced

689,639 MMscf of gas and 8,572 Mbbl of oil. It still has numerous producing wells,

thanks to advanced stimulation and fracturing techniques (Wyoming Oil and Gas

Commission 2006). However, Tip Top is still an aging field with the potential for future

CAES development in depleted reservoirs. For a particular well in the Tip Top field, the

top of the First Frontier is located at 6,689 ft, the Second Frontier top is 7,122 ft, and the

Third Frontier is 7,613 ft. For modeling of this section of the Frontier, it was decided to

only consider the First Frontier due to permeability barriers that would be encountered if

the whole interval was considered. It would be unrealistic to assume that air could be

injected throughout the entire thickness of the Frontier. Therefore an average top of the

First Frontier was assumed to be 7,000 ft and an average thickness of 400 ft was used. A

net to gross ratio of 35% was also assumed leaving 140 ft of net thickness. A net to gross

ratio was used because the depleted reservoirs optimal for CAES will be within the net

reservoir zone. In order to get the remaining reservoir dimensions, the dip information

given in the previous section was applied. Using a dip of 12º, a length of 1,880 ft was

calculated. Applying this value of 1,880 ft to both the x and the y dimensions and using

the 400 ft total thickness, a bulk reservoir volume of 1.4138*109 ft3 was calculated.

Based on the porosity and permeability information given above for the Moxa arch area,

two different sets of porosity and permeabilites were used to construct the two models for

the Tip Top/Moxa arch region. Moxa 1, the best case scenario and Moxa 2, a less

desirable scenario are described in Table 5.2.

For the second model location, the Central GGRB/Baxter Basin South has been

modeled. The Baxter Basin South has been in production since 1922 with cumulative oil

and gas production of 570 bbl and 178,686 MMscf, respectively. As the numbers

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indicate, Baxter is mostly a gas field. A review of some of the well files indicated that

the field produces minimal amounts of water. This creates an optimal scenario for using

CAES in a depleted reservoir. The well files also contained formation tops of the

Frontier formation, ranging from around 2,200 ft to 2,800 ft, with thicknesses around 180

ft (Wyoming Oil and Gas Commission 2006). An average top of 2,500 ft was selected

for modeling. The shallow depth of the Frontier in the Baxter field is also ideal for

CAES. This would help to decrease well costs and even allow for multiple wells, so

higher injection and production rates could be achieved. Baxter is also located close to

the Foote Creek Rim wind farm. Upon hydrocarbon depletion, this field would make an

excellent candidate for combined wind energy and CAES, from both a geologic and

geographic standpoint.

For the modeling of Baxter, two different geologic models were constructed.

However, just as was the case with the Moxa models, the bulk volume of each model is

the same. Since the Baxter South Basin is to the west of the Rock Springs Uplift, a dip of

10º-15º is still evident in this area. Using an average dip of 12º and the specified

thickness of 180 ft, a lateral extent of the reservoir could be approximated; this was

calculated as 850 ft. Assuming equal x and y dimensions, a bulk volume of 1.3005*108

ft3 was determined. Using this volume information, two models were constructed.

Baxter 1 is the best case scenario and Baxter 2 is a less desired scenario. The specifics of

these models are presented in Table 5.2. A net to gross ratio of 40% was applied to both

of the Baxter 1 and Baxter 2 models, yielding a net thickness of 72 ft.

Table 5.2 Model inputs for the Moxa and Baxter models

Model Porosity Permeability Pore Volume% md ft3

Moxa 1 17 50 240.339E+06Moxa 2 10 10 141.376E+06Baxter 1 28 500 36.414E+06Baxter 2 5 100 6.503E+06

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Each model required a different EZGEN file. Since both the Moxa arch and Rock

Springs Uplift are anticlines, an anticlinal reservoir with the appropriate volumetric and

geologic properties, defined above, was constructed for each model. All of the EZGEN

input files contain 25 control points to create the structure and distribute parameters. The

EZGEN input files for the Moxa 1, Moxa 2, Baxter 1, and Baxter 2, models can be found

in Appendix C.1, C.2, C.3, and C.4, respectively. Figure 5.5 and Figure 5.6 show the

reservoir structure for the Moxa models and the Baxter models, respectively.

Figure 5.5 Reservoir structure created with EZGEN for use in Moxa 1 and Moxa 2 reservoir models

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Figure 5.6 Reservoir structure created with EZGEN for use in Baxter 1 and Baxter 2 reservoir models

The EZGEN output files that were entered into ECLIPSE 100©, these files for the

Moxa 1, Moxa 2, Baxter 1, and Baxter 2 models can be found in Appendix C.5, C.6, C.7,

and C.8, respectively. With the construction of the grid and distribution of parameters in

ECLIPSE 100© with EZGEN, the appropriate injection and production rates could now

be determined for the Huntorf daily demand schedule. According to Benge and Dew

(2006), injection of 100 MMscf/day of a 65% H2S and 35% CO2 is possible within the

Madison formation of the GGRB. The Madison is a carbonate, consisting of anhydrite

and dolomite zones, all within a limestone formation. Permeability in the Madison

ranges from 0.1 md to 11 md. These high injection rates can be handled by the Madison

because of the secondary permeability that is made possible by the dolomitization

process. These high rates are also possible in areas that are naturally fractured, or could

120

be artificially fractured (Benge and Dew 2004). According to DeJarnett et al. (2003), the

Frontier formation is typically naturally fractured (DeJarnett et al. 2003). Based on the

injection rate capable within the GGRB, a maximum injection rate of 100 MMscf/day

will be modeled. Since the Huntorf injection and production schedule in Table 3.6 is

based on daily energy demand, this same schedule will be used in the GGRB modeling.

Huntorf also uses a four-to-one ratio of production rates to injection rates. This

production to injection ratio will be modeled, as well as, a two-to-one ratio for each

geographical model. Injection ratios of 100, 50, and 1 MMscf/day were initially modeled

for each model. However, the geologic properties of some models inhibited one or more

of these rates from being realized. Based on the results from these simulations and the

power analysis for the CAES compressor and turbine, optimal injection and production

rates were selected for each model.

Some of the production rates being used in the models could be hard to obtain

because of equipment limitations. Using these high production rates provides an

indication of what the formation is capable of producing. If these high rates are to be

achieved in an actual depleted reservoir, then a large production string would have to be

used, or multiple wells could be employed. The latter of the two options would be a

realistic scenario, given the number of existing wells in the two fields of study. As long

as the wells are tapping the same reservoir, then multiple wells could be used to obtain

these high production rates.

The PVT and saturation properties were kept the same as in the Cavern CAES and

Reservoir CAES models. If residual water can be pumped from the reservoir before

CAES operations begin and leakage from aquifers is not present, then this assumption

should be adequate. An ECLIPSE 100© example dataset for the GGRB modeling can be

found in Appendix C.9.

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5.3 Model Results

Results from various runs of the models described in Table 5.1 have been

conducted and will be explained in detail in the following subsections.

5.3.1 Moxa 1 Model Results

The first model to be ran in ECLIPSE 100© was the Moxa 1 model. Table 5.3

shows the different injection and production rates that were modeled for the Moxa 1

model based on the results from the previous rate response.

Table 5.3 Moxa 1 modeled injection and production rates

Inj Rate Prod Rate Time SpanMMscf/day MMscf/day Days

100 400 11 75 period, rest 100 400 11 75 period, rest 100 400 31 75 period, rest 100 200 33 75 period, rest 100 200 3

50 200 350 100 31 4 31 2 3

For the initial model run, an injection rate of 100 MMscf/day and a production

rate of 400 MMscf/day were used. The reservoir could reproduce the large production

rate, but during the second injection period, the model could not obtain the 100

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MMscf/day rate. Figure 5.7 shows the modeled injection rates and Figure 5.8 shows the

modeled production rates.

0

20000

40000

60000

80000

100000

120000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/day

Actual Inj RatesInj Rates for 400 MMscf/day

Figure 5.7 Moxa 1 injection rates for the 100 MMscf/day injection and 400 MMscf/day production model for one day

123

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/dActual Rates

Modeled Rates

Figure 5.8 Moxa 1 production rates for the 100 MMscf/day injection and 400 MMscf/day production model for one day

In order to determine if future injection rates were being meet, the model was

extended over a period of three days, using the same daily injection and production

schedule. The results from this model run showed that only the second period of

injection on the first day was not being achieved. Therefore, this second injection period

was decreased from the 100 MMscf/day to 75 MMscf/day. The model was again run

with the one period of new injection rates. With this the change, all the injection rates

were achieved and there were no adverse effect on the production rates. Figure 5.9

shows the actual injection rates, the modeled injection rates, and the modeled injection

rates with the one 75 MMscf/day injection period; these are all modeled over a three-day

span.

124

0

20000

40000

60000

80000

100000

120000

0 10 20 30 40 50 60 70 80

Time (hrs)

Msc

f/day

Actual Inj RatesInj Rates for 400 MMscf/dayInj Rates for 400 MMscf/day w/ 1 75MMscf/day inj period

Figure 5.9 Moxa 1 injection rates for the 100 MMscf/day injection and 400 MMscf/day production model for three days

The pressure response was the same for the single daily schedule as it was for the

first day of the three-day schedule. Therefore, Figure 5.10 shows the pressure response

for the three-day schedule with the original injection schedule of 100 MMscf/day and

with the modified injection schedule of one injection period of 75 MMscf/day and the

remaining injection periods staying constant at 100 MMscf/day. To provide more detail

of the actual pressures seen in the model, Figure 5.11 provides a zoomed in view of the

pressure response.

125

0

500

1000

1500

2000

2500

3000

3500

4000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

3 days

3 days w/ 1 lower inj period

Figure 5.10 Moxa 1 pressure response for the 100 MMscf/day injection and 400 MMscf/day production model for three days

3500

3550

3600

3650

3700

3750

3800

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

1 day

3 days

3 days w/ 1 lower inj period

Figure 5.11 Zoomed in Moxa 1 pressure response for the 100 MMscf/day injection and 400 MMscf/day production model for three days

126

Figure 5.11 shows that one lower injection period has a minimal effect on the

pressure response. However, the overall pressure response indicates that the production

is too high because the pressure is continually dropping. The highs and lows expected

with the injection and production schedule are still present, but the pressure isn’t able to

rebound to as high of a pressure as it demonstrated in the same time step of the previous

day. This indicates that lower production rates need to be modeled. Therefore, a two-to-

one ratio of production and injection rates was modeled.

For the initial model, with the 100 MMscf/day of injection and 200 MMscf/day of

production, a three-day time frame was considered using the same production and

injection schedule with the one decreased injection period. As before, the reservoir had

no issues handling the production rates. However, a decrease in production rate caused

the second injection period in days 2 and 3 to not reach 100 MMscf/day (Figure 5.12).

0

20000

40000

60000

80000

100000

120000

0 10 20 30 40 50 60 70 80

Time (hrs)

Msc

f/day

Actual Inj Rates w/ 1 75 MMscf/day inj period

Inj Rates for 200 MMscf/day prod w/ 1 75MMscf/day inj period

Figure 5.12 Moxa 1 injection rates for the 100 MMscf/day injection with 1 injection period of 75 MMscf/day and 200 MMscf/day production model for three days

127

Based on the above results, another model run was conducted with all of the daily

second injection periods to 75 MMscf/day. With this change, all the specified injection

rates could be injected into the reservoir. Figure 5.13 provides a comparison of the

injection schedule that was initially applied to the 100 MMscf/day injection and 200

MMscf/day production model to the schedule that was necessary to obtain all of the

specified injection rates.

0

20000

40000

60000

80000

100000

120000

0 10 20 30 40 50 60 70 80

Time (hrs)

Msc

f/day

Previous Inj Rate schedule w/ 1 75 MMscf/day inj period

New Inj Rate schedule w/ 3 75 MMscf/day inj period

Figure 5.13 Moxa 1 injection rates for the actual initial schedule of 100 MMscf/day injection with 1 injection period of 75 MMscf/day and 200 MMscf/day production for three days compared to the

new schedule with 3 injection periods of 75 MMscf/day for three days

The pressure of the Moxa 1 model with the one lower injection period and the

pressure with three lower injection periods are displayed normally in Figure 5.14 and

with greater detail in Figure 5.15.

128

0

500

1000

1500

2000

2500

3000

3500

4000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)3 days w/ 1 lower inj period

3 days w/ 3 lower inj periods

Figure 5.14 Moxa 1 pressure response for the 100 MMscf/day injection and 200 MMscf/day production model for three days with 2 different injection schedules

3500

3550

3600

3650

3700

3750

3800

3850

3900

3950

4000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

3 days w/ 1 lower inj period

3 days w/ 3 lower inj periods

Figure 5.15 Zoomed in Moxa 1 pressure response for the 100 MMscf/day injection and 200 MMscf/day production model for three days with 2 different injection schedules

129

Figure 5.15 shows that an adjustment of the second set of injection rates on each

daily rate schedule definitely has an effect on the pressure response. With the one lower

injection period, the pressure has a steadily decreasing trend. However, with the three

lower injection periods, the overall pressure change is constant. This relationship is

desired because all of the intended production rates are obtained and the overall pressure

change is constant throughout the three days of the daily production and injection

schedule.

The Moxa 1 model was then modified to inject a 50 MMscf/day rate. A four-to-

one and two-to-one production to injection ratio again served as the model runs. Figure

5.16 shows the pressure response for the production to injection ratios for a 50

MMscf/day injection rate and Figure 5.17 provides a zoomed in view of Figure 5.16.

0

500

1000

1500

2000

2500

3000

3500

4000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

3 days w/ 200 MMscf/day prduction

3 days w/ 100 MMscf/day production

Figure 5.16 Moxa 1 pressure response for the 50 MMscf/day injection and 200 and 100 MMscf/day production model for three days

130

3500

3550

3600

3650

3700

3750

3800

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)3 days w/ 200 MMscf/day production

3 days w/ 100 MMscf/day production

Figure 5.17 Zoomed in Moxa 1 pressure response for the 50 MMscf/day injection and 200 and 100 MMscf/day production model for three days

With these model stipulations, all of the injection and production rates could be achieved

without having to alter any of the injection rates. However, the overall change in

pressure decreased to a greater degree than it did with the previous models. The two-to-

one production to injection ratio could also inject and produce the specified rates without

any alterations, but the effect on the pressure was the same as with the four-to-one ratio.

Both the four-to-one and the two-to-one models show a minimal pressure change,

but the two-to-one model keeps a relatively constant pressure throughout the injection

and production schedule. The four-to-one model has the same highs and lows as the two-

to-one model, but the overall pressure change is decreasing over the three-day period.

The overall change in pressure of the four-to-one model is 28 psia, while the overall

change in pressure for the two-to-one model is 8.55 psia. By examining Figure 5.17, it

can be seen that the highs and lows are not significantly different between the two

131

models. Therefore, the majority of the difference between the overall change in pressure

between the two models can be attributed to the steadily declining pressures of the four-

to-one model.

From the previous two models, it can be seen that a two-to-one injection to

production ratio allows the change in pressure to remain relatively constant. The final

modification to the Moxa 1 model involved decreasing the injection rate to 1 MMscf/day.

A modeling of this rate showed the reservoir response to smaller rates that might be

necessary because of equipment limitations. With this adjustment, all of the injection and

production rates were realized for both the 4 MMscf/day (four-to-one ratio) and the 1

MMscf/day (two-to-one ratio) production rates. The normal pressure response is similar

to the above figures; Figure 5.18 shows a zoomed in view of the pressure response for the

4 MMscf/day and 1 MMscf/day production rates.

3500

3550

3600

3650

3700

3750

3800

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

3 days w/ 4 MMscf/day prduction

3 days w/ 2 MMscf/day production

Figure 5.18 Zoomed in Moxa 1 pressure response for the 1 MMscf/day injection and 4 and 2 MMscf/day production model for three days

132

The pressure change within the reservoir was minimal due to the reservoir size and the

low rates. Even with a maximized view of the pressure response, hardly any change in

pressure can be seen. This is due to the fact that these low of rates have a minimal

response on the reservoir because of the reservoir’s size. The overall change in pressure

is 0.563 psia for the four-to-one ratio model and 0.171 psia for the two-to-one ratio

model.

5.3.2 Moxa 2 Model Results

In order to cover different facets of the Moxa arch and Tip Top field area, the

Moxa 2 EZGEN dataset was constructed with lower possible values of porosity and

permeability. After input into the ECLIPSE 100© dataset, an initial model run could be

conducted. As with the Moxa 1 model, the Moxa 2 modeling began with an injection rate

of 100 MMscf/day and a 400 MMscf/day. The various injection and production rates that

were simulated with the Moxa 2 model are shown in Table 5.4.

Table 5.4 Moxa 2 modeled injection and production rates

Inj Rate Prod Rate Time SpanMMscf/day MMscf/day Days

100 400 120 80 320 40 31 4 31 2 3

The results obtained with the initial rates of 100 MMscf/day of injection and 400

MMscf/day of production were quite different than what was seen in Moxa 1. With the

133

Moxa 2 model, neither the injection or production rates could be obtained. Additionally,

the highs and lows of the pressure response were not as pronounced as in the initial

model runs with Moxa 1. Figure 5.19 and Figure 5.20 show the injection and production

rates, respectively and Figure 5.21 shows the detailed pressure response of the 100

MMscf/day injection and 400 MMscf/day production rates model.

0

20000

40000

60000

80000

100000

120000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/day

Actual Inj RatesInj Rates for 400 MMscf/day

Figure 5.19 Moxa 2 injection rates for the 100 MMscf/day injection and 400 MMscf/day production model for one day

134

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0 5 10 15 20 25 30

Time (hrs)

Msc

f/d

Actual Rates

Production Rate of 400 MMscf/day

Figure 5.20 Moxa 2 production rates for the 100 MMscf/day injection and 400 MMscf/day production model for one day

3500

3550

3600

3650

3700

3750

3800

0 5 10 15 20 25 30

Time (hrs)

Pres

sure

(psi

a)

Figure 5.21 Zoomed in Moxa 2 pressure response for the 100 MMscf/day injection and 400 MMscf/day production model for one day

135

Figure 5.19 and Figure 5.20 indicate that the lower porosity and permeability of

Moxa 2 are not able to handle the high injection and production rates. By examining the

injection and production rates that could be achieved through the modeling, it looked as

though a 25 MMscf/day injection and 100 MMscf/day production rate could be handled

by the reservoir. However, the model still had trouble injecting the 25 MMscf/day. The

100 MMscf/day production rate could be obtained, but a decrease to 25 MMscf/day

injection rate was still not enough. Therefore, a rate schedule of 20 MMscf/day injection

and 80 MMscf/day production rates was modeled. The Moxa 2 model was able to inject

and produce the specified rates of this schedule, so the two-to-one production to injection

ratio was also modeled. All of the rates were reproduced in this model as well. The

zoomed in pressure response for the four-to-one and two-to-one production to injection

ratios for an injection rate of 20 MMscf/day is given in Figure 5.22.

3500

3550

3600

3650

3700

3750

3800

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

3 days w/ 80 MMscf/day production

3 days w/ 40 MMscf/day production

Figure 5.22 Zoomed in Moxa 2 pressure response for the 20 MMscf/day injection and 80 and 40 MMscf/day production model for three days

136

Figure 5.22 shows that with a 20 MMscf/day injection rate, the pressure begins to

level off; the highs and lows are not as prominent. The above figure also emulates a

result found with the Moxa 1 model. With a two-to-one production to injection ratio, the

overall change in pressure is not as large as with the four-to-one ratio. In this case, the

pressure change was 19 psia for the four-to-one ratio model and 5.8 psia for the two-to-

one ratio model.

As with the Moxa 1 model, lower rates were modeled with the Moxa 2 model to

determine how the reservoir will respond. As expected, all of the injected rates of 1

MMscf/day and the production rates of 4 MMscf/day for the four-to-one model and 2

MMscf/day for the two-to-one model were obtained in the model runs. The pressure

response for these two simulations is given in Figure 5.23.

3500

3550

3600

3650

3700

3750

3800

3850

3900

3950

4000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

3 days w/ 4 MMscf/day prduction

3 days w/ 2 MMscf/day production

Figure 5.23 Zoomed in Moxa 2 pressure response for the 1 MMscf/day injection and 4 and 2 MMscf/day production model for three days

137

The results of the Moxa 2 model are similar to the results seen with the Moxa 1

model for these low rates. Because of the size of the reservoir, the injection and

production do not have much of an effect on the reservoir. Therefore the change in

pressure is minimal.

5.3.3 Baxter 1 Model Results

Using the EZGEN dataset constructed for the Baxter 1 model, an initial model run

was performed in ECLIPSE 100© with the injection rates of 100 MMscf/day and the

production rates of 400 MMscf/day. The injection and production rates modeled for the

Baxter 2 model is shown below in Table 5.5.

Table 5.5 Baxter 1 modeled injection and production rates

Inj Rate Prod Rate Time SpanMMscf/day MMscf/day Days

100 400 3100 200 350 200 350 100 31 4 31 2 3

All of the Baxter model runs used a three-day time span for the injection and

production schedule. Due to the high porosity and permeability associated with the

Baxter 1 model, all of the specified injection and production rates could be replicated in

the model. Another model run was conducted with a two-to-one ratio of production to

injection rates. The pressure response for the four-to-one ratio and the two-to-one ratio is

depicted in Figure 5.24.

138

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)3 days w/ 400 MMscf/day prod rate

3 days w/ 200 MMscf/day prod rate

Figure 5.24 Baxter 1 pressure response for the 100 MMscf/day injection and 400 and 200 MMscf/day production model for three days

The same result is again seen with the Baxter 1 model as with the previous

models. The four-to-one ratio model has a greater overall pressure drop than the two-to-

one ratio. With this model, however, the slight pressure increase that was seen with the

two-to-one ratio model is more pronounced. The change in pressure is 136 psia with the

four-to-one model and 97 psia for the two-to-one model.

Since the Baxter 1 model had no problem injecting 100 MMscf/day and

producing 400 MMscf/day, the reservoir could obviously handle the lower injection rates

of 50 MMscf/day and 1 MMscf/day and the corresponding production to injection ratios

of four-to-one and two-to-one. The pressure response for the 50 MMscf/day models and

the 1 MMscf/day models are given in Figure 5.25 and Figure 5.26, respectively.

139

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)3 days w/ 200 MMscf/day production

3 days w/ 100 MMscf/day production

Figure 5.25 Baxter 1 pressure response for the 50 MMscf/day injection and 200 and 100 MMscf/day production model for three days

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

3 days w/ 4 MMscf/day production

3 days w/ 2 MMscf/day production

Figure 5.26 Baxter 1 pressure response for the 1 MMscf/day injection and 4 and 2 MMscf/day production model for three days

140

The 50 MMscf/day exhibits the same behavior as the 100 MMscf/day model, but

the highs and lows are not as pronounced as they are in the 100 MMscf/day model. The

low rate model shows little pressure response because of the reservoir size and properties.

In order to determine what the reservoir was capable of handling with these low rates, a

large production to injection ratio model was run. With a 1 MMscf/day injection rate, a

20 to 1 ratio was achieved for production to injection rates without any issues obtaining

the specified rates. With this large of a ratio, the pressure never rebounds and a steady

decline in pressure is observed. Also when this high of a ratio is considered, the fact that

air is not being replaced as it is extracted from the reservoir needs to be taken into

account.

5.3.4 Baxter 2 Model Results

The lower porosity and permeability of the Baxter 2 model had a significant effect

on production rates and consequently, on the pressure response. Once again, the initial

model run used injection rates of 100 MMscf/day and production rates of 400

MMscf/day. All of the modeled injection and production rates for Baxter 2 are located in

Table 5.6.

Table 5.6 Baxter 2 modeled injection and production rates

Inj Rate Prod Rate Time SpanMMscf/day MMscf/day Days

100 400 325 100 325 50 31 4 31 2 3

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This reservoir model had no problem injecting the 100 MMscf/day, but could only

produce around ¼ of the production rates. Figure 5.27 shows the production rates for the

100 MMscf/day injection rates.

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0 10 20 30 40 50 60 70 80

Time (hrs)

Msc

f/d

Actual RatesProduction Rate of 400 MMscf/day

Figure 5.27 Baxter 2 production rates for the 100 MMscf/day injection and 400 MMscf/day production model for three days

The production is increasing from one day of production to the next, but even by

the third day, the modeled production rates are still half of the desired rates, at best. The

pressure response emulates this lack of production with continually increasing values.

With all of the injection rates entering the reservoir, but not all of the production rates,

the pressure has to increase. The reservoir volume is at its threshold from the increased

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supply of air, but without the available production to relieve the pressure, the pressure

just keeps increasing. Figure 5.28 shows this pressure response.

0

200

400

600

800

1000

1200

1400

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

Figure 5.28 Baxter 2 pressure response for the 100 MMscf/day injection and 400 MMscf/day production model for three days

The highs and lows expected with the compression and expansion of air are still

present, but with not as much production to relieve the pressure, a steadily increasing

pressure occurs. In order to try and find more optimal injection and production rates, a

model with 25 MMscf/day of injection and 100 MMscf/day of production was run. Even

with this decrease in rates, the reservoir was still not capable of producing 100

MMscf/day of air. Figure 5.29 displays the production rate results from the model run.

143

0

20000

40000

60000

80000

100000

120000

0 10 20 30 40 50 60 70 80

Time (hrs)

Msc

f/dActual RatesProduction Rate of 400 MMscf/day

Figure 5.29 Baxter 2 production rates for the 25 MMscf/day injection and 100 MMscf/day production model for three days

The production rates with this model are an improvement over the previous model

run of 100 MMscf/day injection and 400 MMscf/day production, but these rates are still

too high to achieve the desired production rate. The pressure response associated with

the injection rate of 25 MMscf/day and the production rate 100 MMscf/day is displayed

in Figure 5.30.

144

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

Figure 5.30 Baxter 2 pressure response for the 25 MMscf/day injection and 100 MMscf/day production model for three days

The pressure response with these rates for the Baxter 2 model is similar to the

response seen with the previous models. This is because more of the air is being

produced out of the reservoir, allowing for a greater pressure drop to occur. Since the

rates were very close to being achieved for the four-to-one ratio, a two-to-one ratio model

was run. By decreasing the production rates to 50 MMscf/day, all of the rates could be

obtained. The pressure response of the two-to-one ratio model for the 25 MMscf/day

injection rate is located in Figure 5.31.

145

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)

Figure 5.31 Baxter 2 pressure response for the 25 MMscf/day injection and 50 MMscf/day production model for three days

Lower injection rates were modeled with a four-to-one ratio, but with the low

porosity of the Baxter 2 model, a four-to-one ratio could not be achieved. Therefore, the

final model with the Baxter 2 model was the low rate model of 1 MMscf/day of injection

and a four-to-one and two-to-one ratio of production. Like the previous models, all of the

injection and production rates with these low rates were realized and the change in

pressure (Figure 5.32) was minimal.

146

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80

Time (hrs)

Pres

sure

(psi

a)3 days w/ 4 MMscf/day prduction

3 days w/ 2 MMscf/day production

Figure 5.32 Baxter 2 pressure response for the 1 MMscf/day injection and 4 and 1 MMscf/day production model for three days

5.4 Power Analysis

A power analysis will be provided for power output that the turbine can generate

based on the production rates obtained above. Efficiencies have not been included in the

calculations since these vary between different pieces of machinery. Therefore, when

efficiencies are considered the power output supplied by the turbine will be lower than

indicated.

In order to determine the power that can be supplied based on the modeled

production rates, a series of calculations were performed. These calculations were

independent of depth and reservoir properties since the calculations are made at the inlet

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and outlet of the turbine, which are both at the surface. Therefore, the power output for

the Moxa and Baxter models for the various production rates is the same.

To begin the series of calculations, the production rates were converted to

volumes of air based on the length of the given production period. There were two daily

production periods, one lasting for two hours and the other for three hours. Once the

produced air volume from each production period was found, then the mass of air was

calculated using the air surface density of 1.2395 kg/m3 (0.0774 lbm/ft3). According to

Cheung et al. (2006) the power output for a CAES turbine is

(5.5)

where Qs = power output, m = mass of air, cp = specific heat of air = 1.05 kJ/kg K at

surface, and = change in temperature. The term uses the ambient air temperature

of 283.149 K (50 F) and the average temperature of the air after it has been heated for

turbine entry of 1,050 K (1,430 F) (Cheung et al. 2006). With the power calculated, the

energy, in MWh, could also be found by dividing the power term by the power

generation period of either two or three hours. The results from these calculations for all

of the production rates for the Moxa models and the Baxter models are included in Table

5.7.

Table 5.7 Generated power and from the various production rates of the Moxa and Baxter models

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Production Rate Air Volume Air Mass

Generated Power

Total Daily Power

MMscf/day m3 kg MW MW2 hr 3 hr 2 hr 3 hr 2 hr 3 hr Total

400 943894.9 1415842.41169987.

01754980.

5 261.7 392.5 654.2200 471947.5 707921.2 584993.5 877490.3 130.8 196.3 327.1100 235973.7 353960.6 292496.8 438745.1 65.4 98.1 163.680 188779.0 283168.5 233997.4 350996.1 52.3 78.5 130.850 117986.9 176980.3 146248.4 219372.6 32.7 49.1 81.840 94389.5 141584.2 116998.7 175498.1 26.2 39.3 65.44 9438.9 14158.4 11699.9 17549.8 2.6 3.9 6.52 4719.5 7079.2 5849.9 8774.9 1.3 2.0 3.3

The power created from modeled production rates is quite large for the higher

production rates. If the facilities can support such large rates through wide production

strings or multiple wells, then a significant amount of stored energy can be made

available to meet energy demands. In order to make a comparison to conventional

energy sources, Table 5.8 was constructed.

Table 5. 8 Comparison of CAES daily power output to amount of natural gas and coal necessary to achieve the same power over the five-hour production period

CAES Daily PowerNatural Gas using 820

BTU/scfCoal using 26

million BTU/ton# of Homes

Provided w/ PowerMW BTU/hr scf MMscf ton  

654.2 223.23E+07 1361.1E+04 13.6 429.3 198,469327.1 111.61E+07 680.57E+04 6.8 214.6 99,234163.6 55.807E+07 340.28E+04 3.4 107.3 49,617130.8 44.645E+07 272.23E+04 2.7 85.9 39,69481.8 27.903E+07 170.14E+04 1.7 53.7 24,80965.4 22.323E+07 136.11E+04 1.4 42.9 19,8476.5 2.2323E+07 13.611E+04 0.1 4.3 1,9853.3 1.1161E+07 6.8057E+04 0.1 2.1 992

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For each five-hour power-generating period of CAES, a conversion was made to BTU/hr.

This could then be divided by the 820 BTU/scf for natural gas and the 26 million

BTU/ton of coal (Forest Products Laboratory 2004). Multiplying these values by the

five-hour production period gives the equivalent amount of the more conventional energy

sources. In order to include a more realistic power application, the number of homes that

each MW value can supply power for is provided. With the highest power output, almost

200,000 homes can be provided with electricity.

5.4 Discussion of GGRB Models and Power Implications

The overall response to the various injection and production rates of the GGRB

models was encouraging for implementing CAES in actual reservoirs. The high porosity

and permeability of the Baxter 1 model allowed for the achievement of the highest

modeled injection and production rates. If the appropriate surface facilities can be used,

then 654.2 MW of daily power can be produced with the addition of a CAES plant.

When this is combined with an average wind power around 1,450 MW, a significant

energy source is available. Having 654.2 MW accessible when the wind is not blowing,

or during a period of peak demand, is a great solution to the problem of wind being an

intermittent energy source. For the Baxter 1 model, the optimal injection and production

rates are the 100 MMscf/day of injection and 400 MMscf/day of production. If

information can be found that justifies a higher injection rate in the region, then the

Baxter model could use even higher rates. However, based on the current information

available, the injection and production rates above are the optimal rates for this high

porosity and permeability model. Additionally, if equipment restraints limit the injection

and production rates, then lower rates will still provide an adequate power output (Table

5.10) to compensate for the shortcomings of wind energy.

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The Moxa 1 model could handle the large production rate of 400 MMscf/day

associated with the initial model run, but could not reproduce the 100 MMscf/day

injection rate during the second injection period of the daily schedule. Once this second

injection period was reduced to 75 MMscf/day, the model could handle all of the

injection and production rates. The power output that can be produced with the Moxa 1

model is 654.2 MW, the same as the power output with the Baxter 1 model. Lower rates

can be used if equipment limitations exist, but the optimal injection and production

schedule for the porosity and permeability of the Moxa 1 model is 100 MMscf/day

injection rates, with 75 MMscf/day injection rates during the second daily period of

injection, and a 400 MMscf/day production rate.

With the lower porosity and permeability values of the Moxa 2 and Baxter 2

models, the high injection and production rates of 100 MMscf/day and 400 MMscf/day,

respectively, could not be achieved. With the Moxa 2 model, the injection rate had to be

decreased to 20 MMscf/day with a corresponding production rate of 80 MMscf/day. This

yields a daily power output of 130.8 MW, which is still a large enough output to justify

the combination of CAES with wind energy in the region. The Baxter 2 model has a

lower porosity, but a higher permeability than the Moxa 2 model. With this combination,

it can achieve a slightly higher injection rate than the Moxa 2 model, but a lower

production rate. The optimal schedule obtained with the Baxter 2 model is an injection

rate of 25 MMscf/day and a production rate of 50 MMscf/day. Even with lower injection

rates, this model required a two-to-one production ratio. With this production rate, the

Baxter 2 model is capable of supplying a daily power output of 81.8 MW. Once again,

both of these models could use lower injection and production rates to compensate for

wind intermittency, if the facilities could not accommodate the higher rates.

Based on the above results, if the reservoir unit has a low permeability then the

injection rates are harder to achieve than the production rates. Overall, both of the Baxter

models have higher permeabilities than the Moxa models; the lowest permeability with

the Baxter models is 100 md (Baxter 2), which is higher than the highest permeability of

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50 md, with the Moxa 1 model. Consequently, the Baxter models had no problems

injecting the 100 MMscf/day injection rates. Both of the Moxa models had difficulties

injecting the 100 MMscf/day rates. The Baxter 1 model had a very high porosity and

permeability and therefore had no issues obtaining any of the rates. The Baxter 2 model

has low porosity and a higher permeability, which has more of an effect on the

production rates. Since Moxa 2 has both low porosity and low permeability, the

production rates are affected, as well as, the injection rates. Therefore, according to these

four models, permeability is the driving force for injecting and producing the desired

rates. This is because permeability is the measure of a formation’s ability to transport

fluid and porosity is the amount of storage space available. A formation with a higher

permeability allows fluid to flow more easily than a formation with a lower permeability.

This is why the Baxter models could inject air into the formation better than the Moxa

models. Permeability also affects production; the higher the permeability the better the

fluid can be produced out of a formation. The production issue associated with the

Baxter 2 model can be attributed to the combination of a 5% porosity and a 100 md

permeability.

The optimal rate schedules described above all incorporate the four-to-one

production to injection ratios. These ratios were selected because they optimize the

power production that can be obtained. The two-to-one ratios maintain the pressure in

the reservoir better, but do not provide as much power as the four-to-one ratios. If

pressure loss in the reservoir ever becomes an issue, then the production to injection ratio

can be reduced for a few days, until the desired pressures are restored. At this time, the

four-to-one ratio can be resumed to optimize power production. Additionally, if not as

much energy from storage is necessary during a certain time period, then the production

to injection ratio can be reduced, or both rates could be minimized. By knowing the

maximum injection and production rates that these different reservoir models can handle,

a combination of rates can be used to achieve the desired power output from storage of

compressed air.

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CHAPTER 6

CONCLUSIONS

This final chapter will present the major results and conclusions from the

modeling of CAES in different geologic settings. All three model sets will be examined

and some similarities and differences will be discussed. The important conclusions of the

study will then be the focus. The chapter will look at recommendations for future work

in the area of implementing CAES in reservoir environments and a final discussion will

conclude the study.

6.1 Major Results

The modeling of CAES began with the construction of the Cavern CAES model

to simulate the Huntorf CAES facility. The goal of this model was to obtain a pressure

match using the same production and injection schedule and rates that are used at

Huntorf. With this initial model setup, the pressure match was unsatisfactory. Changes

in pore volume were simulated to try to obtain a match, but these attempts were

unsuccessful. After a personal communication with F. Crotogino, it was realized that the

production rates are maximum obtainable rates. Therefore, the production rates were

decreased for the next model run. With this adjustment, an acceptable pressure match

could be realized. This model with the decreased production rates is labeled as

Sensitivity C and was selected as the best representation of the Huntorf facility. Different

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permeabilities were modeled with the Cavern CAES model to show that permeability was

not having an effect on the pressure response in a cavern environment; the result was as

desired.

Successful modeling of the Huntorf facility with ECLIPSE 100© shows that

petroleum engineering tools can be used to examine sustainable energy options, such as

CAES. Achieving a reasonable pressure match with the recorded Huntorf pressures,

verifies the model’s creditability for simulating similar processes. Therefore, the model

was then modified to simulate a pore volume equal to the Huntorf cavern volume and the

same rates and schedule as Sensitivity C, but in a reservoir. EZGEN files were

constructed for 100 md, 1,000 md, and 10,000 md permeability and model runs were

conducted in ECLIPSE 100©. The 100 md model could not achieve the Huntorf

production rates, even with an increase up to 100,000 times the original pore volume.

The rates that are used at Huntorf are too high for this lower permeability and porosity

environment. The 1,000 md model gave a better response. It only took three times the

original pore volume, to obtain the rates of Sensitivity C in the Cavern CAES model.

Before the increase, there was only one hour of production in the second production

period of Well P-1 that could not achieve the rate. As expected, if the permeability was

increased to 10,000 md, the reservoir had no problem obtaining the desired production

rates.

Obviously, a 100 md reservoir with 20% porosity is not reasonable for using the

same rates and effective pore volume that can be obtained in a cavern environment. If a

1,000 md reservoir can be used, then the Huntorf rates are reasonable since only one hour

of production with one well could not be reproduced. Even though the 10,000 md model

could produce all of the specified rates, a reservoir with a permeability of 10,000 md is

very rare. Therefore, if the same Sensitivity C Huntorf rates are to be used, then the

1,000 md model is the model of choice. With a working model of CAES in a cavern

setting and a reservoir setting using the same injection and production rates, a more

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practical application was necessary to look at actual reservoirs where CAES could be

implemented.

Based on reservoir characteristics of different areas of the GGRB, four reservoir

models could be constructed. The Moxa 1 and Baxter 1 models are the best case

scenarios for reservoir properties in their areas and the Moxa 2 and Baxter 2 models, are

less ideal candidates in terms of reservoir properties. Based on available injection data in

the GGRB, an initial injection rate of 100 MMscf/day was used in all of the models, with

a four-to-one production to injection ratio. After this model was run, an optimal rate

schedule was found based on the initial results and subsequent model runs. The Moxa 1

model could produce all of the production rates, but couldn’t handle the injection rates.

The second injection period was causing the problem, so this rate was reduced to 75

MMscf/day for the three-day time span. With this change, an optimal injection and

production schedule was realized for the Moxa 1 model; the production rate of 400

MMscf/day was left unchanged. The high porosity and permeability of the Baxter 1

model allowed it to obtain all of the initial injection and production rates of 100

MMscf/day and 400 MMscf/day, respectively. Since additional data verifying higher

rates could not be found within the GGRB, the optimal injection and production rates are

these initial rates. For the lower porosities and permeabilities of the Moxa 2 and Baxter 2

models, the initial production and injection schedule could not be obtained. The Moxa 2

model required an injection rate of 20 MMscf/day and a production rate of 80

MMscf/day. The Baxter 2 model, with a lower porosity, but a higher permeability, could

inject rates of 25 MMscf/day, but only produce rates of 100 MMscf/day.

By comparing the models of the GGRB, it can be seen that the models with higher

permeabilities could inject and produce higher rates than the models with lower

permeabilities. This is because permeability determines how effective a fluid will flow

through a formation and porosity determines how much storage space is available. For

example, both Baxter models could inject the desired rates, but the lower permeability

Moxa models had problems injecting the initial rates. Excluding the Baxter 2 model, the

155

higher permeability models could also produce better. The combination of porosity and

permeability with the Baxter 2 model led to its inconsistency.

A power analysis of the CAES components necessary for the GGRB models,

determined that the turbine power output based on modeled production rates is quite large

for the higher production rates and still beneficial for lower production rate values.

6.2 Model Comparisons

Some definite similarities can be found between the three model sets. The shape

of the pressure response is comparable for all of the models. This is because even though

all of the models do not use the same injection and production rates, the same rate

schedule is used throughout. Since periods of injection raise the pressure in the structure

and periods of production lower the pressure, the same high and low pressure regions are

emulated throughout the models. The pressure values themselves are different, but the

overall trend is the same.

As far as some of the differences between the models, in the Cavern CAES

models, a modification to pore volume gave inconclusive results, but with the 100 md

and 1,000 md Reservoir CAES models, an increase in pore volume helped to increase the

production rates. With the reservoir models, a larger pore volume meant more space in

which the injected air could migrate. This in turn gives the reservoir a better chance of

producing all of the desired production rates. Since some of the air will be trapped in

pore throats upon injection and cannot be produced, an increase in pore volume increases

the amount of pore space, allowing for more air to be produced. For the Cavern CAES

models, this issue of pore space is not a concern. When trying to achieve the initial

pressure match by changing the pore volume, a smaller pore volume had a better pressure

response during the initial periods of injection and a larger pore volume had the better

response during the later periods of production and injection. Since there is not one pore

156

volume that can improve in both periods, the production rate is better initially with the

smaller pore volumes, but then digresses over time. The opposite is true with the larger

pore volumes.

6.3 Model Conclusions

The major conclusions of modeling CAES in different geologic settings will be

presented in terms of importance. The most significant conclusions gained from the

study will be at the beginning of the list and the conclusions with lesser importance will

be towards the end of the list.

1. CAES could be used in actual reservoirs, depending on the geologic properties.

a. All four of the GGRB models demonstrated the ability to make use of

CAES. The rates that could be used with the lower porosity and

permeability Moxa 2 and Baxter 2 models were not as significant as the

higher porosity and permeability Moxa 1 and Baxter 1 models, but still

high enough to justify the implementation of CAES. However, the

potential for CAES can be maximized if reservoirs similar to the Moxa 1

and Baxter 1 models can be used.

b. Overall, the Baxter 2 model had the best results; this was the only model

able to inject and produce all of the 100 MMscf/day and 400 MMscf/day

respective rates. If studies can be conducted that show formations within

the GGRB can handle injection rates higher than 100 MMscf/day, then an

even greater power output could be realized with this model.

157

2. ECLIPSE 100© is an effective tool for modeling combined wind energy and

CAES.

a. Three different CAES models confirmed that ECLIPSE 100© can model

the CAES process independent of geologic setting. A successful pressure

match with the Cavern CAES model could be modified to represent a

hypothetical reservoir with the same injection and production rates,

schedule, and wells. Once successful model runs could be obtained with

this hypothetical reservoir, the use of a similar model in an actual reservoir

could be justified. Going through this series of steps confirms the validity

of using ECLIPSE 100© for the modeling of CAES.

3. The GGRB has good potential for a combined wind energy and CAES facility.

a. Not only are the results from modeling CAES in the GGRB basin

encouraging, but the abundant supply of wind energy makes the GGRB a

prime location for a combined wind energy and CAES facility. Foote

Creek Rim and the Medicine Bow Wind Project site are currently

providing wind power, and according to wind speeds at the Medicine Bow

Wind Project site and Figure 1.1, Class 4 to 6 wind speeds are present.

These speeds are more than adequate for making an economical wind

farm.

6.4 Recommendations for Future Work

The results from this study are a promising first step for implementing CAES in

porous media and the GGRB is a prime facility for continued research in this field. It has

abundant wind energy and some areas have the high porosities and permeabilities

necessary for successful CAES. However, a more intensive reservoir study needs to be

158

conducted to get a better understanding of the potential for combined wind and CAES in

this region. The list below shows the areas that should be considered for future research,

in terms of importance.

1. A single reservoir should be selected for study and all of the available reservoir

properties should be obtained.

a. A detailed reservoir characterization should be conducted. This should

include an examination of rock properties (porosity, permeability,

saturations, etc.), permeability barriers, such as shale, within the reservoir

unit, reservoir dimensions, natural fracturing, failure mechanisms, such as

stresses and strain of the reservoir rock, and leakoff from the reservoir,

more specifically, how well the reservoir is sealed. Failure mechanisms

are important to determine how high of pressures the formation can

handle, and in order for the pressurized air to stay within the reservoir,

seals need to be present around the entire reservoir.

b. A detailed reservoir simulation can then be conducted with the

information gained from the reservoir characterization. If the reservoir

has some residual water saturation, then the Honarpour or similar

correlations should be used in EZGEN to account for this situation. Once

all of the initial model inputs for the reservoir are entered into ECLIPSE

100©, different model runs can be conducted for a variety of scenarios.

c. A single well that is in charge of both injection and production could be

modeled as a means for cost reduction and possibly increased efficiency.

d. If the reservoir was in a depleted gas reservoir with multiple wells tapping

the reservoir, then injection and production can be modeled using these

existing wellbores. The well design and corrosion issues need to be

considered before implementing this option.

159

e. The use of horizontal wells to obtain higher rates should be modeled as

well, since they generally increase a well’s productivity index.

f. If an aquifer is attached to the reservoir this also needs to be addressed in

the study.

2. A more intensive analysis should be done with the combination of CAES and

wind energy.

a. If possible, wind data from the area where the reservoir is located should

be gathered and a more specific analysis should be made with the

compressor and turbine calculations. These calculations should consider

any energy losses that were found and the efficiencies of the compressor

and turbine to be used.

3. A cost analysis should be conducted for opening a combined wind and CAES

facility.

a. The cost of installing wind turbines, as well as, the cost of constructing a

CAES plant should be analyzed. An estimation of the cost per kW

produced, once the facility is up and running, should also be included.

6.5 Final Discussion

The results from the three models analyzed in this research, show that ECLIPSE

100© is an appropriate tool for modeling CAES in both cavern and reservoir

environments. First, a successful history match of the pressure response associated with

a daily injection and production schedule was obtained using known data in a cavern

environment. This model could then be modified to represent a reservoir environment.

After verifying the same production and injection schedule and rates with the reservoir

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model, a model could be created for a practical application in the GGRB. The results

from these model runs are very encouraging for combining the power of wind energy

with the reliability of CAES. All of the models could produce rates that would be

beneficial for additional power production during intermittent wind energy or peak

demand periods. With the appropriate geographical location and reservoir properties,

such as those evident in the GGRB, this type of energy source can become a profitable

commodity that can help meet the energy needs of future generations.

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NOMENCLATURE

CAES – Compressed air energy storageNREL – National Renewable Energy LabHAWT – Horizontal axis wind turbineGGRB – Greater Green River BasinSMES – Superconducting magnetic energy storageUTES – Underground thermal energy storageATES – Aquifer thermal energy storageDTES – Duct thermal energy storageHP – High pressureLP – Low pressureMBE – Material balance equationv = Fluid velocity

= The permeability tensor = Fluid viscosity

= The gradient of the potential functionh = the flow potential

FRP – Fiberglass reinforced plasticBw – Water formation volume factor

= Change in volume during the pressure reduction = Change in volume due to the reduction in temperature

Bg – Gas formation volume factorVR = Cubic foot of reservoir volumeVsc = Standard cubic foot of gasz = Gas-deviation factorT = Temperaturep = Pressurepr = Reduced pressureTr = Reduced temperaturepc = Critical pressureTc = Critical temperatureg = Gas viscosityMg = Gas molecular weight = PorosityVP = Pore volumeVB = Bulk volume

162

REFERENCES

Abou-Kassem, J. H., S. M. Farouq Ali, et al. (2006). Petroleum Reservoir Simulation. Houston, Gulf Publishing Company.

American Wind Energy Association. (1999). "Wyoming Wind Project Begins Powering Pacific Northwest." Retrieved August 2, 2006, from http://www.awea.org/news/wpa12.html.

Australian Greenhouse Office (2005). Advanced Electricity Storage Technologies Programme: Energy Storage Technologies: a review paper. Department of the Environment and Heritage, Commonwealth of Australia.

Benge, G. and E. G. Dew (2004). Meeting the Challenges of Design and Execution of Two High Rate Acid Gas Injection Wells. SPE/IADC Drilling Conference. Amsterdam, Netherlands.

Black, P. E. (2004). "Algorithms and Theory of Computation Handbook." "objective function", in Dictionary of Algorithms and Data Structures Retrieved November 13, 2006, from http://www.nist.gov/dads/HTML/objective.html

Blumer, D. J. (2006). Properties of Produced Water. SPE Petroleum Engineering Handbook. J. Fanchi, SPE.

Bullough, C., C. Gatzen, et al. (2004). Advanced Adiabatic Compressed Air Energy Storage for the Integration of Wind Energy. European Wind Energy Conference London, UK.

Bureau of Land Management. (2006). "Wyoming Wind Energy Project." Retrieved August 2, 2006, from http://www.wy.blm.gov/rfo/wind.htm.

Cape Wind. (2006). "Project at a Glance." Retrieved August 3, 2006, from http://www.capewind.org/article24.htm.

Cheung, K. Y. C., S. T. H. Cheung, et al. (2006). Large-Scale Energy Storage Systems. London, Imperial College.

Crotogino, F. (2006). Clarification of Huntorf Operations. J. Neumiller.

163

Crotogino, F., K.-U. Mohmeyer, et al. (2001). Huntorf CAES: More than 20 Years of Successful Operation. Orlando, Florida, U.S.A.

Danish Wind Industry Association. (2004). "Wind Energy Guided Tour." Retrieved June 11, 2006, from http://www.windpower.org.

DeJarnett, B. B., F. H. Lim, et al. (2003). Greater Green River Basin Production Improvement Project. U.S. Department of Energy's Federal Energy Technology Center. Fort Worth, TX.

Denholm, P., G. L. Kulcinski, et al. (2005). "Emissions and Energy Efficiency Assessment of Baseload Wind Energy Systems." Environmental Science Technology 39(6): 1903-1911.

EA Technology (2004). Review of Electrical Energy Storage Technologies and Systems and of Their Potential for the UK, DTI Technology Programme.

Fanchi, J. R. (2002). EZGEN - Generation of Flow Model Input.

Finn, T. M. (2005). Geothermal Gradient Map of the Southwestern Wyoming Province, Southwestern Wyoming, Northwestern Colorado, and Northeastern Utah. Petroleum Systems and Geologic Assessment of Oil and Gas in the Southwestern Wyoming Province, Wyoming, Colorado, and Utah. Denver, U.S. Geological Survey.

Flores, R. M. and L. R. Bader (1999). Fort Union Coal in the Greater Green River Basin, East Flank of the Rock Springs Uplift, Wyoming: A Synthesis. U.S. Geological Survey Professional Paper 1625-A, U.S. Geological Survey.

Forest Products Laboratory. (2004). "TechLine: Fuel Value Calculator." Retrieved November 10, 2006, from http://www.fpl.fs.fed.us/documnts/techline/fuel-value-calculator.pdf.

Gibson, R. I. (1997). "Greater Green River Basin." Retrieved October 16, 2006, from http://www.gravmag.com/grnriv.htm.

Gipe, P. (1995). Wind Energy Comes of Age. New York, John Wiley & Sons, Inc.

Gonzalez, A., B. O Gallachoir, et al. (2004). Study of Electricity Storage Technologies and Their Potential to Address Wind Energy Intermittency in Ireland. Rockmount Capital

164

Partners. Cork, Ireland, Sustainable Energy Research Group, Department of Civil and Environmental Engineering, University College Cork.

Greenblatt, J. B., S. Succar, et al. (2006). Baseload wind energy: Modeling the competition between gas turbines and compressed air energy storage for supplemental generation. Energy Policy.

Holst, K. (2005). The Iowa Stored Energy Plant: A Project Review and Update.

Katz, D.L., and M.R. Tek (1981). “Overview on Underground Storage of Natural Gas.” Journal of Petroleum Technology: 943-951.

Kirschbaum, M. A. and L. N. R. Roberts (2005). Geologic Assessment of Undiscovered Oil and Gas Resources in the Mowry Composite Total Petroleum System, Southwestern Wyoming Province, Wyoming, Colorado, and Utah. Petroleum Systems and Geologic Assessment of Oil and Gas in the Southwestern Wyoming Province, Wyoming, Colorado, and Utah. Denver, U.S. Geological Survey.

Makansi, J. (2001). "Energy Storage: The Sixth Dimension of the Electricity Production and Delivery Value Chain." Retrieved June 10, 2006, from http://www.energystoragecouncil.org/1%20-%20Jason%20Makansi-ESC.pdf.

Makansi, J. (2001). "Energy Storage: The sixth-and-missing link in the electricity value chain." Global Energy Business July/August 2001.

Manwell, J. F., J. G. McGowan, et al. (2002). Wind Energy Explained: Theory, Design, and Application. West Sussex, England, John Wiley & Sons Ltd.

McCain Jr., W. D. (1990). The Properties of Petroleum Fluids. Tulsa, OK, PennWell Publishing Company.

Miskimins, J. L. (2000). Characterization of Present-Day Stress States Near Faults, North LaBarge Field, Sublette County, Wyoming. Petroleum Engineering. Golden, Colorado School of Mines. Master of Science.

National Energy Technology Laboratory. (2004). "Transmission, Distribution, & Refining: Natural Gas Storage." Retrieved October 20, 2006, from http://www.netl.doe.gov/technologies/oil-gas/TDR/Storage/Storage.html.

National Renewable Energy Laboratory. (2000). "Wind Resource." Retrieved May 30, 2006, from http://www.nrel.gov/wind/wind_map.html.

165

NaturalGas.org. (2004). "Storage of Natural Gas." Retrieved October 13, 2006, from http://www.naturalgas.org/naturalgas/storage.asp.

NEG Micon North America. (2004). "Foote Creek 3." Retrieved August 2, 2006, from http://www.awea.org/projects/summaries/FooteCreek3.pdf.

Oil and Gas Investor (2005). "Going Gangbusters." Tight Gas: A Supplement of Oil and Gas Investor.

Paksoy, H. O. (2005). Underground Thermal Energy Storage - A Choice for Sustainable Future. Adana, Turkey, Cukurova University.

Patel, M. R. (2006). Wind and Solar Power Systems. Kings Point, New York, U.S.A, Taylor & Francis.

Platte River Power Authority. (2006). "Monthly Wind Speed and Performance Data 2004." Retrieved August 2, 2006, from http://www.prpa.org/energysources/windspeedperform04.htm.

Research Reports International (2004). Energy Storage Technologies For Electric Power Applications.

Ridge Energy Storage & Grid Services L.P. (2005). The Economic Impact of CAES on Wind in TX, OK, and NM, Texas State Energy Conservation Office.

Schilthuis, R. J. (1936). "Active Oil and Reservoir Energy." AIME 118: 33-37.

Schlumberger (2004). ECLIPSE Reference Manual.

Schlumberger (2004). ECLIPSE Technical Description.

Sonntag, R. E., C. Borgnakke, et al. (1998). Fundamentals of Thermodynamics. New York, John Wiley & Sons, Inc.

State of Texas (2006). New Electric Generating Plants in Texas Since 1995.

Thomas, G. W. (1982). Principles of Hydrocarbon Reservoir Simulation. Boston, International Human Resources Development Corporation.

166

Towler, B. F. (2006). Gas Properties. SPE Petroleum Engineering Handbook. J. Fanchi and Lake, SPE.

Wyoming Oil and Gas Commission. (2006). "Baxter Basin South Unit 23 Well File." Retrieved October 30, 2006, from http://wogcc.state.wy.us/Wellapino.cfm?napino=375562.

Wyoming Oil and Gas Commission. (2006). "Tip Top Unit T57X-27G Well Files." Retrieved October 25, 2006, from http://wogcc.state.wy.us/Wellapino.cfm?napino=3520807

167