Flyers Lab & Capability · characterization has been the scanning electron microscope (SEM) as well as focused ion beam (fib) slicing combined with SEM. Images from various locations

Nancy Taylor, Contracts & Assistant Business Manager

EMAIL: [email protected] PHONE: (801) 585-9137

egi.utah.edu | EGI ... the science to find energy | [email protected]

CompletedImmediate Delivery

Principal Investigators:

Milind Deo, Ph.D.EGI Affiliate Scientist, Professor, Chemical Engineering

Email: [email protected]

Tom Anderson, M.Sc.Geologic Lead, Senior Advisor to the Director

Email: [email protected]

April 25, 2014 10:14 AM

Project I 00973_2

Energy & Geoscience Institute

Principal Investigator: Dr. Milind Deo

Geologic Lead: Mr. Tom Anderson

Liquids from ShalesReservoir Description & Dynamics Phase 2

I 00973_2

Niobrara

Eagle Ford

Eagle Ford

Sponsor Cost $40k (USD)

Sponsors Receive:

• Digital Report

• Matrix Permeability Determination

• On-site Presentation

http://egi.utah.edu/about/affiliate/milind-deo.php




http://egi.utah.edu/about/staff/tom-anderson.php



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Available for Immediate Delivery | Liquids from Shales Phase 2: Reservoir Description & Dynamics | I 00973_2

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Executive SummaryThe production of liquids from shales has revolutionized the oil industry. The Eagle Ford oil and condensate production together averaged about 845,000 barrels/day from October to December 2013. In October 2013, the Bakken oil production in north Dakota averaged over 875,000 barrels per day. Similarly, the liquids production from the Permian basin shales is also growing at a rapid pace. This level of growth in the production of liquids from shales requires a close examination of all aspects of geologic, production, and operational controls on recovery.

In this second phase of research, we continued in our comprehensive quest at understanding all of the components that contribute to optimum exploitation of shales for liquids. These included geologic considerations, geomechanical modeling, reservoir engineering evaluations, and environmental aspects. Rapid permitting and drilling have led to the belief that shales are basically statistical plays, and that geologic characterization and evaluation have little bearing on economic development. However, we do know that geologic characterization at various scales is important in establishing producibility and optimum recovery.

Sections 2–5 of the report deal with the geologic evaluations. Section 2 clearly outlines the geologic examinations undertaken in this study. Detailed characterization of the Niobrara is discussed in Section 3, with an overall attempt to establish the role of geology on production. Of all the properties studied, fracture intensity emerged as the property with the strongest correlation to production.

Section 4 examines pore-level characterization of mainly the Niobrara. The goal was to identify the different varieties of pore types found in the Niobrara and compare them to available classifications for carbonates and shales. Images of the Niobrara chalks and marls and other characterization reveal the incredible complexity of the shales and indicate storage in kerogen and inter- and intra-crystalline pores. Analyses of shales using the different techniques and tools add to our understanding of porosity, permeability, and mechanical properties of shales.

Section 5 provides a detailed workflow of shale characterization at the pore scale. There are still unanswered questions concerning both storage of liquids in shales and permeability variations; the characterization of shales at the pore scale is considered important. The tool of choice for pore scale characterization has been the scanning electron microscope (SEM) as well as focused ion beam (fib) slicing combined with SEM. Images from various locations in the Bakken show the variations in the characteristics of the shales and provide comparisons with the findings for the Niobrara.

Sections 6 –11 deal with the various engineering operations. The material balance methodology developed in this research for liquids being produced out of tight rocks is detailed in Section 6. This methodology requires production data and fluids characterization. Using the data and the method developed, it is possible to obtain pressure profiles and saturation information, and to estimate reservoir permeability prior to interference with another hydraulic fracture. The development of this method for all types of reservoir fluids, and associated data analysis technique, were significant achievements in this project. The analytical tool for matrix permeability determination is available to all sponsors.

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Section 7 builds on the mechanistic studies conducted in liquids from shales, phase 1 and follows that with combinatorial studies based on experimental design. We conducted two studies – one for black-oils and the other with condensates. The main factors that affect production are detailed in table 1.

The importance of factors affecting recovery (1, 10, 20 years, using 5 stb/d (stock tank barrels/day) are listed in order of importance Important project results illustrate recovery distributions for uncertainties for all important input parameters. The algebraic equations for the surrogate models used in the Monte Carlo simulations have been made available to the sponsors. Companies will be able to input their own data in the models to generate PDFs relevant to their systems. Similar results are available for the condensates. The order of factors for condensates differ only slightly when compared to black-oils.

Section 8 details a new technique EGI has developed for incorporating data for model refinement, identification, and parameter estimation. The ensemble Kalman filter technique has been used in inverse modeling in reservoir engineering. We show a variation of the method to identify fracture orientations and properties. This technique will be useful when constructing accurate representations of hydraulic fractures when production data and some additional information such as microseismic and production logs are available.

Section 9 deals with recovery of injected water as a function of wettability of the formation. It is known that only about 30% of the water injected during hydraulic fracturing is recovered. The recoveries computed in our simulations using complex fracture geometries range from about 25% to 45% depending on the formation wettability. As the wettability of the matrix changes from being water-wet to oil-wet, recovery increases by a significant amount. This study shows that it is quite likely that the injected water may be retained in the matrix and is trapped as immovable water saturation. The complex-fracture simulations in this Section were performed using advanced reactive transport simulator (ARTS), a University of Utah-developed modular reservoir simulator tool.

We know that decisions made regarding surface equipment and conditions affect subsurface operations and vice-versa. Rapid deployment of resources for liquid shale developments may not always result in optimal choices. This is highlighted in Section 10, which assesses the impact of using specific types of surface equipment on oil quality as well as the amount of gas lost in separators. The current practice in most locations is to use single-stage separation, our research indicates there are better practices.

Section 11 shows how detailed mineralogical information can be used to predict the growth of hydraulic fractures – single fractures or fractures in clusters. Idaho National Laboratory, our partner in this effort performed these simulations. Discrete element modeling was used to generate the results. The simulations reveal factors that confine the propagation of fractures and we suggest injection rate details. Multi-perforation clusters are constrained by a unique set of fracture parameters. Results can be compared with available microseismic data to validate a hydraulic fracture network in the stimulated volume.

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Future ResearchThis Phase 2 project highlighted the need to understand the storage, thermodynamics, and flow in complex nanoporous media and a need to develop optimization production and recovery schemes given the geologic conditions. We have seen that recoveries are about 10% OOIP even with close fracture spacing. It is important to consider strategies for increasing these recoveries. We have two projects in development to address these very issues.

1. Measurement of Storage and Flow in Nanostructured Materials as Proxy for Natural Shale Systems (EGI # 01072)

2. Improved Liquid Recovery in Shales, Optimization for Field Development (EGI # 01073)

Research team

Staff Expertise/Affiliation

Milind Deo, Ph.D. Principal Investigator

EGI Affiliate Scientist; Professor & Chair, Chemical Engineering, University of Utah

Tom Anderson, M.Sc. Geologic Lead EGI Senior Advisor & Research Scientist

Raul Velasco, Ph.D. Candidate Department of Chemical Engineering, University of Utah

Palash Panja, Ph.D. Candidate Department of Chemical Engineering, University of Utah

Jing Zhou, Ph.D. Candidate Department of Chemical Engineering, University of Utah

Hongtao Jia, M.Sc. Candidate Department of Chemical Engineering, University of Utah

Carrie Welker, M.Sc. Candidate Department of Geology & Geophysics, University of Utah

Peter Pahnke, M.Sc. Candidate Department of Geological Sciences, Brigham Young University

Hai Huang, Senior Scientist Idaho National Laboratory

Richard Roehner, Associate Professor (Lecturer)

Department of Chemical Engineering, University of Utah

http://egi.utah.edu/corporate-associates/current-projects/I01072.php



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Phase 2 Sponsors

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Table of ContentsExecutive Summary .......................................................................................................................................... 1Section 2 Geological Overview........................................................................................................................ 7

2.1 Geological Overview......................................................................................................................................... 82.2 Niobrara Formation, Silo Field Study ............................................................................................................ 92.3 Pore Type Classification – Denver-Julesburg Basin ................................................................................... 102.4 Fluid flow in nano-pore throats using multi-resolution micro-imaging .............................................. 122.5 Source-rock geochemistry of the Niobrara in the Denver Basin ............................................................ 162.6 References ........................................................................................................................................................ 19

Section 3 Geological Controls on Oil Production from the Niobrara Formation at Silo Field Laramie County, Wyoming ...................................................................... 21

3.1 Introduction ..................................................................................................................................................... 223.1.1 Motivation for Study .............................................................................................................................. 223.1.2 Scope of Work ........................................................................................................................................... 233.1.3 Geologic Setting ...................................................................................................................................... 233.1.4 Silo Field History ...................................................................................................................................... 29

3.2 Previous Studies .............................................................................................................................................. 323.2.1 Resistivity Mapping ................................................................................................................................ 323.2.2 Natural Fractures ..................................................................................................................................... 32

3.3 Objectives ......................................................................................................................................................... 333.4 Data and Methods ........................................................................................................................................... 343.5 Results ............................................................................................................................................................... 35

3.5.1 Cross Sections ........................................................................................................................................... 353.5.2 Core Description ...................................................................................................................................... 373.5.3 Core – Log Calibration ........................................................................................................................... 41

3.5.3.1 Porosity ............................................................................................................................................413.5.3.2 Weight percent calcite ..................................................................................................................423.5.3.3 Fracture intensity ...........................................................................................................................423.5.3.4 Total organic carbon (TOC) ........................................................................................................44

3.5.4 Determining Geologic Controls – Bivariate Analysis .................................................................. 483.6 Discussion ......................................................................................................................................................... 51

3.6.1 Limits of Resistivity Mapping to Predict Sweet Spots ................................................................ 513.6.2 Possible Application of the Δ log R Technique to Estimate Oil in Place .............................. 523.6.3 Tectonic Control on Fracture Intensity and Production ............................................................ 53

3.7 Conclusions ...................................................................................................................................................... 563.8 References ........................................................................................................................................................ 573.9 Appendix A ....................................................................................................................................................... 59

Section 4 Carbonate Mudrock Microporosity Classification and Characterization: Upper Cretaceous Niobrara Formation, Denver-Julesburg Basin, Colorado and Wyoming .................................................... 63

4.1 Introduction ..................................................................................................................................................... 644.2 Geologic Background ..................................................................................................................................... 644.3 Objectives ......................................................................................................................................................... 674.4 Data and Methods ........................................................................................................................................... 67

4.4.1 Thin Section Petrography ..................................................................................................................... 68

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4.4.2 Quantitative Evaluation of Minerals Using Scanning Electron Microscopy ....................... 694.4.3 Ion Milling .................................................................................................................................................. 704.4.4 SEM Analyses ............................................................................................................................................ 714.4.5 MAPSTM ........................................................................................................................................................ 72

4.5 Results ............................................................................................................................................................... 734.5.1 Interparticle Pores ................................................................................................................................... 744.5.2 Intraparticle Pores ................................................................................................................................... 754.5.3 Organic Matter Pores ............................................................................................................................. 764.5.4 Fracture Pores ........................................................................................................................................... 774.5.5 Comparison ............................................................................................................................................... 78

4.6 References ........................................................................................................................................................ 78Section 5 Quantifying, Connecting and Evaluating Shale Properties at Multiple Scales: ........................ 79Multi-Resolution Micro-Imaging of the Bakken and Niobrara ................................................................... 79

5.1 Introduction to Workflow Techniques ......................................................................................................... 805.2 Elemental Topography ................................................................................................................................... 82

5.2.1 X-ray Computed Tomography (CT) ................................................................................................... 825.2.2 XRF digital mapping ............................................................................................................................... 82

5.3 Petrology ........................................................................................................................................................... 835.3.1 Thin Section Petrography ..................................................................................................................... 835.3.2 X-ray Diffraction (XRD) ........................................................................................................................... 845.3.3 QEMSCAN® ................................................................................................................................................. 86

5.4 Microscopy ....................................................................................................................................................... 885.4.1 Ar-Ion Milling ............................................................................................................................................ 885.4.2 Scanning Electron Microscope (SEM) .............................................................................................. 91

5.5 Multi-Resolution Micro-Imaging of the Bakken and Niobrara: Scope of Work ........................................................................................................................................................... 915.6 Multi-Resolution Micro-Imaging Results: Bakken..................................................................................... 93

5.6.1 Thin Section Petrography ..................................................................................................................... 935.6.2 X-ray diffraction (XRD) ........................................................................................................................... 975.6.3 QEMSCAN® Analyses Results ............................................................................................................... 995.6.4 Sample BK001 ........................................................................................................................................... 995.6.5 Sample BK002 ........................................................................................................................................... 995.6.6 Sample BK003 ........................................................................................................................................... 995.6.7 Sample BK004 .........................................................................................................................................1005.6.8 Sample BK005 .........................................................................................................................................1005.6.9 SEM Analyses Results ...........................................................................................................................107

5.7 Multi-Resolution Micro-Imaging Interpretation: Bakken ......................................................................1205.8 Multi-Resolution Micro-Imaging Results: Niobrara Formation ...........................................................121

5.8.1 X-ray Computed Tomography (CT) .................................................................................................1215.8.2 Thin Section Petrography Analyses ................................................................................................1225.8.3 QEMSCAN® Analyses ............................................................................................................................1225.8.4 Scanning Electron Microscope (SEM) Analyses ..........................................................................123

5.9 Multi-Resolution Micro-Imaging Interpretation: Niobrara Formation ...............................................1235.10 Multi-Resolution Micro-Imaging Interpretation: Summary ...............................................................128

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5.10.1 Future Work ...........................................................................................................................................1285.11 References ....................................................................................................................................................1295.12 Appendix .....................................................................................................................................................130

Section 6 Material Balance Calculations .................................................................................................... 1356.1 Introduction and Synopsis ..........................................................................................................................1366.2 The Method ....................................................................................................................................................137

6.2.1 Case 1: Black-Oil Linear Flow into a Fracture ...............................................................................1386.2.2 Case 2: Black-oil (high initial dissolved gas oil ratio Rsi) Linear Flow into a Fracture ....1416.2.3 Case 3: Gas Linear Flow into a Fracture .........................................................................................142

6.3 Corrected Average Pressure Method .........................................................................................................1446.3.1 Case 2: Black-oil (high Rsi) Linear Flow into a Fracture Recomputed Using the New Formulation to Account for Compressibility ............................................................................................145

6.4 Other important calculations......................................................................................................................1476.5 Implementation into software ....................................................................................................................1506.6 APPENDIX A ....................................................................................................................................................1506.7 Appendix B .....................................................................................................................................................1506.8 References ......................................................................................................................................................1546.9 Nomenclature ................................................................................................................................................155

Section 7 Factors that Control Black-Oil and Condensate Production from Shales – Surrogate Reservoir Models and Uncertainty Analysis ............................................................................................................... 157

7.1 Introduction ...................................................................................................................................................1587.2 Methodology for Black-oil ...........................................................................................................................159

7.2.1 Reservoir Model for Black-oil ............................................................................................................1597.2.2 Input Parameters ...................................................................................................................................159

7.3 Experimental Design For Black-oil .............................................................................................................1597.3.1 Regression Model ..................................................................................................................................160

7.3.1.1 Model accuracy........................................................................................................................... 1607.3.1.2 The Coefficient of Determination (R²) ................................................................................... 1617.3.1.3 Normalized Root Mean Square Error (NRMSE) ................................................................... 161

7.3.2 Workflow ...................................................................................................................................................1627.4 Simulation Results for Black-oil ..................................................................................................................163

7.4.1 Oil Recovery ............................................................................................................................................1637.4.2 Gas Recovery ...........................................................................................................................................164

7.5 Validation of the Surrogate Model .............................................................................................................1647.6 Uncertainty Analysis .....................................................................................................................................166

7.6.1 Input for Uncertainty Analysis ..........................................................................................................1677.6.2 Uncertainty in Oil Recovery ...............................................................................................................1687.6.3 Uncertainty in Gas Recovery .............................................................................................................170

7.7 Summary and Conclusions for oil production .........................................................................................1717.8 Methodology for Condensates ...................................................................................................................172

7.8.1 Reservoir Model .....................................................................................................................................1727.8.2 Input Factors ...........................................................................................................................................1737.8.3 Reservoir Fluids ......................................................................................................................................174

7.9 Simulation Results for condensates...........................................................................................................1757.9.1 Condensate Recovery ..........................................................................................................................176

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7.9.2 Gas Recovery ..........................................................................................................................................1767.9.3 Condensate to Gas Ratio (CGR) .......................................................................................................1777.9.4 Input for Uncertainty Analysis ..........................................................................................................1787.9.5 Uncertainty in Condensate Recovery ...........................................................................................1797.9.6 Uncertainty in Gas Recovery .............................................................................................................181

7.10 Summary and Conclusions for condensate production ......................................................................1827.11 Acknowledgements ....................................................................................................................................1827.12 Nomenclature ..............................................................................................................................................1837.13 References ....................................................................................................................................................184

Section 8 Incorporating Data for Improving the Input Parameters and Model Predictions .................. 1878.1 Introduction ...................................................................................................................................................1888.2 Problem statement .......................................................................................................................................189

8.2.1 The Ensemble Kalman Filter Implementation .............................................................................1908.3 modified implementation ...........................................................................................................................1928.4 Illustrative Examples .....................................................................................................................................194

8.4.1 Simple Model with Faults ...................................................................................................................1948.4.1.1 Reservoir model with accurate knowledge of fractures.................................................... 1958.4.1.2 Reservoir model without accurate knowledge of fracture locations ............................. 200

8.5 Injection only Scenario .................................................................................................................................2058.6 PUNQ-S3 Case – SPE Comparative study ..................................................................................................2078.7 Conclusions ....................................................................................................................................................2148.8 References ......................................................................................................................................................214

Section 9 Water Balance Study .................................................................................................................... 2179.1 Introduction ...................................................................................................................................................2189.2 Technical Approach .......................................................................................................................................2199.3 Case Studies ...................................................................................................................................................222

9.3.1 Case 1: Domain with Orthogonal Fractures .................................................................................2229.3.2 Case 2: Domain with Non-Orthogonal Fractures ......................................................................224

9.4 Conclusions ....................................................................................................................................................2279.5 References ......................................................................................................................................................227

Section 10 Design of Surface Facilities ....................................................................................................... 22910.1 Introduction .................................................................................................................................................23010.2 Background ..................................................................................................................................................23010.3 Evaluations ...................................................................................................................................................23210.4 Conclusions ..................................................................................................................................................24110.5 Work to be done – Improved Liquid Recovery in Shales project .......................................................24110.6 Acknowledgement .....................................................................................................................................24210.7 References ....................................................................................................................................................242

Section 11 Coupled Discrete Element Model and Network Flow Model for Hydraulic Fracturing Simulations in Shale ..................................................................................................................................... 243

11.1 DISCRETE ELEMENT MODEL (DEM) FOR FRACTURING SIMULATIONS ..............................................24411.2 NETWORK FLOW MODEL AND COUPLING WITH DEM FOR HYDRAULIC FRACTURING..................24511.3 SIMULATION RESULTS OF HYDRAULIC FRACTURING PROPAGATIONS UNDER VARIOUS CONDITIONS ...........................................................................................................................................................247

11.3.1 Hydraulic Fracture Propagations Under Different Confining Stress States ..................24711.3.2 Effects Of Injection Rates .................................................................................................................251

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11.3.3 Effects Of Fluid Viscosity ...................................................................................................................25111.3.4 Effects Of Mechanical And Hydraulic Heterogeneities .........................................................25211.3.5 Hydraulic Fracture Propagations from Horizontal Wellbore with Multiple Perforations .253

11.4 Discussion and conclusive remarks .........................................................................................................257

List of FiguresFigure 2.1 Target shale plays in Phase 1 (red) and Phase 2 (blue). Modified from EGI report I 00973. .................... 8Figure 2.2 One system for pore type classification (adapted from Loucks, 2012). .......................................................11Figure 2.3 Index map showing core and outcrop sample collection locations. Modified from EGI report I

00973. ...............................................................................................................................................................................12Figure 2.4 Example of a nanopore (Slatt and O’Brien, 2011). ...............................................................................................13Figure 2.5 Sizes of typical molecules versus pore throats (Nelson, Philip H., 2009). ....................................................14Figure 2.6 Simulation model of total apparent permeability versus Darcy permeability and versus Knudsen

Diffusion (EGI report I 00973). .................................................................................................................................14Figure 2.7 Creation of 3D volumes of shale using FIB technology coupled with Ar-ion milling. ............................15Figure 2.8 Location for Bakken black shale core samples. .....................................................................................................15Figure 2.9 Thermal gradient map of the Denver-Julesburg Basin (Thul, 2012). ............................................................16Figure 2.10 Maturity and depth in the Denver Basin (Thul, 2012). .....................................................................................17Figure 2.11 Regional maturity zones in the Denver Basin (Thul, 2012). ...........................................................................18Figure 3.1 Location of the Denver Basin with the locations of Silo, Hereford, and Wattenberg fields highlighted

in green (oil prone) and red (gas prone). .............................................................................................................23Figure 3.2 Generalized area of the Western Interior Cretaceous Basin during Niobrara deposition. ...................24Figure 3.3 Depositional model for sediments deposited in the WIC seaway. ...............................................................25Figure 3.4 Diagrammatic E-W cross section of the WIC foreland basin. ..........................................................................25Figure 3.5 Image on left from Sonnenberg (2012) after Longman et al. (1998) and Barlow (1985). On the right

is a type log of the Niobrara Formation at Silo Field. .....................................................................................27Figure 3.6 Chalk, marl and limestone of the Niobrara Formation at Silo Field have different petrophysical

properties as illustrated by this cross plot of Gamma Ray (GR) versus Deep Resistivity (ILD) for the Lee 41-5 well. ..................................................................................................................................................................... 28

Figure 3.7 Generalized east-west cross section through the Denver Basin. ..................................................................29Figure 3.8 Silo Field, Laramie County, Wyoming and the four township area, T15-16N, R64-65W, used in this

study. Overlain on this map are well locations colored according to drilling era and sized by first year oil (MBBLS). Wrench fault model after Sonnenberg and Weimer (1993). .....................................30

Figure 3.9 Cumulative production of oil, gas, and water from 1983 through present for Silo Field 3.9a (Top). Also shown is the number of producing wells through time. Oil, gas, and water production history from the Niobrara at Silo Field from 1983 to present 3.9b (Bottom). Also shown is the number of producing wells through time. ........................................................................................................31

Figure 3.10 NW-SE left-lateral wrench fault interpreted in Silo Field (Sonnenberg and Weimer, 1993) and proposed fracture model (Sonnenberg and Weimer, 1993, from du Rouchet, 1981). This model incorporates the presence of vertical stylolites, extension fractures, and wrench faults. .................33

Figure 3.11 Workflow elements .......................................................................................................................................................35Figure 3.12 Large format cross sections are included in Section 3 Appendix A (Panels A-E). .................................36

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Figure 3.13 Lee 41-5 well core description. Cored interval covers around 37 ft. of the Niobrara B chalk bench. See Figure 3.15 for stratigraphic context of cored thickness. NB003R and NB004R indicate sample locations analyzed in Section 4 of this report. ..................................................................................................37

Figure 3.14 Combs 1 well, core description. Cored interval covers around 40 ft. of the Niobrara B chalk bench. NB001R and NB002R indicate sample locations analyzed in Section 4 of this report. .......................38

Figure 3.15 Lee 41-5 and Combs 1 core photographs illustrating key features (A – D). Both cores are gray to dark gray, chalk and marl, and exhibit calcite-filled hairline fractures, none to moderate bioturbation, horizontal and vertical stylolites, Inoceramid and oyster fossils, and pyrite laminae and nodules. ..................................................................................................................................................................39

Figure 3.16 Entire Niobrara Formation (~300 ft.) in the Lee 41-5 well. ............................................................................40Figure 3.17 Relationship between GR and XRD measurements of weight % calcite for both cores. ....................42Figure 3.18 Example of quantifying fracture intensities using FILs, Lee 41-5 well. ......................................................43Figure 3.19 Initial results of applying the Δ Log R method of modeling TOC for the Niobrara Formation in the

Lee 41-5 well. ................................................................................................................................................................45Figure 3.20 Core descriptions, well log signatures, core measurements (discrete points), and calculated

curves for the Lee 41-5 (top) and the Combs 1 (bottom) wells (Panels A – B). Both cored intervals are taken from the lower B chalk bench. ............................................................................................................46

Figure 3.21 Bivariate analysis of geologic variables and first year production. Contoured maps show spatial variability of a single geologic variable in the Lower B chalk interval at Silo Field (Panels A-D). ..50

Figure 3.22 Fracture Intensity versus Deep Resistivity for both the lower B chalk and B marl intervals of the Niobrara Formation. Note the clear separation in resistivity between the B marl and Lower B chalk supports that a resistivity cutoff of ~30 ohm-m or greater characterizes reservoir rocks. Resistivity does seem to correlate to fracture intensity in the Lower B chalk with the exception of one outlier. 51

Figure 3.23 The Δ log R method of modeling TOC may be useful to indicate oil in place in Niobrara reservoir rocks. .................................................................................................................................................................................52

Figure 3.24 Locations of wells with fracture intensities calculated from FID logs. Average fracture intensity throughout the lower B chalk interval was compared to the distance each well was to the nearest NW-SE fault and to the center NW-SE fault/salt dissolution edge. ...........................................................53

Figure 3.25 (A) Average fracture intensity of the Lower B chalk versus the distance each well is located from the nearest NW-SE trending fault. (B) Average fracture intensity of the Lower B chalk versus the distance each well is located from the central NW-SE trending fault. ......................................................54

Figure 3.26 Average fracture intensity of the lower B chalk versus Distance from the center fault/dissolution edge (A), and first year oil versus Distance from the center fault/dissolution edge (B). ...................55

Figure 3.27 (A) Average fracture intensity of the Lower B chalk versus Distance from the center fault/salt edge and (B) first year oil versus Distance from the center fault/salt edge for wells in fault block 3 (Figure 3.24). Yes, fracture intensity and production increase with proximity to the central fault/salt edge but there is not a direct correspondence to fracture intensity with production. This is another apt example in which considering completion practices and incorporating multivariate analysis may clarify why wells are outliers to the general trend. ................................................................56

Figure 4.1 The Western Interior Cretaceous Basin during Niobrara deposition. Source area for clastic sediments is dominantly to the west and is related to Sevier thrusting. Total organic content (TOC) increases to the east where carbonate content is generally higher. Figure modified from Sonnenberg (2011), after Longman, et al., (1998) and Kauffman, (1977). .........................................................................65

Figure 4.2 Stratigraphic column of the Niobrara Formation at Six Mile Fold, north of Boulder, Colorado illustrating the Fort Hayes Limestone and the chalk/marl sequences of the Smoky Hill Member. The column on the right zooms in on the B interval showing additional detail. Figure modified from Longman et al., (1998) and Gustason and Deacon (2010)..................................................................66

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Figure 4.3 Index map of Colorado, Wyoming, Nebraska and Kansas showing areas of Niobrara oil and gas production and sample collection locations for sample images displayed in this section of the report. Figure modified from Williams and Lyle, (2011). ................................................................................67

Figure 4.4 Photomicrographs displaying typical differences between Chalks (top) and Marls (bottom) and transmitted (left) versus UV (right) light sources. Scale is the same for all photomicrographs. ......68

Figure 4.5 QEMSCAN® image displaying typical Niobrara compositional variability and heterogeneity. Oval shapes are squashed fecal pellets made up of coccolith and nanolith fragments. Also visible is a calcite filled fracture. ...................................................................................................................................................69

Figure 4.6 Example of secondary Electron (top) and Back Scatter Electron (BSE) SEM (bottom) images displaying the difference between non-Ar-ion milled samples (left) and Ar-ion milled samples (right). Photos by Hitachi, 2012...............................................................................................................................70

Figure 4.7 BSE SEM image displaying the similar compositional variability and heterogeneity seen by typical QEMSCAN® analyses. Oval shapes are squashed fecal pellets made up of coccolith and nanolith fragments. .......................................................................................................................................................................71

Figure 4.8 Stitched MAPS™ mosaic made up of 25 high resolution, high magnification BSE SEM images. White circular shapes are pyrite framboids, light grey ovular shaped areas are squashed fecal pellets made up of coccolith and nanolith fragments, medium grey areas are siliceous detrital grains and clay floccules, dark grey areas are organic matter (some with OM pores) and black areas are pores. 72

Figure 4.9 The mudrock pore classification ternary diagram (modified from Loucks et al. (2012). By qualitative analysis of SEM results, the Niobrara B Chalk plots as a mixed-pore type network with strong interparticle influence and the Niobrara B Marl plots as an organic matter pore network also with strong interparticle influence. .................................................................................................................................73

Figure 4.10 Examples of interparticle pores within the Niobrara. .....................................................................................74Figure 4.11 Examples of intraparticle pores within the Niobrara. .....................................................................................75Figure 4.12 Example of organic matter pores within the B Chalk interval of the Niobrara (NB010R). ..................76Figure 4.13 Example of organic matter pores within the B Marl interval of the Niobrara (NB003R)......................76Figure 4.14 Example of fracture pores within the B Chalk interval of the Niobrara (NB010R). ................................77Figure 4.15 Example of fracture pores within the B Marl interval of the Niobrara (NB009R). .................................77Figure 5.1 Summary of methods. ...................................................................................................................................................80Figure 5.2 Revised workflow showing development of hardware, software, and preparation techniques for

shale analyses. ..............................................................................................................................................................81Figure 5.3 Schematic representation of XRF elemental (Al, Ca, Fe and Si) analyses and composite analyses of

these elements into an XRF map. ...........................................................................................................................83Figure 5.4 Schematic representation of QEMSCAN® analyses showing mineralogy and micro-depositional

features. Image A shows a low-resolution image of the sample, B is a high-resolution scan of the sample, indicated by a red box on image A. ......................................................................................................87

Figure 5.5 SEM images highlighting the differences between un-milled and Ar-ion milled samples. Un-milled images are on the left, milled on the right. Images courtesy of Hitachi (IM4000ion mill). SE indicates Secondary Electrons; BSE indicates Back Scattered Electrons. .................................................89

Figure 5.6 Secondary Electron (Everhart Thornley Detector) SEM image highlighting ion milling through a shale sample from the Bakken (BK003). The image emphasizes the areas showing ion milling and the way in which milling occurs surrounding crystal grains. .......................................................................90

Figure 5.7 Sample locations for the five Bakken (BK001, BK002, BK003, BK004 and BK005) samples analyzed. ...92

Figure 5.8 Petrographical thin section analyses of Bakken samples (BK001, BK002 and BK003). Photomicrographs show representative mineralogy and textures from samples with specific features highlighted. Inset D is noted as ~200 µm in width. .......................................................................94

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Figure 5.9 Petrographical thin section analyses of Bakken samples (BK004 and BK005). Photomicrographs show representative mineralogy and textures from samples with specific features highlighted. 95

Figure 5.10 Petrographical thin section analyses of Bakken sample BK003 showing anastomosing fracture networks. Fractures show both vertical and horizontal fracture networks with evidence of clay annealing indicating that fractures may be natural. ......................................................................................96

Figure 5.11 XRD results for the five Bakken (BK001, BK002, BK003, BK004 and BK005) samples analyzed. Results are plotted on a standard ternary diagram showing analogous shale plays from North America. Average mineralogical results from the five Bakken samples are shown in the inset. ....98

Figure 5.12 Comparison of QEMSCAN® and XRD analyses of Bakken Formation samples on standard XRD ternary plot. ................................................................................................................................................................ 102

Figure 5.13 QEMSCAN® petrological analyses of Bakken sample BK001. Inset legend indicates key mineral inputs related to the depositional environment. Main image shows key areas of note in the QEMSCAN® image. .................................................................................................................................................... 103





Figure 5.18 Scanning electron microscope (SEM) images of Bakken sample BK001. Organic porosity highlighted by inset magnified boxes............................................................................................................... 108

Figure 5.19 Enhanced color-scanning electron microscope (SEM) images of Bakken sample BK001. Specific features are highlighted by inset annotations on figures. Enhanced color images are created by attributing colors to specific gray-scales. ......................................................................................................... 109





Figure 5.24 Scanning electron microscope (SEM) elemental mapping of Bakken sample BK003. Samples are mapped using discrete elemental (EDS) analyses for calcium, carbon, iron, potassium and sulfur. Important points are highlighted on individual images, with an SEM image of samples also included for reference. ............................................................................................................................................ 114


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Figure 5.26 Enhanced color-scanning electron microscope (SEM) images of Bakken sample BK004. Specific features are highlighted by inset annotations on figures. Enhanced color images are created by attributing colors to specific gray-scales. ........................................................................................................ 116


Figure 5.28 Enhanced color-scanning electron microscope (SEM) images of Bakken sample BK005. Specific features are highlighted by inset annotations on figures. Enhanced color images are created by attributing colors to specific gray-scales. ........................................................................................................ 118

Figure 5.29 Summary map showing common porosity types within samples analyzed from the Bakken. .... 119Figure 5.30 3D render of the Niobrara chalk core sample. Prominent features of the imaged core include

a calcite vein (higher density, white) and pyrite (extremely dense, blue lenses), in addition to distinct darker and lighter bands indicating discrete bedding and changes in sediment distribution/influxes................................................................................................................................................. 122

Figure 5.31 Key petrological comparison of the Bakken ‘Black Shales’ and Niobrara chalks/marls encompassing mineralogy and porosity characteristics. ........................................................................................................ 124

Figure 5.32 Petrographical thin section analysis (left image) and UV thin section analysis (right image) of Niobrara chalk sample (NB006). Photomicrographs show representative mineralogy and textures from samples with specific features highlighted. ........................................................................................ 125

Figure 5.33 Petrographical thin section analysis (left image) and UV thin section analysis (right image) of Niobrara marl sample (NB003). Photomicrographs show representative mineralogy and textures from samples with specific features highlighted. PPL = Plane Polarized Light .................................. 125

Figure 5.34 QEMSCAN® petrological analyses of Niobrara sample NB006. .................................................................. 126Figure 5.35 QEMSCAN® petrological analyses of Niobrara sample NB003. .................................................................. 126Figure 5.36 SEM analyses of Niobrara chalk sample NB006. ............................................................................................. 127Figure 5.37 SEM analyses of Niobrara marl sample NB003. .............................................................................................. 127Figure 5.A1 QEMSCAN® image of Bakken sample BK001. .................................................................................................. 130Figure 5.A2 QEMSCAN® image of Bakken sample BK002. .................................................................................................. 131Figure 5.A3 QEMSCAN® image of Bakken sample BK003. .................................................................................................. 132Figure 5.A4 QEMSCAN® image of Bakken sample BK004. .................................................................................................. 133Figure 5.A5 QEMSCAN® image of Bakken sample BK005. .................................................................................................. 134Figure 6.1 Linear flow into a vertical fracture. ......................................................................................................................... 139Figure 6.2 Average Pressure Profile, and interpolation into known bottom-hole pressure. .................................. 140Figure 6.3 Pressure Profiles determined for different production times. ...................................................................... 140Figure 6.4 Pressure Profiles determined for different production times. ...................................................................... 141Figure 6.5 Difference between Equilibrium (Material Balance) Average Pressure and “Real Time” Average

Pressure. Δ may be positive or negative. ..................................................................................................... 142Figure 6.6 Pressure profiles determined for different production times for a gas system. ..................................... 143Figure 6.7 Pressure profiles determined for different production times using the compressibility correction

relation for a gas system. ........................................................................................................................................ 144Figure 6.8 Pressure Profiles determined using corrected average pressures. ............................................................. 147Figure 6.9 Calculated permeability versus time. .................................................................................................................... 148Figure 6.10 Pressure decline over time at a point 50 ft away from the fracture. ........................................................ 149Figure 6.11 Fluid saturation profile for Case 1 at 90 days of production....................................................................... 149Figure 6.A1 A two-tank system illustrating pressure communication. .......................................................................... 150Figure 6.A2 Figure illustrating the equilibrium state when the two tanks are communicating. ......................... 151Figure 7.1 Workflow for Response Surface Generation. ...................................................................................................... 162

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Figure 7.2 Comparison of regression models with simulation results for (A) time-based oil recovery and (B) rate-based recovery. ................................................................................................................................................ 163

Figure 7.3 Comparison of regression models with simulation results for (A) time-based gas recovery and (B) rate-based gas recovery. ........................................................................................................................................ 164

Figure 7.4 Comparison of surrogate models with simulation results for (A) time-based oil recovery and (B) rate-based oil recovery. .......................................................................................................................................... 165

Figure 7.5 Comparison of surrogate models with simulation results for (A) time-based gas recovery and (B) rate-based gas recovery. ........................................................................................................................................ 165

Figure 7.6 Workflow of Response Surface Generation. ........................................................................................................ 166Figure 7.7 Oil Recovery after 10 years of production (A) Hierarchy of input parameters (B) Probability

distribution of output. ............................................................................................................................................. 168Figure 7.8 Oil Recovery when oil rate reaches 5 STB/day. (A) Hierarchy of input parameters (B) Probability

distribution of output. ............................................................................................................................................. 168Figure 7.9 Gas Recovery after 10 years of production. (A) Hierarchy of input parameters. (B) Probability

distribution of output. ............................................................................................................................................. 170Figure 7.10 Gas Recovery when oil rate reaches 5 STB/day. (A) Hierarchy of input parameters (B) Probability

distribution of output. ............................................................................................................................................ 170Figure 7.11 The geometry of a simulated reservoir. ............................................................................................................. 173Figure 7.12 Pressure-Temperature diagram for three distinct reservoir fluids (red arrows indicate the reservoir

temperatures for the reservoir fluids). ............................................................................................................... 175Figure 7.13 Comparison of regression model with simulation results for (A) time-based condensate recovery

and (B) rate-condensate based recovery (5 STB/day limit). ...................................................................... 176Figure 7.14 Comparison of regression models with simulation results for (A) time-based gas recovery and (B)

rate-based gas recovery. ........................................................................................................................................ 177Figure 7.15 Comparison of regression models with simulation results for (A) time-based condensate to gas oil

ratio and (B) rate-based condensate to gas oil ratio. ................................................................................... 177Figure 7.16 Condensate Recovery after 10 years of production (A) Hierarchy of input parameters (B)

Probability distribution of output. ...................................................................................................................... 179Figure 7.17 Condensate Recovery when oil rate reaches 5 STB/day (A) Hierarchy of input parameters (B)

Probability distribution of output. ...................................................................................................................... 179Figure 7.18 Gas Recovery after 10 years of production (A) Hierarchy of input parameters (B) Probability

distribution of output. ............................................................................................................................................. 181Figure 7.19 Gas Recovery when oil rate reaches 5 STB/day (A) Hierarchy of input parameters (B) Probability

distribution of output. ............................................................................................................................................. 181Figure 8.1 The procedure for implementing the Ensemble Kalman Filter. ................................................................... 190Figure 8.2 The entire reservoir domain, circles are sources (production wells), and crosses are sinks (injection

wells). ............................................................................................................................................................................ 194Figure 8.3 The reference log-permeability (ln k) distribution for the reservoir (actual). ......................................... 195Figure 8.4 True and biased permeability distribution models (top panel) followed by permeability evolution

through time (1, 5, 10, 20, 40, and 100 days). ................................................................................................. 197Figure 8.5 Permeability changes over time.............................................................................................................................. 198Figure 8.6 True, biased, measurement and estimate output. (a) the bottom hole pressure of well 1, (b) the

production rate of well 2, (c) the production rate of well 3, (d) the bottom hole pressure of well 4, (e) and (f) the pressure of monitoring wells. .................................................................................................. 199

Figure 8.7 The updated permeability field under seven different location assumptions. Top left panel is the true permeability distribution. Panels (a) ~ (g) are estimated results. .................................................. 202

Figure 8.8 The log distribution of permeability and pressure distributions of the true and bias models. (a) ln k

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of the true model (b) pressure from the true model at t = 200/day (c) ln k of the biased model (d) pressure from the biased model at t = 200/day. ............................................................................................ 206

Figure 8.9 The updated permeability and pressure distribution using EnKF............................................................... 207Figure 8.10 Top layer of the PUNQ-S3. ....................................................................................................................................... 208Figure 8.11 The permeability distributions of the reference, initial and EnKF updated reservoir model. (a) ~ (e)

indicates five different layers. .............................................................................................................................. 211Figure 8.12 Panels (a) through (l) are the predicted BHP of six production wells from initial and updated

ensembles. Panels a through l .............................................................................................................................. 212Figure 9.1 The phenomenal growth in production of liquids from shales using the Eagle Ford as an example.

In just over a three-year period, insignificant production has been transformed to over 52 million barrels of liquids in 2011. ...................................................................................................................................... 218

Figure 9.2 The framework used in simulating water injection and production in fractured systems. The Discretization Methods (DM) are decoupled from the Physical Models (PM). ................................... 219

Figure 9.3 Two fractured systems with orthogonal fractures simulated in this study. ............................................. 220Figure 9.4 Two fractured systems with non-orthogonal fractures simulated in this study. .................................. 220Figure 9.5 Three sets of capillary pressures used in this study. ......................................................................................... 221Figure 9.6 Water saturations in the matrix through one hydraulic fracture and interacting natural fractures.

The left panel is the water-wet case, the middle panel is the mixed-wet case and the right panel is the oil-wet case. As the wettability goes from water-wet to oil-wet the infiltration decreases creating an increase in the injected water recovery. In this particular example, the large cross-cutting feature does not take a significant amount of water off site. A water saturation scale is also shown. .................................................................................................................................................................. 226

Figure 9.7 Water retained in the matrix through one hydraulic fracture and interacting natural fractures. The domain without the large cross-cutting feature is used. The left panel is the water-wet case, the middle panel is the mixed-wet case and the right panel is the oil-wet case. As the wettability goes from water-wet to oil-wet the infiltration decreases creating an increase in the injected water recovery. A water saturation scale is also shown. .......................................................................................... 226

Figure 10.1 Conventional condensate reservoir fluid pressure-temperature diagram, showing path from reservoir conditions to high-pressure separator. .......................................................................................... 233

Figure 10.2 Conventional condensate field process simulation flow sheet – ASPEN v7.2. .................................... 233Figure 10.3 Well subsurface flow modeling with ASPEN v7.2. .......................................................................................... 235Figure 10.4 Subsurface flow modeling with “high” bottom hole pressure (BHP). ...................................................... 235Figure 10.5 Subsurface flow modeling with “low” bottom hole pressure (BHP). ....................................................... 236Figure 10.6 Eagle Ford black-oil reservoir fluid PT-diagram by PVTsim v20. ............................................................... 236Figure 10.7 Eagle Ford black-oil single-stage separation material balance flow sheet (ignoring water). Results

per simulation with PVTsim v20. ......................................................................................................................... 237Figure 10.8 Eagle Ford black-oil two-stage separation material balance flow sheet (ignoring water). Results

per simulation with PVTsim v20. ......................................................................................................................... 238Figure 10.9 Eagle Ford condensate* reservoir fluid PT-diagram by PVTsim v20. (*Condensate plausible per

Whitson and Sunjerga [2012]). ............................................................................................................................. 238Figure 10.10 Eagle Ford condensate single-stage separation material balance flow sheet (ignoring water).

Results per simulation with PVTsim v20. .......................................................................................................... 239Figure 10.11 Eagle Ford condensate two-stage separation material balance flow sheet (ignoring water).

Results per simulation with PVTsim v20. .......................................................................................................... 240Figure 11.1 A two-dimensional (2D) DEM model: (a) a discrete element network generated by randomly

packing discs of variable sizes with a uniform size distribution; and (b) a bent elastic beam connecting nodes i and j used for DEM. ........................................................................................................... 244

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Figure 11.2 Representation of the concept of using a conjugate lattice (blue lattice) for network flow modeling and coupling with the DEM network (white lattice). ............................................................... 245

Figure 11.3 Model problem setup (left), hydrostatic confinement, and (right) anisotropic confinement........ 248Figure 11.4 Simulated fracture propagation process at different stages (from top-left to bottom-right) under

hydrostatic confinement conditions. These plots show the locations, orientations, and lengths of individual microfractures, colored by their apertures. ................................................................................ 249

Figure 11.5 The simulated fracture propagation process at different stages (from top-left to bottom-right) under anisotropic confinement conditions with a maximum to minimum confining stress ratio of 2. ...................................................................................................................................................................................... 250

Figure 11.6 The comparison of fracturing patterns between the high injection rate (left) and low injection rate (right). ............................................................................................................................................................................ 251

Figure 11.7 The comparison of fracturing patterns between the high viscosity fluid (left) and low viscosity fluid (right). The color scales indicate the fluid pressure. ........................................................................... 251

Figure 11.8 Conceptual models for hydraulic fracturing under mechanical (left) and hydraulic heterogeneities. The thicknesses of the arrows indicate the magnitudes of the vertical and horizontal stresses. 252

Figure 11.9 Simulated final hydraulic fractures under mechanical (left) and hydraulic (right) heterogeneities shown in Figure 11.2. ............................................................................................................................................... 253

Figure 11.10 Model set up for a multi-stage horizontal wellbore with maximum compressive stress in the vertical direction and minimum compressive stress in the horizontal direction (left), and the details of perforations (right). .............................................................................................................................. 253

Figure 11.11 The simulated fracture propagation process from a horizontal wellbore with multiple perforations. Color scales indicate fluid pressure. (Click to play videos) .............................................. 254

Figure 11.12 Stress evolutions during the hydraulic fracturing process. (Click to play videos) ............................ 256Figure 11.13 Effect of perforations on the fracturing pattern. (Click to play videos) .............................................. 257

List of TablesTable 1.1 Factors that affect oil production from liquid shales .............................................................................................. 3Table 2.1 Bakken black shale core samples. ................................................................................................................................15Table 3.1 Correlation coefficient values for geologic variables on first year oil production for vertical wells

drilled in the 1980s at Silo Field. Fracture intensity is the greatest influence on production followed by weight % calcite. Thickness, resistivity, and porosity are not related to the magnitude of production. ...............................................................................................................................................................48

Table 5.1 Table 1 Results of the bulk Rietveld refinements by sample name, with abundances given in weight percent of sample. ......................................................................................................................................................97

Table 5.2 QEMSCAN® analyses for Bakken Formation samples. ...................................................................................... 101Table 6.1 Example production data: Case 1 to 3. ................................................................................................................... 138Table 7.1 List of input parameters chosen for the study. .................................................................................................... 160Table 7.2 List of input distribution. ............................................................................................................................................ 167Table 7.3 Uncertainty in oil recovery and rank of input parameters. ............................................................................. 169Table 7.4 Uncertainty in gas recovery and rank of input parameters. .......................................................................... 171Table 7.5 List of input parameters selected for the study. .................................................................................................. 174Table 7.6 Composition of three distinct reservoir fluids. .................................................................................................... 174Table 7.7 List of Input Distribution. ............................................................................................................................................. 178

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Table 7.8 Uncertainty in oil recovery and rank of input parameters. ............................................................................. 180Table 7.9 Uncertainty in gas recovery and rank of input parameters. .......................................................................... 182Table 8.1 Comparison of last outputs from six wells with observation updating with the first predictions of

outputs without updating ..................................................................................................................................... 203Table 8.2 The root mean square (RMS) of error between predicted output from updated results and

observations. .............................................................................................................................................................. 204Table 9.1 Properties of the domain with orthogonal fractures and simulations. ...................................................... 222Table 9.2 Water injections and recoveries for the three wetting scenarios and for the two domains studied in

this paper based on injection for 6 hrs in 30 days of production. Base case recoveries using matrix permeability of km = 0.001mD. ............................................................................................................................. 222

Table 9.3 Water recoveries for the three wetting scenarios and the two domains studied in this paper. Recovery rates using a matrix permeability of 0.01 mD. ............................................................................ 223

Table 9.4 Water recoveries for the three wetting scenarios and for the two domains studied in this paper. Recovery rates using a matrix permeability of 0.1 mD. .............................................................................. 223

Table 9.5 Properties of the domain with non-orthogonal fractures and simulations .............................................. 224Table 9.6 Water recoveries for the three wetting scenarios and for the two domains studied in this paper.

Recoveries are for the base case where the initial reservoir pressure was 2000 psia and the matrix permeability was 0.5 mD. ...................................................................................................................................... 224

Table 9.7 Water recoveries for the three wetting scenarios and for the two domains studied in this paper. Recoveries are for the base case capillary pressures where the initial reservoir pressure was 5000 psia and the matrix permeability was 0.1 mD. ............................................................................................... 225

Table 9.8 Water recoveries for the three wetting scenarios and for the two domains studied in this paper. Recoveries are when the capillary pressures were ten times the base case capillary pressures used. The shapes of the capillary pressure curves were the same as the ones used in Figure 9.4. The initial reservoir pressure was 5000 psia and the matrix permeability was 0.1 mD. ................. 225

Table 10.1 Comparison of material balances for Eagle Ford black-oil and condensate reservoir fluids for single- and two-stage separation. ..................................................................................................................................... 240


Research Interests

• Production of fluids from shales

• Reservoir engineering

• Enhanced oil recovery

Email [email protected]

Phone 801-581-7629

Milind Deo, Ph.D.Milind Deo is a Professor and Chairman of the Department of Chemical Engineering at the University of Utah and Director of the Petroleum Research Center where he has supervised over 20 Ph.D. students and six Masters students.

He received his B.S. from the Indian Institute of Technology in Chennai, India and his Ph.D. from the University of Houston in Houston, TX, in Chemical Engineering. His Ph.D. research concentrated on the development of methods for determining residual fluid saturations after a carbon dioxide flood.

After a short stay at the Schlumberger Perforating Center in Houston where he worked on under-balanced perforating technology, followed by two years of post-doctoral work at Stanford University, he joined the University of Utah in December 1989. At Stanford, his main areas of interest included detailed petroleum characterization and phase behavior of oil, gas, and carbon dioxide mixtures. His main areas of research at the University of Utah have been reservoir engineering and enhanced oil recovery. He is widely published in the field with numerous papers and articles, and has authored several EGI reports.

Research HighlightsDr. Deo’s research group was involved in two highly successful U.S. Department of Energy (DOE) Class I and Class III Reservoir Projects. The Class I Reservoir Project with Lomax Exploration Company led to the revitalization of waterfloods in the Uinta Basin, while the Class III Project reactivated an idle lease in the Midway Sunset Field, resulting in over two million barrels of additional oil produced.

His research group has also developed a new generation of reactive-transport, multiphase reservoir simulators that are also capable of modeling fractures as discrete networks. He is conducting a comprehensive multifaceted study on Liquids from Shales that includes production analysis, development of rapid analysis and forecasting tools, material and geologic characterization, reservoir simulation, and geomechanics.

Currently, he and his students are engaged in research related to production of oil from oil shale, carbon dioxide enhanced oil recovery and sequestration, heavy oil production, and flow assurance apart from unconventional gas production.

Dr. Deo has developed a strong oil and gas enhanced oil recovery and reservoir engineering research program at the University of Utah. An important milestone in this program was the establishment of the Petroleum Research Center as one of the State Centers of Excellence. He has also established a computational component with focus on fractured reservoirs and discrete-fracture models, including a series of multi-phase, finite element discrete-fracture models as an alternative to dual-porosity models to help better understand and manage fractured reservoirs.

April 3, 2014 10:47 AM

Professor Chemical Engineering


Tom has over 35 years of experience in the energy sector. He came to EGI after serving for seven years with the U.S. Department of Energy at the Rocky Mountain Oilfield Testing Center in Casper, Wyoming, with his last position being Chief Scientist.

He earned an M.Sc. in Geology from Brigham Young University, in Provo, Utah, with a specialization in Structural Geology and Geomorphology. He also received a B.Sc. in Geology from Brigham Young University.

Tom also has extensive experience with Halliburton’s Landmark Graphics, Conoco, and Gulf Oil, managing global technology implementation projects, providing technical services, and discovering and developing oil and gas fields in North America. His geographic expertise includes the Rocky Mountains, Wind River Basin, Powder River Basin, onshore South Texas, Snake River Plain, Great Basin, Thrust Belt, Los Angeles Basin, and the Williston Basin. Internationally, he worked in London for Conoco (UK) Ltd on North Sea projects, and has worked for clients Mobil, Exxon, and BP in Norway, the UK, Germany, Malaysia, Australia, Venezuela, Trinidad, Canada, Egypt, Dubai, Indonesia, and Brazil.

Tom is a Registered Professional Geologist in the State of Wyoming, a member of the American Association of Petroleum Geologists, (President of the Rocky Mountain Section, 2010), Rocky Mountain Association of Geologists (Denver), Utah Geological Association, and the Wyoming Geological Association (President in 2006).

Tom’s publications include geothermal power generation from oil production, fractured reservoir characterization for CO2 injection design, high-resolution seismic mapping of a shallow oil reservoir, structural geology of the Circle Ridge oilfield, Wyoming, exploration potential of the lower Ordovician in the Williston Basin, and structural geomorphology of the Wasatch Fault in central Utah.

At EGI, Tom is focusing on the geologic aspects of liquids from shales, other unconventional reservoirs and researching the geothermal potential of deep sedimentary basins. Leading field trips throughout the rocky mountain region and demonstrating analogs for conventional and unconventional reservoirs have been valuable to several of our CA members. In support of the industry at large, Tom dedicates time to mentoring students in petroleum geology, professional development, and career options.

Email [email protected]

Phone 801-585-9178

Senior Advisor to the Director

Research Interests

• Unconventional reservoirs (Bakken, Niobrara)

• Geothermal energy from co-production in oilfields and deep sedimentary basins

• Geologic carbon storage and reservoir characterization

• Geologic mapping (surface and subsurface) and field geology

• Computing technology, including GIS (Geographic Information Systems)

• Petroleum exploration and development geology

• Geomorphology and structural geology

Tom Anderson, M.Sc.

April 3, 2014 10:47 AM

Documents

Flyers Lab & Capability · characterization has been the scanning electron microscope (SEM) as well as focused ion beam (fib) slicing combined with SEM. Images from various locations