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WHOC12-363

Integrated Advanced Well Completion Design Implementation Helps To

Quantify Uncertainty and Optimize Well Performance in a Heavy Oil Field in the GOM

D., GARCÍA GAVITO A. E., FREITES PEMEX Schlumberger F., RODRIGUEZ R. J, CARVAJAL PEMEX Schlumberger

L. A., CARRILLO M. A., ROMERO PEMEX Schlumberger

This paper has been selected for presentation and/or publication in the proceedings for the 2012 World Heavy Oil Congress

[WHOC12]. The authors of this material have been cleared by all interested companies/employers/clients to authorize dmg events

(Canada) inc., the congress producer, to make this material available to the attendees of WHOC12 and other relevant industry

personnel.

Abstract

The independence in the design of well positions, geometries and completions in the reservoir frame has been one of the main concerns of the petroleum industry in general. Up to this moment it had been almost impossible to develop an integrated approach that led to the optimization of well performances. This kind of approach is critically needed for heavy and extra-heavy oil reservoirs, where non-conventional wells and different completion configurations have to be considered in order to overcome production problems (the mobility ratio is adverse to the oil and water cut is usually very high).

This project comes up to solve this issue and presents a

new methodology that brings the world of reservoir simulation and well completion design closer together and allows to automatically run several different combinations of well spatial positions and geometries (vertical, deviated, horizontal and multilateral) and the optimization of ICD´s and cased holes configurations.

Three wells were fully designed in a heavy oil field in the

Gulf of México (carbonates), using advanced reservoir simulation options like sector modeling (SM), local grid refinement (LGR) and well segmentation. Each well encompassed up to 9 unknown parameters for an open hole

which were systematically evaluated using sensitivity and uncertainty analysis (equal spacing and Central Composite sampler respectively). A process of "proxy" training and optimization was then carried out to completions with ICD´s and cased hole. The module of Uncertainty and Optimization of Petrel was successfully used to support the automated process.

The results were compared with a conventional well

considered in the original field development plan and showed that options of multilateral wells with open hole and horizontal wells with ICD´s dramatically increase the oil recovery and improve water production control. The time to perform the analysis was reduced in more than 70% in comparison with regular studies for well position and geometry.

Introduction

Historically, well design has been an area of continuous

discussion and research; the lack of tools and workflows to

perform a complete evaluation of all the factors involved (well

position, geometry and completions) has made of this an

inefficient and unreliable process.

Currently, in the work team, a regular well design study could

take 2-3 months and will go first to a reservoir simulation

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model to make discrete tests over a few possible location and

well geometries and then to analytical tools for completions,

where complex well-reservoir system interactions are not

considered. In other words, the process is carried out in a series

of “almost” independent steps.

The main objective of this project was to tackle down this way

to work by developing an integrated methodology that allows

an effective evaluation of multiple well geometries, positions

and completions configurations to identify the optimum way to

produce a highly complex heavy oil field located in the Gulf of

Mexico (GOM).

This field has no production wells and the information

available was generated for a simulation study directed to the

design of a development strategy. The simulation grid was built

using the information gathered for two exploration wells and

encompasses a total of 174.276 active cells (corner point

geometry) with the following dimensions: 150x150 areal and

variable size in vertical direction with approximately 10 m

through the production formation and 95 m in the cells located

below the water-oil contact (-4228 m). The average pressure

was estimated in 215.3 Kg/cm2 at a reference depth of -3800

m. The reservoir is under-saturated (Pb=55 Kg/cm2) and the

fluid density is of 11 oAPI approximately.

Directional wells (deviated 30o) with open hole were

considered in the original study and used as a comparison basis

for the ones designed in this project. The developed workflow

will be explained for one of the three well we designed in this

project. For the other two just the final results are shown.

Methodology and Results

The last releases of Petrel, where a whole range of new options

regarding well completions have been included, have opened a

window of opportunities for the development of an integrated,

statistically supported approach for well design, allowing

testing more well alternatives in less time. Figure 1 presents the

workflow developed in the “Advanced Well Completion

Design Project”, as it was called for PEMEX.

Three phases were carefully depicted, each one with specific

but closely related tasks. All processes were performed in the

same platform and using the same simulator (ECLIPSE100).

This is one of the most important differences in the

methodology we propose and regular studies for well design.

As it was stated before, the design of well geometries and

completions were done using different tools, according to the

different levels of detail required in every stage of the process.

As a consequence, the workflow tend to be divided in a series

of “almost” independent steps, while our methodology is

carried out using nothing but the combination of

Petrel/ECLIPSE100, keeping the input of one stage as the

direct result of the previous one.

Let us give a brief description of every phase of the workflow

we propose before going deeper into them. The purpose of

“Phase 1” is to prepare a model to achieve the detail needed

for the design of a well completion. Generally, operators

companies build simulation models for their fields, in order to

establish their developments plans. These models are usually

too coarse for well design and need to be refined. However,

refining could be a tricky process and it is necessary to be

aware of the changes in production profiles while doing it.

“Phase 2” represents the first major task in the workflow,

where multiple combinations of well geometries and positions

are explored simultaneously: the result of this stage will allow

advancing to the next phase with the best possible options with

open hole. In “Phase 3” completions with ICD’s and Cased

Hole are considered; the inner as an alternative to control water

production and the latter as the regular completion used in the

GOM.

Phase 1. Base Model Preparation

Definition of areas of interest

After receiving and validating the simulation model of the

field, the area of interest for new wells was defined. The

selection of this area could be constrained by many reasons:

environmental, budget, rig availability, etcetera; however, if it

is assumed that none of this are actual limitations, as it was the

case in the field we worked on, a simple set of equations

known as the Combined Rock Quality Index (ROI) could be

used to rapidly evaluate the reservoir in terms of its possible

production potential.

………..(1)

………………………….(2)

……………………(3)

By using the calculator of Petrel we can add constraints related

to the distance to the faults and WOC to the previous equations

in order to eliminate these zones from the group of candidates

to drill new wells; this is an effective technique to guarantee

that the results will not violate any physical or operational

limitation. Figure 2 shows an example of how the grid will

look after generating this property.

Sector model and refinement

The original grid was too coarse to perform a well design,

giving the fact that it is a detailed task where production

behavior and water flow need to be followed accurately; Local

Grid Refinement (LGR) had to be considered. To allow

capturing all the effects wanted to study we used a refinement

of 3x3x4, producing cells of 50x50x3 m approximately. This

produced important increases in simulation time making

impossible to refine the whole model. We then decided to

focus on sector models with Flux Boundary for every single

SorSOILPORONTGDzDyDxSOMPV

PORO

AVEPERMRQI 0314.0

3 SOMPVRQIPRESSUREROI

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well (we are designing three wells for which three sector

models will be needed).

It is well known that after a LGR the permeability distribution

is practically unaltered since the process makes a simple

splitting of the cells. To avoid that, we upscaled the refined

area based on the geologic model (see Figure 3). The results

between the refined and the refined-upscaled models were

compared showing increases in the water production for all

cases. This result was expected since the permeability

anisotropy generally contributes with the water flow in heavy

oil reservoirs. No relative permeability curves end-point

upscaling was performed.

Phase 2. Geometry and Position of Open Hole Wells

Design of “Base” Wells

Once the sector models are extracted we can continue with the

design of "Base Wells". These wells were the different base

geometries we want to evaluate (vertical, deviated 700 and

horizontal); laterals could be added to the latter two. All these

options were parameterized as a variable named "Well Type"

(TP). For each TP three versions were built, every one related

to the length of the well (long, medium and short); this was the

second variable: "Length of the Well Type" (LH).

Areal (North-South, DY- East-West, DX) and vertical (DZ)

displacements , the number (NumLaterals), length

(LongLaterals) and azimuth of the laterals and the maximum

liquid production rate completed the list of nine uncertainty

variables over which the study for open hole wells was to be

performed. Parameterizing the variables and assigning ranges

to every one of them will allow to control and narrow the study

to one we can effectively control and analyze.

The spatial positions variables (DX, DY, DZ) ranges are

constrained by the size of the sector model and the refined zone

themselves. Consider that we are recording the “Flux Boundary

Conditions” over a sector and that inside of it an LGR exists

(see Figure 4); a well cannot be placed in both the refined and

non-refined areas; this is a limitation of ECLIPSE100. So, it is

up to the engineer in charge of the study to create a sector and a

LGR big enough to cover all the locations that can be

interesting to test while designing the well.

Notice that all the rest but the liquid production rate are

geometry-related variables. The type of well and its length are

especially important for heavy oil reservoir; maximum contact

between the wells and the reservoir is usually wanted to

increase the productivity index of these and decreasing the

pressure drawdown in the system. For all well types, an

advanced option of ECLIPSE100 known as “Well

Segmentation” was used, in order to simulate accurately the

pressure drops in terms of friction, acceleration and gravitation

in the wellbore. This is a key factor when calculating flow in

comingling of branches and long horizontal production

sections.

Automatic creation of simulation cases

Using the Uncertainty and Optimization module of Petrel a

workflow was built to automate the processes of sensitivity and

uncertainty. This workflow was designed to be the key of an

efficient and standardized methodology, very simple to use and

that requires a minimum amount of information to be run.

Hundreds of data files with different alternatives of multi-

segmented wells could be generated with just one "click" and

systematic, organized and statistically supported studies could

be easily performed.

The workflow (for this stage) consists in 54 lines of code,

encompassing all the processes related to the wells spatial

positions and geometries. The first 44 lines correspond to the

well selection. In practical terms we had 9 different base wells

to test (3 TP, each one in its versions long, medium and short).

A code number was assigned to each well as the combination

of the TP and LH. Depending on the value that every variable

will take in every run of the experiment matrix (we will talk

about it in the following step) a well will be chosen to be

tested. Then that well will pass through the Laterals addition

process (if applicable). Conditional were place in the code to

avoid inconsistencies such as vertical wells with laterals.

Later on, the “Move Well” process (included in the version

2011.1 of Petrel) will displace the well from its original

position to test a given volume. The displaced well will enter

the “Well segmentation” utility to establish calculation nodes.

As it was stated before, this process is very important since it

takes into account all the components of the pressure drop,

bringing more accuracy to the results. The well is then

prepared to be included in the correspondent development

strategy and simulation case. In other words, the output of this

workflow is a data file with a determined configuration that can

be run directly from Petrel or exported and run from the

“Simulation Launcher”.

Sensitivity and Uncertainty Analysis

One of the most important goals to be achieved in this project

was to reduce the time to complete a full well design study. As

a consequence, it is necessary to determine which of the initial

group of variables we have to focus on. A sensitivity analysis

will rapidly allow the identification of the parameters having

major impact over the cumulative oil (NP) and water

production (WP) in the horizon of prediction (15 years); it

important to point out that this kind of analysis is only useful to

dismiss parameters when it is assumed that the variable will not

have a bigger effect interacting with other variables than by

itself.

We used the "Equal spacing sampler" with two sample points

to perform this part of the study. Figure 5 shows the Tornado

plot we obtained after running the process for one of the wells

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under in design. The azimuth and the displacement in the East-

West direction seemed to have a marginal effect over the NP

and were dismissed for the upcoming processes.

The reduced group of 7 variables was considered for the

Uncertainty analysis and sampled using a Fractional Central

Composite experiment. This method allows a deep exploration

of the sampling space, testing points in the extremes and center

of the uncertainty range of every variable and taking into

account 2nd factor interactions between them, even though they

can be confounded with 3rd factor interactions [1].

Hierarchy of open hole wells

Multiple alternatives of well geometries and positions with

open hole were generated and run in ECLIPSE100 (Figure 6);

this led to hierarchy the best possible combinations of

parameters for an improved NP. Figure 6 presents the results of

NP and WP (purple bars) for different well types proved in one

of the sector models we worked on. The yellow bar located to

the left of the graph represents the cumulative oil recovered

with the original well considered in the development plan of

the field and is kept as reference for comparison purposes. The

blue line defines the type of well; for example, code “1”

corresponds to vertical wells, code “3” to deviated 700 and

code “4” to horizontals. The number of laterals attached to

every well is given by the red line.

This kind of plots are very useful to understand the general

behavior of the different well alternatives, as it actually

happens in this case, where horizontal/multilateral wells report

a much better recovery of oil and water control than deviated

or vertical ones. A well with approximately 1.0 Km of

horizontal section with two laterals of 500 m of length,

displaced to the north of the field and the top of the formation

(see Figure 7) and operating at its maximum possible liquid

production rate, reported the best NP an excellent water

production control. Other interesting alternatives are given by

those which did not produce any water in the 15 years of

prediction. For instance, the same well described before will

not produce any water if the limit liquid production rate is

reduced to the minimum of its uncertainty range.

From the operational point of view it is also important to take a

look on the horizontal wells instead of multilateral, given the

complexity of the latter. The best horizontal well will produce

3 MMBls of oil less than the best multilateral and 5 MMBls

more of water. The question is: it is reasonable to assume the

risk of drilling a multilateral well considering the production

behavior shown in this study? This could be only answer by

performing an economical analysis.

Phase 3. Completions optimization

Well completion designs

After finishing the open hole hierarchy we wanted to study the

effect of different kind of completions over the production

profile. We considered two types of completions, in addition to

the open hole we deeply studied in the previous phase. The first

one was the cased hole (TCYD); this is the most common kind

of completion used in the GOM. Although, it is not expected to

act as a production optimizer, the location of the perforations

could help control water production, which is the bigger

problem in the study.

On the other hand we have the Inflow Control Devices

(ICD’s), which are thought to control the pressure profile along

the wells in order to improve its performance. Both the cased

hole and the ICD’s are options completely supported by Petrel,

program that offers the possibility of rapidly configuring,

parameterizing and exporting them to the simulator; the

accuracy of the calculations will be improved by the use of

“Well segmentation”.

At this point a question came to our minds: which wells to use

for adding this completions? The natural way to go would have

been to take the best well found in the uncertainty analysis with

open hole, but, is it really possible to add ICD´s to the full

extension of a multilateral well? And moreover, is it possible to

perforate more than 2 km of length? The inner is extremely

difficult; the experience gained at some locations worldwide

(Saudi Arabia for instance) suggests that for making

operationally possible to place ICD’s in a multilateral well it is

necessary to restrict the flow at some sections of the well,

generating a configuration like the one shown in Figure 8. As a

consequence the main advantage of this kind of well, the

increased well-reservoir contact is lost. The latter almost

impossible, having as a reference that the common length of

perforations in the GOM is approximately 60 m. The decision

then was to migrate to other well options.

Proxy generation and Optimization

For the addition of ICD´s we used the best horizontal well

found in the uncertainty analysis. The study of this completion

was made with base in three parameters, which were thought to

control the entire production process. The variables were: 1) x-

cross section multiplier: this is a very important since it has

direct influence over the flow passing through the devices. A

multiplier greater than one will decrease the pressure drop

between the reservoir and the well with respect to the original

cross area of the device (0.013525 ft2) , while a multiplier

smaller than one will increase the pressure drop and restrict

more the flow to the well. 2) Valves per compartment and 3)

space between packers, will also help to control the pressure

drop and the contribution of different sections of the reservoir

to the global production.

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In order to save time for the optimization of the ICD’s

configuration we decided to train a quadratic proxy. 15 runs

training runs were generated with a Central Composite sampler

by using a simple workflow in Petrel. 5 validation runs were

used to test the accuracy of the analytical model, obtaining an

excellent approach (less than 2% of error). The optimization

was performed over the proxy, showing that a configuration

with 277 m between packers, 3 devices per compartment and

x-cross section of 0.002705 ft2 will represent the best possible

ICD’s scenario in terms of NP and water control (Figure 9).

Figure 10 shows the comparison between the base well (the

one considered in the original field development plan), the best

horizontal well with open hole and the same well completed

with the optimized configuration of ICD’s. The NP was

increased in more than 2 MMBls and the water production

reduced in almost 3 MMBls by the addition of this devices.

For the cased hole completion we decided to use a highly

deviated 70o well and train a proxy with two variables: 1) depth

and 2) length of the perforations. 9 training runs were

generated with a “Central composite” sampler and 3 with a

“Montecarlo” sampler for validation. The error of the proxy

when trying to reproduce the simulation behavior was less than

3%, reliable enough to be used for optimization.

Figure 11 presents the NP and WP for the optimized

configuration with cased hole (length of the perforations: 200 ft

and depth: 14600 ft) and compare it with the base well and the

deviated well with open hole. The optimized scenario allowed

an important improvement of the production profile by

controlling more effectively the water inflow.

Results analysis

Figure 12 presents the best alternatives we found in the study

as a percentage of increment/decrement of water and oil

production of the base well. In practical terms, we are

comparing the final alternatives a drilling/completion engineer

would have available to decide the geometry, position and

completion of a new well.

Improvements of the NP and water production control were

reached. A multilateral well increased the recovery of oil in

more than 14 MMBls (85%); the reason of such a big change

in the production profile was that the multilateral well requires

a smaller reservoir-well pressure drop (see Figure 13) than the

base well to achieve the same liquid rate, slowing down the

advance of water from the aquifer. A simpler alternative as the

horizontal well with ICD’s also showed to be a great option for

this sector model. However, as it was stated before, only by

performing and economical analysis a final decision could be

made.

Figure 14 and 15 show the final results obtained for the other

two wells we analyzed in this study. Both of them were located

in zones with extreme water problems and, as a result, the

maximization of the production could only be achieved by

using horizontal/multilateral wells. For the first case a

horizontal well with ICD’s showed to be the best option,

followed by a multilateral and a horizontal well with open hole.

For the second case (Figure 15), the multilateral well reported

the best NP with a dramatic improvement of 495% with respect

to the base case. Notice than in this case, the ICD’s did not had

the same impact they had in the previous sectors. This was

mainly because the well was located so close to the aquifer that

there were no time see the equilibration of the pressure profile

offered for this kind of devices.

To sum up the importance of this last couple of wells it is must

be point out that they were placed in zones were marginal

production wells were expected, but thanks to this study they

are now considered as keys in the development of the field.

Significance of subject matter

For the very first time in Latin America a project of Advanced

Well Completion Design using numerical simulation and using

static and dynamic characterization models has been

successfully carried out. The methodology efficiently supports

statistic studies of well position, geometry and completions,

allowing the engineers to evaluate multiple alternatives in a

short time. The automated process is very easy to understand

and requires of a small amount of input data to be run, so, the

proposed solution could be used by engineers with minimum

knowledge of Petrel.

Conclusions

An integrated approach for the connection between the

reservoir simulation model and well completion design was

successfully generated and tested.

Automated, easy to use, workflows for sensitivity and

uncertainty analysis of well positions, geometries and

optimization of ICD´s and Cased Hole configurations were

designed.

Multiple scenarios of open hole wells and optimized ICD´s

and Cased Hole configurations were compared with the

conventional wells considered in the original field

development plan and substantial improvements on the NP

and water production controls were found.

The time required to complete the process of well design was

dramatically decrease, saving up to 70%.

Acknowledgement

We would like to thank to all the personnel working at the

"Subdirección de Gestion Recursos Técnicos" of PEMEX for

their collaboration and support at every stage of the project,

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particularly Dr. Daniel García Gavito, for his support during

the development of the work.

Nomenclature

Dx, Dy, Dz: cell dimensions.

NTG: Net to gross.

PORO: Porosity.

SOIL: Oil saturation

Sor: Residual oil saturation.

SOMPV: Pore volume saturated with mobile oil.

RQI: Reservoir quality index.

AVEPERM: Average permeability.

PRESSURE: Reservoir pressure.

ROI: Combined Rock Quality Index.

REFERENCES

1. Petrel 2011.1 Help. Uncertainty and Optimization Module.

Figure 1. Methodology for the Advanced Well Completion Design.

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Figure 2. Areas of interest in a field of the GOM.

Figure 3. Refined Vs Refined and Upscale sector model.

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Figure 4. Sector model and refinement.

Figure 5. Tornado Plot.

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Figure 6. Hierarchy of well types with open hole wells.

Figure 7. Well in its original position (blue) Vs Well in its optimized position (red)

31.25

34.7

40.67

43.83

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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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Np,

Wp

(MM

Bls)

Cases

Well I Hierarchy of Well Types

With base on Np, Wp

NP (MMBls) WP (MMBls) TP Number of Laterals

43.83 40.67

34.7

31.25

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Figure 8. Configuration of ICD’s in a multilateral well.

Figure 9. Optimized ICD’s configuration.

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Figure 10. Comparison of original wells Vs ICD’s optimized configuration.

Figure 11. Comparison of original wells Vs TCYD’s optimized configuration.

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Original Horizontal Open Hole Horizontal with ICD's

16.59

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Configuration

WP (MMBls)

NP (MMBls)

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Original Deviated 70o with open hole

Deviated 70o with Cased Hole

16.59

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38.31

Well 1Comparison of Original Wells Vs TCYD's Best

Configuration

WP (MMBls)

NP (MMBls)

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Figure 12. Comparison between best alternatives for well 1.

Figure 13. Comparison of Reservoir-Well pressure drops for the base well and the best multilateral.

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Figure 14. Comparison between best alternatives for well 2.

Figure 15. Comparison of best alternatives for well 3.