Evaluation of In-Fill Well Placement and Optimization Using Experimental Design and Genetic Algorithm

Evaluation of In-Fill Well Placement and Optimization Using Experimental Design and Genetic Algorithm.

ABSTRACT

Determination of optimal well locations for infill drilling is a challenging task because engineering and geologic variables affecting reservoir performance are often nonlinearly correlated and have some degree of uncertainty attached to them.

Numerical models which are the basis of well placement decisions rely on data that are uncertain, which in turn translate to uncertainty in our numerical simulation forecasts.

The objective of this research is to employ an efficient optimization technique to the well placement problem to determine the optimum infill well location.

Based on the success of its previous application by other authors in solving the well placement problems, Genetic Algorithm (GA) will be used here as the main optimization engine.

An experimental design is used to generate some experimental simulation runs using the uncertain parameters, and these uncertain parameters are used to fit a response surface model of the objective function.

The response surface methodology is used to identify the optimum design under conditions of uncertainty to build a proxy model that can be utilized to predict the cumulative oil produced.

Our application of GA to determine the optimal location for infill well placement in a synthetic reservoir is improved by using a set of screening criteria and some engineering judgment to reduce the search space for possible locations.

The proxy model generated from the response surface methodology is also combined with GA to determine the optimal locations for three cases of drilling two, four or six additional infill wells in the reservoir modeled in this study.

The study found that response surface models can be used as a proxy tool coupled with GA to provide reliable results; and to reduce the number of simulation runs required for the well placement optimization problem.

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TABLE OF CONTENTS

Abstract………………………………………. iii

Acknowledgments……………………………………… iv

Contents………………………………………. v

List of Tables……………………………….. vii

List of Figures……………………………. viii

1. Introduction and Statement of Problem………………………. 1

1.1. Introduction…………………………………………………………… 1

1.2. Literature Review …………………………………………… 3

1.2.1. Optimization Techniques……………… 3

1.2.2. Stochastic Optimization Algorithms…………………………… 4

1.3. Statement of problem and purpose …………………………………………. 8

1.4. Scope of work ……………………………… 9

1.5. Organization of thesis ………………………….. 10

2. Optimization Algorithm…………………………………………… 11

2.1. Overview of Genetic Algorithm…………………………….. 11

2.1.1. Genetic Algorithm (GA)……………………………………………. 11

2.1.2. GA Operators…………………………………………………………… 13

2.2. Use of Proxies ………………………………………………………………………. 15

2.3. Experimental Design………………………………………………………………. 16

2.4. Response Surface Methodology………………………………………………. 18

3. Reservoir Model………………………………………………………….. 20

3.1. Reservoir description……………………………………………………………….. 20

3.2. Production data assimilated ……………………………………………………… 21

4. Well placement Optimization……………………. 23

4.1. Constraints for Well Placement….…………………………………………… 23

4.2. Saturation and Pressure Screening……………………………………………… 24

4.3. Reservoir Uncertainty………………………………………………………………. 28

4.4. Sensitivity Analysis………………………………………………………………….. 28

4.5. Generating Response Surface Model…………………………………………… 31

4.6. Implementing Genetic Algorithm……………………………………………….. 37

4.7. Case Study: Inter-well spacing and optimal number of wells to be drilled… 39

4.8. Result Summary……………………………………………………………….. 40

5. Conclusion and Recommendation…………………………………. 44

5.1. Summary and Conlusion…………………………………………………………… 44

5.2. Recommendation…………………………………………………………………….. 45

Nomenclature………………………………………………………. 47

Reference……………………………………. 48

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INTRODUCTION

There is a growing demand to develop petroleum reservoirs through the drilling of in-fill wells to exploit the hydrocarbon reserves not properly drained by existing producing wells.

Well placement can be referred to as all activities associated with drilling a wellbore to intercept one or more specified locations.

The term is usually used in reference to vertical, directional or horizontal wells that are oriented to maximize contact with the most productive parts of reservoirs.

As well spacing is decreased, the shifting well patterns alter the formation-fluid flow paths and increase sweep to areas where greater hydrocarbon saturations exist.

A wide well spacing will leave some oil and gas bearing sands in areas not penetrated, while a close spacing will cause some oil and gas bearing sands to be penetrated by two wells or more, causing interference and lowering the reserves drained by the wells and economic profit.

This study is done to determine the optimal locations for well placement to support field development plans.

One of the most challenging and influential problems associated with drilling in-fill wells is finding the optimum number of wells and their placement in the reservoir.

In this problem, there are many variables to consider like geological, well configurations, production variables and economic variables.

All these variables, together with reservoir geological uncertainty, make the determination of a suitable development plan for a given field difficult, since the design has to evaluate hundreds or thousands of potential infill alternatives.

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REFERENCES

Aanonsen, S. I., Eide, A. L., Holden, L. and Aasen, J. O. (1995). “Optimizing Reservoir Performance under Uncertainty with Application to Well Location,” paper SPE 30710 presented at the 1995 SPE Annual Technical Conference & Exhibition, Dallas, Texas, October 22-25.
Abukhamsin, A Y. (2009). Optimization of well design & location in a real field. Master’s Report, Department of Energy Resources Engineering, Stanford University, California.
Artus, V., Durlofsky, L. J., Onwunalu, J. E., and Aziz, K. (2006). Optimization of nonconventional wells under uncertainty using statistical proxies. Computational Geosciences, 10(4):389–404.
Badru, O., and Kabir, C. S. (2003). Well placement optimization in field development. Paper SPE 84191 presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, U.S.A., 5-6 October.
Bittencourt, A. C., and Horne, R. N. (1997). “Reservoir Development and Design Optimization”, paper SPE 38895 presented at the SPE Annual Technical Conference and Exhibition, San Antonio, TX, October 5-8.

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