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"Optimization of Well Placement Under Time-Dependent Uncertainty" by Umut Özdoğan

A Hybrid Genetic Algorithm (HGA) was used as the optimization method to find the best locations of the wells. In order to find the optimum

decisions for different risk attitudes, a utility framework, that enables the assessment of the uncertainty of the well-placement decisions, was used.

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Determining the optimum location of the wells is a crucial decision to be made during a field development plan. The quality of the decision is strongly dependent upon the amount of the information available to the decision-maker at the time the decision is made. Knowing that the development phase of a reservoir is a dynamic period in which different categories of information are added to system from distinct sources, one should make the well placement decisions considering these time-dependent contributions of information. This study proposes an approach that addresses the value of time-dependent information to achieve better decisions in terms of reduced uncertainty and increased

probable Net Present Value (NPV).

Through this new approach, production history data obtained from the wells, as they are drilled, are integrated into the well placement decisions. Unlike previous approaches, well placement optimization is coupled with recursive history matching steps. To test the results of the proposed approach, an example reservoir and its realizations, all of which match the history response of the example reservoir, were investigated. At each step of optimization, a reduction in the uncertainty of the multiple realizations was observed, as production history became available.

TOC

1. Introduction

2. Problem Statement

3. Optimization Tools

4. Utility Framework

5. Multiplacement with Pseudohistory

6. Analysis of Multiplacement with Pseudohistory

7. Full Optimization of Well Placement

8. Concluding Remarks

A. Program Details for the New Approach

• | •

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