1887

Abstract

Summary

Optimal well placement targeting sweet spots within the reservoir is critical for oil field development. However, geological uncertainty can potentially impact the robustness of the well planning solutions and may negatively impact field development strategies. Traditional workflows may seek the optimal well locations either on one geological model or in an average manner over a set of selected geological realizations by optimizing a chosen objective function such as Net Present Value (NPV). These approaches however tend to be somewhat deficient for realistic field case studies. Firstly, traditional workflows avoid an explicit treatment of well planning variance due to geological uncertainty. Secondly, traditional optimization normally cannot meet the requirement of optimizing two or more conflicting objective functions simultaneously.

In this paper, we propose a workflow that handles geological uncertainty in a novel manner. The workflow is based on a multi-objective optimization approach using the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The mean NPV is maximized while the standard deviation is minimized simultaneously for all well placement scenarios over all geological realizations. The power and utility of the proposed workflow is demonstrated on a reservoir case study. The results indicate that the proposed approach leads to improved decision making capabilities by providing a suite of well planning solutions that can be incorporated in decision making. Moreover, this workflow demonstrates a novel treatment of geological uncertainty. It takes into account the risk attitude of decision-makers and broadens field development strategies intelligently.

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/content/papers/10.3997/2214-4609.20141902
2014-09-08
2020-04-01
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