A Closer Look At Differential Evolution For The Optimal Well Placement Problem

TL;DR

This paper evaluates the effectiveness of Differential Evolution (DE) in optimizing well placement for hydrocarbon reservoirs, demonstrating its ability to find high-quality solutions and analyzing how different DE configurations affect performance.

Contribution

It provides a comprehensive analysis of DE configurations for the well placement problem using benchmark datasets and simulation tools, highlighting its potential in real-world applications.

Findings

DE can find high-quality well placement solutions

DE performance is influenced by configuration parameters

Preliminary results guide optimal DE parameter settings

Abstract

Energy demand has increased considerably with the growth of world population, increasing the interest in the hydrocarbon reservoir management problem. Companies are concerned with maximizing oil recovery while minimizing capital investment and operational costs. A first step in solving this problem is to consider optimal well placement. In this work, we investigate the Differential Evolution (DE) optimization method, using distinct configurations with respect to population size, mutation factor, crossover probability, and mutation strategy, to solve the well placement problem. By assuming a bare control procedure, one optimizes the parameters representing positions of injection and production wells. The Tenth SPE Comparative Solution Project and MATLAB Reservoir Simulation Toolbox (MRST) are the benchmark dataset and simulator used, respectively. The goal is to evaluate the performance of DE in solving this important real-world problem. We show that DE can find high-quality solutions, when compared with a reference from the literature, and a preliminary analysis on the results of multiple experiments gives useful information on how DE configuration impacts its performance.