  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.
