Search Operators for Genetic Algorithms Applied to Well Positioning in Oil Fields

Optimizing production strategies for oil extraction is not a simple task, mainly due to the large number of variables and uncertainties associated with the problem. Metaheuristics are well-known tools that can be easily applied to this type of problem. However, the large amount of objective function evaluations that such tools require to obtain a good solution is a serious drawback in the context of oil production strategy definition (PSD): the evaluation of a production strategy requires the use of oil field simulation software and each simulation can take hours to complete. Thus, in this work a modified version of a steady-state genetic algorithm is proposed, together with specific recombination, mutation and local search operators specifically tailored for the PSD problem, which aim to reduce the computational cost of the optimization process. The developed algorithm was used to optimize the well positions in a production strategy for a synthetic oil reservoir model and the results were compared with those obtained by a classical genetic algorithm and by a commercial optimization tool.