Optimization of oil reservoir models using tuned evolutionary algorithms and adaptive differential evolution

In the petroleum industry, accurate oil reservoir models are crucial in the decision making process. One critical step in reservoir modeling is History Matching (HM), where the parameters of a reservoir model are adjusted in order to improve its accuracy and enhance future prediction. Recent works applied evolutionary algorithms (EAs) such as GA, DE and PSO for the HM problem, but they have been limited to classical versions of these algorithms. A significant obstacle to applying EAs to HM is that each call to the fitness function requires an expensive simulation, making it difficult to tune the control parameters for EAs in order to obtain the best performance. We apply and evaluate state-of-the-art, adaptive differential algorithms (SHADE and jDE), as well as non-adaptive evolutionary algorithms (standard DE, PSO) that have been tuned using standard black-box benchmark functions as training instances. Both of these approaches result in significant improvements compared to standard methods in the HM literature. We also apply fitness distance correlation analysis to the search space explored by our algorithms in order to better understand the landscape of the HM problem.