History Matching with Differential Evolution Approach; a Look at New Search Strategies

Abstract

Abstract History matching and uncertainty quantification are two important aspects of modern reservoir engineering studies. Finding multiple history matched models for uncertainty quantification with fast and efficient optimization algorithms are the focus of research in assisted history matching methods. Recently a new approach for history matching has been proposed based on differential evolution optimization algorithm. Differential evolution is a very powerful optimization method with a simple structure and few tuning parameters which makes it easy to use in automatic history matching frameworks. In this paper we are looking at three new search strategies as alternates to the proposed method in previous publication for obtaining multiple history-matched reservoir models. These strategies of differential evolution are different in the way that new models are generated during automatic history matching process. The comparative study presents the differences between performances of new search schemes for a simple reservoir simulation case in Gulf of Mexico. We compare the best history matching results and sensitivity of algorithms to starting conditions. Tradeoff between speed of convergence to good fitting regions and coverage of the search space is also demonstrated for different variants of differential evolution. We show that some variants of differential evolution exhibit global searching characteristics, while other ones quickly obtain good results, saving time and computational resources in reservoir engineering studies. Final part of this paper focuses on the uncertainty of production forecasts, discussing the prediction capability of applied differential evolution algorithms.