An Adaptive Evolutionary Algorithm for History-Matching

Abstract

Abstract Efficient history matching of highly uncertain reservoir models is important in many applications of the reservoir engineering area, such as reservoir management, production prediction, and development optimisation. History matching has commonly been done manually by a tedious trial and error approach in which global and local adjustments are done to the model properties until a best model is obtained which honours production response of the reservoir model. With recent advances in computer science and hardware, assisted history matching techniques can improve the quality of the match within less time and effort. Stochastic evolutionary algorithms are popular methods in history matching and have been widely used to explore and search the global parameter search space and find multiple good fitting models. They are quite easy to implement, and if well applied, able to be globally convergent even in complex problems such as history matching. General critiques of these algorithms include high computational demands and demand for the tuning of the algorithm’s control parameters. Adaptation methods for EAs adjust algorithm’s control parameters during the evolution to improve the search quality and accelerate the convergence by guiding search toward appropriate regions of the search space. This insures a balance between exploration and exploitation properties of the algorithm. In this paper, we introduce a novel adaptive scheme for EAs, which intelligently adapts the control parameter that affects the diversity of the population. The adapted control parameter is number of selected to generated solutions in each generation, which controls the balance between exploration and exploitation. We apply proposed algorithm to optimisation problem of two test functions and history matching problem of a well-known synthetic reservoir simulation model. Our results show that, compared with the original EAs, adaptive EA is able to find better spread of fitting models with better convergence to the minimum misfit, thus more effective and efficient history matching is achieved.