Evolutionary algorithms applied to history matching of complex reservoirs

Conventional direct optimization methods and evolutionary algorithms are applied to the problem of history matching in reservoir engineering. The advantage of parallel computing for the optimization of complex reservoir models is investigated. Methods to improve the convergence of evolutionary algorithms by introducing prior information are applied. The potential of using optimization methods for the problem of reservoir modeling in various modeling phases is discussed. The methodology is illustrated on realistic simulation cases. In conclusion, results suggest that evolution strategies can be applied successfully to generate possible solutions in the early modeling phase.