Conventional direct optimization methods and Evolutionary Algorithms are applied to the problem of history matching in reservoir engineering. For the optimization of complex reservoir models the potential of parallel computing is investigated. Methods to improve the convergence of Evolutionary Algorithms by introducing expert knowledge is discussed.

An interface program has been developed which links an industry standard reservoir simulator to an optimization software package designed as a multipurpose environment for parallel optimization. Permeabilities, fault transmissibilities as well as relative permeabilites and barrier locations have been included in the optimization.

Results are presented for synthetic and real reservoirs with up to 30 wells and 40 design parameters. The potential and the area of applicability of the optimization method to the problem of reservoir modeling in various modeling phases are discussed. The improvement of performance based on parallelism in a network environment is evaluated.

In conclusion, results suggest that Evolution Strategies can be successfully applied for generating possible solutions in the early modeling phase. The introduction of expert knowledge to the optimization methods is essential for reducing the multidimensional search space and improving convergence.

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