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• W2040545377 abstract "Abstract Computer-aided history matching techniques are increasingly playing a role in the reservoir characterisation process. In general, these techniques provide a single solution that matches the observed data. It has been widely recognised that a solution found to a history matching problem is not unique, but alternative solutions are usually not sought, due to time and cost restrictions. This paper describes how evolutionary computation can be used to obtain multiple possible solutions without repeating the history matching process. Once a geological and structural model has been defined, Genetic Algorithms are used to carry out hydrocarbon reservoir characterisation by conditioning the reservoir simulation model to production data (history matching). The proposed approach uses ideas from Darwinian evolutionary theory, combining genetic algorithms and geostatistical modelling to generate multiple reservoir descriptions. The algorithms return a suite of solutions (corresponding to different realisations of the reservoir model) from which the reservoir team can select a representative group for further analysis. The solutions give history matches with a quality comparable to what might be achieved by hand. The method is relatively easy to implement, robust with respect to lost or corrupted solutions, cheap in terms of computational costs, and can be easily parallelised." @default.
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• W2040545377 date "2000-10-01" @default.
• W2040545377 modified "2023-09-23" @default.
• W2040545377 title "Improved Reservoir Characterization through Evolutionary Computation" @default.
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• W2040545377 doi "https://doi.org/10.2118/62942-ms" @default.
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