This paper presents a novel approach in automatic history matching using a differential evolution algorithm. Differential evolution is becoming a popular global optimization method and has been widely applied in many challenging engineering problems outside the oil industry. Some advantages of differential evolution are its simplicity in structure which leads to ease of coding and straightforward parallelisation, and its few control parameters making it easy to use in an operational context. These advantages make it important to evaluate differential evolution for use in automatic history matching frameworks where reservoir engineers require a powerful, yet easy to work tool with little effort to find optimum algorithm parameters.

We have applied differential evolution approach to obtain history matched models of a reservoir simulation case in Gulf of Mexico. The effect of different tuning parameters on the performance of differential evolution has been studied and the algorithms sensitivity to initial starting conditions investigated. Results confirm that differential evolution is a good optimization tool in obtaining multiple good history matched models. We have also compared the match quality of models obtained using differential evolution with the Neighbourhood Algorithm (NA). This comparison shows that differential evolution obtains comparable or better models in terms of matching misfit.

Finally, we studied the effect of the number of observation data points used in history matching with differential evolution algorithm. We show how additional information helps the algorithm in finding the promising regions of the search space and narrowing down the uncertainty intervals of the predictions.

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