Over the last decade the ensemble Kalman filter (EnKF) has attracted attention as a promising method for solving the reservoir history matching problem: Updating model parameters so that the model output matches the measured production data. The method possesses unique qualities such as; it provides real time update and uncertainty quantification of the estimate, it can estimate any physical property at hand. The method does, however, have its limitations; in particular it is derived based on an assumption of a Gaussianity. A recent method proposed to improve upon the original EnKF method is the Adaptive Gaussian mixture filter (AGM). The AGM loosens up the requirements of a linear and Gaussian model by making smaller linear updates and including importance weights associated with each ensemble member at computational costs as low as EnKF. In this paper we present results where the AGM algorithm is combined with localization. To validate the performance of AGM the result is compared with the EnKF, with and without localization. From the results, we are able to distinguish the performance of the different filters. In particular all the methods provide good history match, but we see that the AGM stands out by better honoring the original geostatistics.


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