A Novel Approach for Combining Multiple-point Statistics and Production Data in Reservoir Characterization

Very often history-matched models happen to be inconsistent with the prescribed complex geological model. Therefore, in recent years the need for use of multiple-point statistics as prior information in reservoir characterization problems became evident. In traditional approach one often looks only for a maximum likelihood solution in the space of models allowed by prior. However, in this case, the prior probability of the model is not quantified and, hence the consistency with prior is not guaranteed. In this study we apply a multiple-point statistics framework recently developed in our research group for the solution of history matching problem. The nature of the approach allows us to unite training-image based statistical information and production data in one objective function and guide the solution towards the maximum a posteriori model. As an example, we consider a 3D synthetic reservoir with channel-like sedimentary structures. Applying the suggested framework, we show the obtained solution to be consistent with desirable accuracy both with prior information and data observations.