The History Matching Problem with Geostatistical Priors in a Smooth Formulation

Abstract

We focus on minimizing the number of reservoir simulation runs and conserving geological realism of the solutions when solving the history matching problem. Geological a priori information is taken into account by means of multiple point statistics borrowed from training images (conceptual geological models). Then production data and prior information are integrated into a single differentiable objective function, minimizer of which has a high posterior value. Solving the proposed optimization problem for an ensemble of different starting models, we obtain a set of solutions honoring both data and prior information.