History Matching With an Ensemble Kalman Filter and Discrete Cosine Parameterization

Abstract

AbstractHistory matching of large hydrocarbon reservoirs is challenging due to several reasons including: 1) Scarcity of available measurements relative to the number of unknowns, leading to an ill-posed inverse problem; 2) Computational effort required for large reservoir problems; 3) The need to insure that solutions are geologically realistic. All of these problems can be helped by using algorithms that rely on efficient and parsimonious descriptions (or parameterizations) of reservoir properties. This paper combines a novel parameterization approach, the discrete cosine transform, with a recursive history matching technique, the ensemble Kalman filter, to provide efficient estimation of unknown geological properties in large reservoirs. The application and generality of this approach is demonstrated using two waterflooding experiments characterized by different types of geological variability.