This work presents a systematic and rigorous approach of reservoir decomposition combined with the ensemble Kalman smoother to overcome the complexity and computational burden associated with history matching field-scale reservoirs in the Middle East. The paper provides the formulation of the iterative regularizing ensemble Kalman smoother, introduces the use of streamline maps to facilitate domain decomposition, and presents a discussion on covariance localization. Computational-efficiency problems are addressed by three levels of parallelization.
Introduction
History matching, in which uncertain parameters are chosen so the reservoir model can reproduce the historical field performance, plays a key role in field development. Several techniques have been developed in the past decades to address the history-matching problem.
