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.
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. It is widely acknowledged that a single deterministic reservoir model is not sufficient to represent a reservoir’s complex characteristics along with its uncertainty. The underlying reason is that history matching is an ill-posed inverse problem with nonunique solutions that can match the historical data.
To overcome the nonuniqueness problem in the history-matching process, the ensemble Kalman filter (EnKF) has been introduced to the petroleum industry with many successful applications. The EnKF can be characterized as a Monte Carlo version of the classic Kalman filter in the sense that it uses an ensemble of samples to represent necessary statistics, such as covariance of model parameters and the correlations between model parameters and observations. An important feature of the EnKF method is that it sequentially assimilates observations when available to update the realizations in the ensemble, which includes the uncertain model parameters and primary model state variables. Hence, the EnKF is suitable for real-time data assimilation to update the ensemble continuously when new data are available....
Field-Scale Assisted History Matching Using a Systematic Ensemble Kalman Smoother
01 April 2017