Business/economics

Field-Scale Assisted History Matching Using a Systematic Ensemble Kalman Smoother

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.

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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.

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