Ensemble-based methods have proved to be effective in calibrating multiple reservoir models with historical production data. However, because of the complex nature of hydrocarbon reservoirs, the model calibration is always a simplified version of reality with coarse representation and unmodeled physical processes. This flaw in the model that causes mismatch between actual observations and simulated data when “perfect” model parameters are used as model input is known as model error. The results of the authors’ research showed a promising benefits from use of a systematic procedure of model diagnostics, model improvement, and model-error quantification during data assimilations.
Introduction
For reservoir history matching using ensemble-based methods, typically the model error is either ignored or treated by inflating the observation error covariance beyond the actual measurement errors.
