Error behavior modeling in Capacitance-Resistance Model: A promotion to fast, reliable proxy for reservoir performance prediction

Abstract

Using the original form of Capacitance-Resistance Model (CRM), as a waterflooding performance prediction tool, for modeling real reservoirs makes some unavoidable errors. Combination of this model with available data assimilation methods yields more powerful simulation tool with updating parameters over time. However, the inherent uncertainty arisen by modeling complex reservoirs with only a limited number of CRM parameters is not addressed yet. In this study, the model error behavior has been simulated through a physically-based dynamical system in which it has been correlated with the original model parameters. The ensemble-based Kalman filter (EnKF) data assimilation method has been employed to practice observation data. To show the validity of the developed CRM-Error system, we have employed it to replicate the data obtained from a synthetic model of an Iranian reservoir. Results show that acceptable ranges for the production rates have been achieved via this model in comparison with observed data.