Assessing the Uncertainty in Reservoir Description and Performance Predictions with the Ensemble Kalman Filter

Abstract

AbstractRecently, the ensemble Kalman filter (EnKF) has gained popularity in atmospheric science for the assimilation of data and the assessment of uncertainty in forecasts for complex, large-scale problems. A handful of papers have discussed reservoir characterization applications of the EnKF, which can easily and quickly be coupled with any reservoir simulator. Neither adjoint code nor specific knowledge of simulator numerics is required for implementation of the EnKF. Moreover, data are assimilated (matched) as they become available; a suite of plausible reservoir models (the set of ensembles) is continuously updated to honor data without rematching data assimilated previously. Because of these features, the method is far more efficient for history matching dynamic data than automatic history matching algorithms based on optimization algorithms. Moreover, the suite of ensembles provides a way to evaluate the uncertainty in reservoir description and performance predictions.Here we establish a firm theoretical relation between randomized maximum likelihood and the ensemble Kalman filter. We also consider examples where the performance of the EnKF does not provide a reliable characterization of uncertainty in the model or performance predictions. Although we have previously generated reservoir characterization examples where the method worked well, here we also provide examples where the performance of EnKF does not provide a reliable characterization of uncertainty.