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

摘要

Recently, 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 spe-cific knowledge of simulator numerics is required for im-plementation of the EnKF. Moreover, data are assimilated (matched) as they become available; a suite of plausible reservoir models (the set of ensembles) is continuously up-dated to honor data without rematching data assimilated previously. Because of these features, the method is far more efficient for history matching dynamic data than au-tomatic history matching algorithms based on optimiza-tion 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 be-tween randomized maximum likelihood and the ensemble Kalman filter. We also consider examples where the per-formance of the EnKF does not provide a reliable char-acterization of uncertainty in the model or performance predictions. Although we have previously generated reser-voir characterization examples where the method worked well, here we also provide examples where the performance of EnKF does not provide a reliable characterization of un-certainty.