Hybrid Ensemble Kalman Filter With Coarse-Scale Constraint for Nonlinear Dynamics

摘要

Interest in ensemble Kalman filters (EnKFs) is driven by the need for continuous reservoir-model updating and uncertainty assessments based on dynamic data. The EnKF approach relies on sample-based statistics derived from an ensemble of reservoir-model realizations. Sampling error in these statistics, particularly with the use of modest ensemble sizes, can degrade EnKF performance severely, leading to parameter overshoots and filter divergence. The proposed hybrid-multiscale EnKF improved operational-data assimilation and helped overcome many limitations associated with the classical EnKF implementation.