A gathered EnKF for continuous reservoir model updating

We propose a gathered EnKF method in which some data are stored instead of being assimilated immediately. Model updating is done only if the gathered data volume exceeds a specified threshold, or if the observations diverge significantly from prior ensemble predictions. A theoretical prediction of the effects of gather size is given and tested with a scalar case. It is shown that, for a linear dynamic model, sampling errors are reduced by gathering the observations. The gathered EnKF is further tested with 2D synthetic reservoir models and compared with a standard EnKF, indicating that the gathered EnKF may be more stable than the traditional EnKF and may save computational effort in simulation restarts, file transfers, and input-output at EnKF assimilation steps - even for nonlinear reservoir dynamics models.