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The ensemble Kalman filter (EnKF) has been successfully implemented to assimilate data in reservoir history matching problems. In the EnKF method, a suite of reservoir models (set of ensemble members) runs independently forward in time (forecast step), and is continuously updated as new data becomes available (analysis step). In this paper, an efficient implementation of the EnKF is presented in which three-level parallelization is employed.

The first level of parallelization is during the forecast step, where each ensemble member runs on a separate processor. This is very efficient for a large number of ensemble members, but without additional parallelization, the memory of a single processor constrains the size of the reservoir simulation. Therefore, a second level of parallelization which uses a parallel reservoir simulator for each realization is implemented. The analysis step requires collecting a state …