Parallelization of Ensemble Kalman Filter (EnKF) for Oil Reservoirs

This thesis describes the design and implementation of a parallel algorithm for data assimilation with ensemble Kalman filter (EnKF) for oil reservoir management. The implemented application works on large number of observations from time-lapse seismic, which lead to a large turnaround time for the analysis step, in addition to the time consuming simulations of the realizations. Provided that parallel resources are used for the parallel simulations of the realizations, the analysis step also deserves parallelization. Our experiments show that parallelization of the analysis step in addition to the forecast step also scales well, exploiting the same set of resources with some additional efforts.