Continuous Reservoir Model Updating Using Streamline-Assisted Ensemble Kalman Filter

Abstract

Abstract The recent use of the ensemble Kalman filter EnKF for data assimilation and assessment of uncertainties in future forecast in reservoir engineering seems to be very promising. It provides ways of incorporating any type of production data or time lapse seismic information in an efficient way; however, the use of the EnKF in history matching comes with its share of challenges and concerns. The overshooting of certain values in the permeability field, possible increase in the material balance errors of the updated phase(s), and limitation to work with non-Gaussian permeability distribution are some of the most critical problems of the EnKF. The use of larger ensemble size may mitigate some of these problems but are prohibitively expensive to implement. This paper presents a conditioning technique that can be implemented with the EnKF, which eliminates or reduces the magnitude of these problems. This allows for the use of a reduced ensemble size, thereby leading to significant saving in time during field scale implementation. Our approach involves no extra computational cost and is easy to implement. Additionally the final history matched model tends to preserve most of the geological features of the initial geologic model. A quick look at the procedure is provided here. It enables implementation of this approach into current EnKF algorithms. We demonstrate the power and utility of our approach with an example.