History Matching Channelized Reservoirs Using the Ensemble Kalman Filter

Abstract

Summary Even though the ensemble Kalman filter (EnKF) is widely used, history matching reservoirs with facies description has proven to be a major challenge. A preferred technique for estimating large scale facies fields within the petroleum industry is still missing. In this paper, we present a new approach to this problem. Instead of applying the EnKF directly to facies realizations, the approach applies a transformation of facies fields to a specific level set function, representing distances between facies types. This ensures better agreement with the EnKF Gaussianity assumptions, and the method always returns facies realizations with geological authenticity. The method also offers large flexibility in generating the initial ensemble, which can be performed using any geostatistical tool. Furthermore, no modifications of the standard EnKF equations are needed. The methodology is evaluated on two synthetic examples with increasing complexity. In both examples, we consider reservoirs with channel structure. The results presented show that the updated models give large improvements in matching the measurements, and the uncertainty of the models is decreased. Further, recovery of the true petrophysical parameters is highly dependent on sufficient information in the measurements, but in one of the examples considered we are able to completely recover the true channel structure. Additional improvements in the quality of the updated facies fields are obtained by proper handling of the distances close to the reservoir boundaries, and by conditioning on specific statistical measures to better preserve prior information about channel properties.