Principle component analysis methods for covariance localization in the ensemble kalman filter history matching

History matching is a widely used reservoir simulation workflow. Its goal is to create models which reasonably match historical field injection and production data so future predictions can be made. Many methods have been developed in the past to try to solve this problem. One set of methods that have been those that involve ensemble data assimilation. An example is the Ensemble Kalman Filter (EnKF), which has been widely implemented. A key issue with ensemble methods is that Under sampling can severely degrade the reliability of the estimation. In this paper we introduce a new method to improve the result quality in ensemble data assimilation methods.