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Owing to its simplicity and efficiency, the ensemble Kalman filter (EnKF) is being used to assimilate static and dynamic measurements to continuously update reservoir properties and responses. Many EnKF implementations have shown promising results even when applied to multiphase flow history matching problems. A Gaussian density for model parameters and state variables is an implicit requirement for obtaining satisfactory estimates through the EnKF or its variants. The EnKF may not work properly when the relationship between model parameters, state variables, and observations are strongly nonlinear and the resulting joint probability distribution is non-Gaussian. For instance, near the displacement front of an immiscible flow, use of the EnKF to directly update saturation may lead to non-physical results. In this work, we address the non-Gaussian effect through a change in parameterization. Instead of …