Simultaneous estimation of relative permeability and capillary pressure for PUNQ-S3 model with a damped iterative ensemble Kalman Filter technique

A damped iterative ensemble Kalman filter (IEnKF) algorithm has been proposed to estimate relative permeability and capillary pressure curves simultaneously for the PUNQ-S3 model, while its performance has been compared with that of the CMOST module. The power-law model is employed to represent the relative permeability and capillary pressure curves, while three-phase relative permeability for oil phase is determined by using the modified Stone II model. By assimilating the observed production data, the relative permeability and capillary pressure curves are inversely, automatically, and successively updated, achieving an excellent agreement with the reference cases. Not only are the associated uncertainties reduced significantly during the updating process, but also each of the updated reservoir models predicts the production profile that is in a good agreement with the reference cases. Although both the damped IEnKF and CMOST generate similar history matching results and prediction performance, the estimation accuracy of the damped IEnKF method developed in this study is generally much better than that of the CMOST. Besides, the variations in the ensemble of the updated reservoir models and production profiles of the damped IEnKF provide a robust and consistent framework for uncertainty analysis.