History-matching with Ensemble-Based Methods: Application to an Underground Gas Storage Site

Abstract This paper shows the application of two ensemble-based assimilation methods, the Ensemble Kalman filter (EnKF) and the Ensemble Smoother (ES), to constrain an underground gas storage site to well pressure data. The EnKF is a sequential data assimilation method that provides an ensemble of models constrained to dynamic data. It entails a two-step process applied any time data are collected. First, production responses are computed for every model within the ensemble until the following acquisition time. Second, models are updated using the Kalman filter to reproduce the data measured at that time. The EnKF has been widely applied in petroleum industry. More recently, the ES was successfully applied to a real field case. This method is also based on the Kalman filter, but the update is performed globally over the entire history-matching period: values simulated at assimilation times are considered all together in the update step. The uncertain parameters considered here are the porosity and horizontal permeability values populating several layers of the geological model. Applying both the ES and EnKF methods, the spread within the ensembles is reduced and the predictions based on the ensembles of updated petrophysical distributions get closer to the pressure data corresponding to the history-matching and prediction periods.