A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Waterflooding Performance

Abstract

Summary We develop and use a new data-driven model for assisted history matching of production data from a reservoir under waterflood and apply the history-matched model to predict future reservoir performance. Although the model is developed from production data and requires no prior knowledge of rock-property fields, it incorporates far more fundamental physics than that of the popular capacitance–resistance model (CRM). The new model also represents a substantial improvement on an interwell-numerical-simulation model (INSIM) that was presented previously in a paper coauthored by the latter two authors of the current paper. The new model, which is referred to as INSIM-FT, eliminates the three deficiencies of the original data-driven INSIM. The new model uses more interwell connections than INSIM to increase the fidelity of history matching and predictions and replaces the ad hoc computation procedure for computing saturation that is used in INSIM by a theoretically sound front-tracking procedure. Because of the introduction of a front-tracking method for the calculation of saturation, the new model is referred to as INSIM-FT. We compare the performance of CRM, INSIM, and INSIM-FT in two synthetic examples. INSIM-FT is also tested in a field example.