History- Matching and Forecasting Production Rate and Bottomhole Pressure Data Using an Enhanced Physics- Based Data-Driven Simulator

In this study, we present a novel application of our newly developed physics -based data-driven interwell numerical simulator (INSIM) referred to as INSIM- BHP to history match highly variable real -life (oscillatory) oil rate and bottomhole pressure (BHP) data acquired daily in multiperforated wells produced from an oil reservoir with bottomwater drive mechanism. INSIM- BHP provides rapid and accurate computation of well rates and BHPs for history matching, forecasting, and production optimization purposes. It delivers precise BHP calculations under the influence of a limited aquifer drive mechanism. Our new version represents the physics of two -phase oil -water flow more authentically by incorporating a harmonic -mean transmissibility computation protocol and including an arithmetic -mean gravity term in the pressure equation. As the specific data set considered in this study contains a sequence of highly variable oil rate and BHP data, the data density requires INSIM- BHP to take smaller than usual timesteps and places a strain on the ensemble-smoother multiple data assimilation (ES- MDA) history-matching algorithm, which utilizes INSIM- BHP as the forward model. Another new feature of our simulator is the use of time-variant well indices and skin factors within the simulator's well model to account for the effects of well events on reservoir responses such as scaling, sand production, and matrix acidizing. Another novel modification has been made to the wellhead term calculation to better mimic the physics of flow in the wellbore when the production rate is low, or the well(s) is(are) shut in. We compare the accuracy of the history-matched oil rate and BHP data and forecasted results as well as computational efficiency for history matching and future prediction by INSIM- BHP with those from a high-fidelity commercial reservoir simulator. Results show that INSIM-BHP yields accurate forecasting of wells' oil rates and BHPs on a daily level even under the influence of oscillatory rate schedules and changing operational conditions reflected as skin effects at the wells. Besides, it can help diagnose abnormal BHP measurements within simulation runs. Computational costs incurred by INSIM- BHP and a high-fidelity commercial simulator are evaluated for the real data set investigated in this paper. It has been observed that our physics-based, data-driven simulator is about two orders of magnitude faster than a conventional high-fidelity reservoir simulator for a single forward simulation. The specific field application results demonstrate that INSIM- BHP has great potential to be a rapid approximate capability for history matching and forecasting workflow in the investigated limited-volume aquifer-driven development.