Proxy Modeling of the Production Profiles of SAGD Reservoirs Based on System Identification

Abstract

Large scale physics-based reservoir models are employed routinely in the prediction of the behavior of steam assisted gravity drainage (SAGD) processes under different operational situations. However, parametric uncertainty persists in these models even after history matching with production data. This uncertainty, and the computational cost associated with the full-scale reservoir simulations, makes it challenging to use reservoir simulators in closed-loop control of reservoirs. As an alternative strategy, we present in this work a dynamic proxy model for the reservoirs based on system identification and the prediction error method using only injection and production data. These proxy models are validated against field data from a SAGD reservoir and simulated synthetic reservoir data and shown to be appropriate for use in model predictive control. We also provide evidence that the predictive power of these models can be improved by the appropriate design of input signals (injection rates and pressures).