Chance-Constrained Model Predictive Control for SAGD Process Using Robust Optimization Approximation

Abstract

Control of a steam-assisted gravity drainage (SAGD) process is a challenging task, because of the presence of various uncertainties, such as geological uncertainty and steam quality uncertainty. They often lead to constraint violations and performance degradation. In this work, a chance-constrained model predictive control (CCMPC) method is presented to generate a safe and optimal control strategy, considering the presence of uncertainties. A novel robust optimization method is applied to solve the chance-constrained optimization problem under general distribution of uncertainties. Two case studies are presented to demonstrate the proposed approach. Furthermore, the modeling of SAGD process is discussed, and the proposed robust optimization-based CCMPC is tested using a reservoir simulator (Petroleum Experts) of the SAGD process. The proposed approach reduces constraint violations that are due to uncertainties and achieves satisfactory performance.