Gap Metric Based Performance Assessment of Subcool Control in Steam Assisted Gravity Drainage Wells

Abstract

Steam assisted gravity drainage (SAGD) is a widely adopted oil extraction technique for heavy oil reservoirs in Alberta, Canada. One of the common approaches by which the producers optimize the production from SAGD reservoirs is by controlling the emulsion level above the producer well bores, a strategy known as subcool control within the industry. In this study, we assess and compare performances of two subcool control strategies, one of which makes use of classic control strategy (PID) and the other is of advanced control strategy (model predictive controller (MPC)). As the controlled process in this case is a non-linear process, we propose a gap metric-based control performance assessment (CPA) method. By this method, the local models as well as their associated weights are determined using the gap metric. We show that the MPC-based strategy outperforms the PID loops-based strategy in subcool control application.