Nonlinear Model Predictive Control of Steam-Assisted-Gravity-Drainage Well Operations for Real-Time Production Optimization

Abstract

Summary In deep oil-sands deposits using the steam-assisted-gravity-drainage (SAGD) recovery process, real-time optimization (RTO) involves controlling optimum subcool to ensure steam conformance. Contemporary workflows use linear model predictive control (MPC) with oversimplified models that are inadequate to represent highly complex, spatially distributed, and nonlinear reservoir dynamics. In this research, two novel workflows using nonlinear MPC (NMPC) are proposed. The first workflow reduces an NMPC problem to linear MPC by estimating an equivalent linear model of a nonlinear black-box model in a mean-square-error sense. Another approach is to use nonlinear dynamic models explicitly for accurate prediction of the plant states and/or outputs. The resulting nonconvex, nonlinear cost optimization problem is solved using an interior-point algorithm at each control interval. Proposed workflows are tested using the history-matched, field-scale model of a SAGD reservoir located in northern Alberta, Canada. Qualitative and quantitative analysis of the results reveals that nonlinear black-box models based on system identification theory can successfully capture the nonlinearity of the SAGD process. Also, both workflows can control the subcool above the desired set-point while ensuring stable well operations. More than a 24% increment is achieved in net present value (NPV) using proposed NMPC workflows compared with the field operations with no closed-loop control. Overall, NMPC can successfully be used for improved RTO, energy efficiency, and greenhouse gas emissions while considering available surface facilities and well configurations.