Optimal Subsurface Flow Management Using Model Predictive Control Techniques

Recent technological advancements in reservoir management, namely the use of smart-wells, i.e., controlled valves deployed downhole, has made significant impact in the improvement of oil recovery for depleted as well as new oil fields. Inspired by classical feedback control techniques, the oil industry started developing closed-loop optimal reservoir management schemes using optimal control and model updating. Optimization techniques, such as model predictive control (MPC) has been successfully applied in the downstream end of the production line, and in general in the process industry. This is due to the fact that MPC is a model based controller design procedure, which can handle processes with stability issues and time-delays, together with a framework to incorporate constraints into the design. However, in the upstream side of the production, MPC has not gained attention until recently, mainly due to the large-scale nature of the optimization problem. In this paper, we apply real-time optimal control techniques to reservoir management, and in particular to reservoir production. Based on the success of model-based optimization to the process industry, we aim to use MPC schemes to increase the potential for greater oil recovery, and therefore, enhanced reservoir management and profitability. MPC offers a robust control implementation together with constraint handling capabilities. At first, a short survey on MPC is presented and the dynamics of an oil-reservoir is introduced together with the basic equations for flow in porous media. Then, linear MPC is applied and the focus is directed to the generation of low-order reservoir models using subspace identification methods. Lastly, due to the highly nonlinear behavior of the reservoir models, nonlinear MPC (NMPC) schemes is suggested. Comparisons will be provided through a set of realistic simulations using an in-house simulator.