Optimal Reservoir control using nonlinear MPC and ECLIPSE

submitted to SPE Intelligent Energy Conference and Exhibition Amsterdam, The Netherlands, February 25–27, 2008 Abstract In this paper, we present an algorithm for optimizing reservoir production using smart well technology. The term smart well is used to indicate a nonconventional well equipped with downhole inflow control devices (ICVs) and instrumentation. This additional instrumentation extends the degree of freedom in the field production planning, since production can be efficiently distributed on the different well segments available. By proper utilization of the ICVs through optimal production planning, an increase in the oil recovery factor for the reservoir can be expected. We propose a method for optimal closed-loop production known from control theory as model predictive control (MPC). A commercial reservoir simulator, ECLIPSE, is used for modelling and predictions. MPC is chosen for its ability to provide an optimal solution for the constrained multivariable control problem. To compute the optimal ICV settings, we propose using a nonlinear MPC (NMPC) application, which can handle the severe nonlinearities found in reservoir models. The NMPC uses a single shooting, multistep, quasi-Newton (SSMQN) method to solve the optimization problem. As the term multistep suggests, this is an iterative method, which solves a sequence of quadratic problems (QPs) in each timestep. We apply our method to a benchmark reservoir model with multiple geostatistical realizations. This model has already proven potential for increased oil recovery by using optimizarion techniques. We show an even additional increase over the former approach in production totals, using the SSMQN method, with as much as 68% increase in one case, and 30% on average compared to the uncontrolled reference case.In this paper, we present an algorithm for optimizing reservoir production using smart well technology. The term smart well is used to indicate a nonconventional well equipped with downhole inflow control devices (ICVs) and instrumentation. This additional instrumentation extends the degree of freedom in the field production planning, since production can be efficiently distributed on the different well segments available. By proper utilization of the ICVs through optimal production planning, an increase in the oil recovery factor for the reservoir can be expected. We propose a method for optimal closed-loop production known from control theory as model predictive control (MPC). A commercial reservoir simulator, ECLIPSE, is used for modelling and predictions. MPC is chosen for its ability to provide an optimal solution for the constrained multivariable control problem. To compute the optimal ICV settings, we propose using a nonlinear MPC (NMPC) application, which can handle the severe nonlinearities found in reservoir models. The NMPC uses a single shooting, multistep, quasi-Newton (SSMQN) method to solve the optimization problem. As the term multistep suggests, this is an iterative method, which solves a sequence of quadratic problems (QPs) in each timestep. We apply our method to a benchmark reservoir model with multiple geostatistical realizations. This model has already proven potential for increased oil recovery by using optimizarion techniques. We show an even additional increase over the former approach in production totals, using the SSMQN method, with as much as 68% increase in one case, and 30% on average compared to the uncontrolled reference case.