Optimization of Smart Well Production Through Nonlinear Model Predictive Control

Optimization of Smart Well Production Through Nonlinear Model Predictive Control

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In this paper, we present an algorithm for optimizing reservoir production using smart well technology. The term smart well is used to indicate an unconventional well equipped with down hole inflow control valves (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 increased oil recovery 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 modeling 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 multi-step 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 time step.
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In this paper, we present an algorithm for optimizing reservoir production using smart well technology. The term smart well is used to indicate an unconventional well equipped with down hole inflow control valves (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 increased oil recovery 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 modeling 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 multi-step 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 time step.

 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 optimization 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 a reference case.

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