Nonlinear Model Predictive Control for Optimal Operation of Electric Submersible Pump Lifted Oil Field

Roshan Sharma and Bjørn Glemmestad


Nonlinear MPC, nonlinear programming, optimization, control


In this work, optimal operation of an Electric Submersible Pump (ESP) lifted oil field is developed by using a Nonlinear Model Predictive Control (NMPC) framework. NMPC is used not only for setpoint tracking but also as an economic optimizer for maximizing profit. Optimal control problem is formulated as a general nonlinear programming (NLP) problem with process constraints. The scope of the NMPC framework is to operate the ESP of each oil well inside its designated operating window by minimizing the cost of the total power consumed by all the ESPs in the field and the cost of operating the separator. In other words, the speed of the ESPs and the production choke valves opening should be optimally chosen for maximizing the profit. At the same time, the capacity of the separator should not be exceeded. The simulation results show that the production choke valves are always 100\% opened to maintain optimal fluid flow rate from the reservoir. With no constraints in the top side facility (separator capacity), it is optimal to run the ESP pumps in the upper right corner of the allowed operating window for the ESPs.

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