Sucker rod pump, Model predictive control, Optimization, Reservoir modeling
This work enables accelerated fluid recovery in oil and gas reservoirs by automatically controlling fluid height and bottomhole pressure in wells. Several literature studies show significant increase in recovered oil by determining a target bottomhole pressure but rarely consider how to control to that value. This work enables those benefits by maintaining bottomhole pressure or fluid height. Moving Horizon Estimation (MHE) determines uncertain well parameters using only common surface measurements. A Model Predictive Controller (MPC) adjusts the stroking speed of a sucker rod pump to maintain fluid height. Pump boundary conditions are simulated with Mathematical Programs with Complementarity Constraints (MPCCs) and a nonlinear programming solver finds a solution in near real-time. A combined rod string, well, and reservoir model simulate dynamic well conditions, and are formulated for simultaneous optimization by large-scale solvers. MPC increases cumulative oil production vs. conventional pump off control by maintaining an optimal fluid level height.
Original Publication Citation
BYU ScholarsArchive Citation
Hansen, Brigham; Tolbert, Brandon; Vernon, Cory; and Hedengren, John, "Model Predictive Automatic Control of Sucker Rod Pump System with Simulation Case Study" (2019). Faculty Publications. 4162.
Ira A. Fulton College of Engineering and Technology
© 2019 Elsevier Ltd. All rights reserved. This is the author's submitted version of this article. The definitive version can be found at https://www.sciencedirect.com/science/article/abs/pii/S0098135418304526
Copyright Use Information