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<p>This paper presents the real-time implementation of a nonlinear model predictive control (NMPC) strategy integrated with nonlinear estimation techniques, aimed at maximizing the production of an artificial lift system operated by an electrical submersible pump (ESP). The NMPC integrates a soft sensor into the control loop to estimate difficult-to-measure variables, thereby improving the accuracy of the controller’s internal model. Additionally, the strategy includes a zone control scheme to handle typical operational constraints of ESP systems, such as variations in downthrust and upthrust over time. The algorithm was developed using the open-source CasADi platform and validated in real-time on an ESP pilot plant. This work also presents an experimental comparison between the NMPC+NMHE and NMPC+EKF strategies, evaluating performance, estimation accuracy, and computational effort. The experimental results demonstrate that the proposed controller effectively maintains the pilot plant operation within a safe and feasible region while optimizing production, even under challenging conditions such as dynamic model uncertainties, parametric variations, and process noise. Both estimation strategies exhibit satisfactory performance; however, NMPC+NMHE provides lower estimation variability, whereas NMPC+EKF offers lower computational cost.</p>

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Implementation of an extended NMPC controller integrated with nonlinear state estimators in an oil well pilot plant with electrical submersible pump installations

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Odilon S. L. de Abreu,
  • Leizer Schnitman,
  • Marcos Pellegrini Ribeiro,
  • Márcio A. F. Martins

摘要

This paper presents the real-time implementation of a nonlinear model predictive control (NMPC) strategy integrated with nonlinear estimation techniques, aimed at maximizing the production of an artificial lift system operated by an electrical submersible pump (ESP). The NMPC integrates a soft sensor into the control loop to estimate difficult-to-measure variables, thereby improving the accuracy of the controller’s internal model. Additionally, the strategy includes a zone control scheme to handle typical operational constraints of ESP systems, such as variations in downthrust and upthrust over time. The algorithm was developed using the open-source CasADi platform and validated in real-time on an ESP pilot plant. This work also presents an experimental comparison between the NMPC+NMHE and NMPC+EKF strategies, evaluating performance, estimation accuracy, and computational effort. The experimental results demonstrate that the proposed controller effectively maintains the pilot plant operation within a safe and feasible region while optimizing production, even under challenging conditions such as dynamic model uncertainties, parametric variations, and process noise. Both estimation strategies exhibit satisfactory performance; however, NMPC+NMHE provides lower estimation variability, whereas NMPC+EKF offers lower computational cost.