Addressing the lack of measurements in the subsea environment by using a model scheduling Kalman filter coupled with a r
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ORIGINAL PAPER
Addressing the lack of measurements in the subsea environment by using a model scheduling Kalman filter coupled with a robust adaptive MPC P. A. Delou1 · M. B. de Souza Jr.2 · A. R. Secchi1 Received: 29 April 2020 / Revised: 15 October 2020 / Accepted: 17 October 2020 © Associação Brasileira de Engenharia Química 2020
Abstract Electric submersible pumps (ESPs) are one of the most widespread oil artificial lifting technologies. In the operation of an ESP there are a large number of parameters that must be monitored and held within operational constraints in order to guarantee stable and optimal operation. Manual control is subject to sub-optimal production and constant violation of operational limits, that can cause either a reduction in ESP lifetime or premature failure. Therefore, a proper automation strategy must be applied to support operators in order to ensure the best production rate with less energy cost. Previous literature has proposed the use of linear MPC based on system identification, however all relevant system variable measurements were considered available. In this paper, the problem of losing measurements of the state variables due to the aggressive subsea environment is addressed. We show that a non-adaptive single linear model strategy lacks in quality for state estimation and, therefore, a robust MPC is not possible under this configuration. In this work, an adaptive constrained MPC coupled with a model scheduling Kalman filter (MSKF) is proposed. Two model scheduling strategies based on linear interpolation of a pre-set number of local models are proposed and compared to successive linearization at every sampling time, based on Taylor series expansion of the nonlinear model. All strategies guarantee model accuracy and model stability over the whole operational range. The proposed scheduling strategies presented similar performance compared to the successive linearization strategy, avoiding the need of obtaining a local linear model at each sampling time. Keywords Electric submersible pump (ESP) · Model predictive control (MPC) · Kalman filter · Adaptive control · Model scheduling List of symbols A1 Cross-section area of pipe below ESP A2 Cross-section area of pipe above ESP Cc Choke valve constant D1 Pipe diameter below ESP D2 Pipe diameter above ESP f ESP frequency f0 ESP characteristics ref. freq.
* P. A. Delou [email protected] 1
Chemical Engineering Program, COPPE, Universidade Federal do Rio de Janeiro, Av. Horácio Macedo, 2030, Centro de Tecnologia, Bloco G, Sala G‑115, Rio de Janeiro, RJ CEP 21941‑914, Brazil
Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 149, Centro de Tecnologia, Bloco E, Sala E‑201, Rio de Janeiro, RJ CEP 21941‑909, Brazil
2
F1 Frictional pressure drop below ESP F2 Frictional pressure drop above ESP g Gravity constant h1 Height from reservoir to ESP hw Total vertical distance in well H Head developed by ESP I Electric current in ESP motor Inp
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