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.

中文翻译：

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通过使用模型调度卡尔曼滤波器与稳健的自适应 MPC 相结合，解决海底环境中缺乏测量的问题

电潜泵 (ESP) 是应用最广泛的石油人工举升技术之一。在 ESP 的运行中，必须监控大量参数并将其保持在运行限制范围内，以保证稳定和最佳运行。手动控制受到次优生产和不断违反操作限制的影响，这可能导致 ESP 寿命缩短或过早失效。因此，必须应用适当的自动化策略来支持操作员，以确保以更少的能源成本获得最佳生产率。以前的文献建议使用基于系统识别的线性 MPC，但是所有相关的系统变量测量都被认为是可用的。在本文中，解决了由于侵蚀性海底环境而丢失状态变量测量值的问题。我们表明，非自适应单线性模型策略缺乏状态估计的质量，因此，在这种配置下不可能实现稳健的 MPC。在这项工作中，提出了一种自适应约束 MPC 与模型调度卡尔曼滤波器 (MSKF) 相结合。基于非线性模型的泰勒级数展开，提出了两种基于预设数量局部模型线性插值的模型调度策略，并与每个采样时间的连续线性化进行比较。所有策略都保证了整个操作范围内的模型准确性和模型稳定性。与连续线性化策略相比，所提出的调度策略表现出相似的性能，