Petroleum Science >2023,??Issue6:??- DOI: https://doi.org/10.1016/j.petsci.2023.05.008
Combining unscented Kalman filter and wavelet neural network for anti-slug Open?Access
文章信息
作者：Chuan Wang, Long Chen, Lei Li, Yong-Hong Yan, Juan Sun, Chao Yu, Xin Deng, Chun-Ping Liang, Xue-Liang Zhang, Wei-Ming Peng
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引用方式：Chuan Wang, Long Chen, Lei Li, Yong-Hong Yan, Juan Sun, Chao Yu, Xin Deng, Chun-Ping Liang, Xue-Liang Zhang, Wei-Ming Peng, Combining unscented Kalman filter and wavelet neural network for anti-slug, Petroleum Science, Volume 20, Issue 6, 2023, https://doi.org/10.1016/j.petsci.2023.05.008.
文章摘要
Abstract: The stability of the subsea oil and gas production system is heavily influenced by slug flow. One successful method of managing slug flow is to use top valve control based on subsea pipeline pressure. However, the complexity of production makes it difficult to measure the pressure of subsea pipelines, and measured values are not always accessible in real-time. The research introduces a technique for integrating Unscented Kalman Filter (UKF) and Wavelet Neural Network (WNN) to estimate the state of subsea pipeline pressure using historical data and a state model. The proposed method treats multiphase flow transport as a nonlinear model, with a dynamic WNN serving as the state observer. To achieve real-time state estimation, the WNN is included into the UKF algorithm to create a WNN-based UKF state equation. Integrate WNN and UKF in a novel way to predict system state accurately. The simulated results show that the approach can efficiently predict the inlet pressure and manage the slug flow in real-time using the riser's top pressure, outlet flow and valve opening. This method of estimate can significantly increase the control effect.
關(guān)鍵詞
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Keywords: State estimation; Stable control; Method fusion; Wavelet neural network; Unscented Kalman filter