Petroleum Science >2022,??lssue 2:??- DOI: https://doi.org/10.1016/j.petsci.2021.09.012
Adaptive fault diagnosis of sucker rod pump systems based on optimal perceptron and simulation data Open?Access
文章信息
作者：Xiao-Xiao Lv, Han-Xiang Wang, Zhang Xin, Yan-Xin Liu, Peng-Cheng Zhao,
作者單位：
投稿時(shí)間：
引用方式：Xiao-Xiao Lv, Han-Xiang Wang, Zhang Xin, Yan-Xin Liu, Peng-Cheng Zhao, Adaptive fault diagnosis of sucker rod pump systems based on optimal perceptron and simulation data, Petroleum Science, Volume 19, Issue 2, 2022, Pages 743-760,
文章摘要
Abstract
A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system (SRPS) used in oil wells to ensure a safe and efficient production. The current diagnosis method is pattern recognition of a dynamometer card (DC) based on feature extraction and perceptron. The premise of this method is that the training and target data have the same distribution. However, the training data are collected from a field SRPS with different system parameters designed to adapt to production conditions, which may significantly affect the diagnostic accuracy. To address this issue, in this study, an improved model of the sucker rod string (SRS) is derived by adding fault-parameter dimensions, with which DCs under 16 working conditions could be generated. Subsequently an adaptive diagnosis method is proposed by taking simulated DCs generated near the working point of the target SRPS as training data. Meanwhile, to further improve the accuracy of the proposed method, the DC features are improved by relative normalization and using additional features of the DC position to increase the distance between different types of samples. The parameters of the perceptron are optimized to promote its discriminability. Finally, the accuracy and real-time performance of the proposed adaptive diagnosis method are validated using field data.
A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system (SRPS) used in oil wells to ensure a safe and efficient production. The current diagnosis method is pattern recognition of a dynamometer card (DC) based on feature extraction and perceptron. The premise of this method is that the training and target data have the same distribution. However, the training data are collected from a field SRPS with different system parameters designed to adapt to production conditions, which may significantly affect the diagnostic accuracy. To address this issue, in this study, an improved model of the sucker rod string (SRS) is derived by adding fault-parameter dimensions, with which DCs under 16 working conditions could be generated. Subsequently an adaptive diagnosis method is proposed by taking simulated DCs generated near the working point of the target SRPS as training data. Meanwhile, to further improve the accuracy of the proposed method, the DC features are improved by relative normalization and using additional features of the DC position to increase the distance between different types of samples. The parameters of the perceptron are optimized to promote its discriminability. Finally, the accuracy and real-time performance of the proposed adaptive diagnosis method are validated using field data.
關(guān)鍵詞
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Sucker rod pump; Dynamometer card; Adaptive fault diagnosis; Sucker rod dynamics; Output metering