首页|Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort Addi-tionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is con-structed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experi-ments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Sucker-rod pumping systemDynamometer cardWorking condition recognitionDeep learningTime-frequency signatureTime-frequency signature matrix

Yun-Peng He、Hai-Bo Cheng、Peng Zeng、Chuan-Zhi Zang、Qing-Wei Dong、Guang-Xi Wan、Xiao-Ting Dong

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State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,Liaoning,China

Key Laboratory of Networked Control Systems,Chinese Academy of Sciences,Shenyang 110016,Liaoning China

Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,Liaoning,China

University of Chinese Academy of Sciences,Beijing,100049,China

School of Artifcial Intelligence,Shenyang University of Technology,Shenyang 110870,Liaoning,China

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国家自然科学基金State Key Laboratory of Robotics of China辽宁省自然科学基金Research Program of Liaoning Liaohe Laboratory

622032342023-Z032023-BS-025LLL23ZZ-02-02

2024

10.1016/j.petsci.2023.08.031

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(1)
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