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A Working Conditions Warning Method for Sucker Rod Wells Based on Temporal Sequence Prediction

Author

Listed:
  • Kai Zhang

    (School of Petroleum Engineering, China University of Petroleum, Qingdao 266500, China
    School of Civil Engineering, Qingdao University of Technology, Qingdao 266500, China)

  • Chengzhe Yin

    (School of Petroleum Engineering, China University of Petroleum, Qingdao 266500, China)

  • Weiying Yao

    (CNOOC EnerTech-Drilling & Production Co., Tianjin 300452, China)

  • Gaocheng Feng

    (CNOOC EnerTech-Drilling & Production Co., Tianjin 300452, China)

  • Chen Liu

    (CNOOC Research Institute Ltd., Beijing 100028, China)

  • Cheng Cheng

    (School of Petroleum Engineering, China University of Petroleum, Qingdao 266500, China)

  • Liming Zhang

    (School of Petroleum Engineering, China University of Petroleum, Qingdao 266500, China)

Abstract

The warning of the potential faults occurring in the future in a sucker rod well can help technicians adjust production strategies in time. It is of great significance for safety during well production. In this paper, the key characteristic parameters of dynamometer cards were predicted by a temporal neural network to implement the warning of different working conditions which might result in failures. First, a one-dimensional damped-wave equation was used to eliminate the dynamic loads’ effect of surface dynamometer cards by converting them into down-hole dynamometer cards. Based on the down-hole dynamometer cards, the characteristic parameters were extracted, including the load change, the position of the valve opening and closing point, the dynamometer card area, and so on. The mapping relationship between the characteristic parameters and working conditions (classification model) was obtained by the Xgboost algorithm. Meanwhile, the noise in these parameters was reduced by wavelet transformation, and the rationality of the results was verified. Second, the Encoder–Decoder and multi-head attention structures were used to set up the time series prediction model. Then, the characteristic parameters were predicted in a sequence-to-sequence way by using historical characteristic parameters, date, and pumping parameters as input. At last, by inputting the predicted results into the classification model, a working conditions warning method was created. The results showed that noise reduction improved the prediction accuracy significantly. The prediction relative error of most characteristic parameters was less than 15% after noise reduction. In most working conditions, their F1 values were more than 85%. Most Recall values could be restored to over 90% of those calculated by real parameters, indicating few false negative cases. In general, the warning method proposed in this paper can predict faulty working conditions that may occur in the future in a timely manner.

Suggested Citation

  • Kai Zhang & Chengzhe Yin & Weiying Yao & Gaocheng Feng & Chen Liu & Cheng Cheng & Liming Zhang, 2024. "A Working Conditions Warning Method for Sucker Rod Wells Based on Temporal Sequence Prediction," Mathematics, MDPI, vol. 12(14), pages 1-25, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2253-:d:1438791
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    References listed on IDEAS

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    1. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Liu, Yonggang & Zhang, Yuanjian, 2023. "Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training," Energy, Elsevier, vol. 266(C).
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