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Development of a Hybrid AI Model for Fault Prediction in Rod Pumping System for Petroleum Well Production

Author

Listed:
  • Aoxue Zhang

    (School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China)

  • Yanlong Zhao

    (School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China)

  • Xuanxuan Li

    (School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China)

  • Xu Fan

    (School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China)

  • Xiaoqing Ren

    (School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China)

  • Qingxia Li

    (Region of Luliang Oilfield, PetroChina Xinjiang Oilfield Company, Karamay 834000, China)

  • Leishu Yue

    (School of Petroleum, China University of Petroleum-Beijing at Karamay, Karamay 834000, China)

Abstract

Rod pumping systems are widely used in oil wells. Accurate fault prediction could reduce equipment fault rate and has practical significance in improving oilfield production efficiency. This paper analyzed the production journal of rod pumping wells in block X of Xinjiang Oilfield. According to the production journal, oil well maintenance operations are primarily caused by five types of faults: scale, wax, corrosion, fatigue, and wear. These faults make up approximately 90% of all faults. 1354 oil wells in the block that experienced workover operations as a result of the aforementioned factors were chosen as the research objects for this paper. To lower the percentage of data noise, wavelet threshold denoising and variational mode decomposition were used. Based on the bidirectional long short-term memory network, an intelligent model for fault prediction was built. It was trained and verified with the help of the sparrow search algorithm. Its efficacy was demonstrated by testing various deep learning models in the same setting and with identical parameters. The results show that the prediction accuracy of the model is the highest compared with other 11 models, reaching 98.61%. It is suggested that the model using artificial intelligence can provide an accurate fault warning for the oilfield and offer guidance for the maintenance of the rod pumping system, which is meant to reduce the occurrence of production stagnation and resource waste.

Suggested Citation

  • Aoxue Zhang & Yanlong Zhao & Xuanxuan Li & Xu Fan & Xiaoqing Ren & Qingxia Li & Leishu Yue, 2024. "Development of a Hybrid AI Model for Fault Prediction in Rod Pumping System for Petroleum Well Production," Energies, MDPI, vol. 17(21), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5422-:d:1510364
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    References listed on IDEAS

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    1. Parri, Srihari & Teeparthi, Kiran & Kosana, Vishalteja, 2024. "A hybrid methodology using VMD and disentangled features for wind speed forecasting," Energy, Elsevier, vol. 288(C).
    2. Fuquan Song & Heying Ding & Yongzheng Wang & Shiming Zhang & Jinbiao Yu, 2023. "A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network," Energies, MDPI, vol. 16(6), pages 1-22, March.
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