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Predicting Pump Inspection Cycles for Oil Wells Based on Stacking Ensemble Models

Author

Listed:
  • Hua Xin

    (School of Mathematics and Statistics, Northeast Petroleum University, Daqing 163318, China)

  • Shiqi Zhang

    (School of Mathematics and Statistics, Northeast Petroleum University, Daqing 163318, China)

  • Yuhlong Lio

    (Department of Mathematical Sciences, University of South Dakota, Vermillion, SD 57069, USA)

  • Tzong-Ru Tsai

    (Department of Statistics, Tamkang University, Tamsui District, New Taipei City 251301, Taiwan)

Abstract

Beam pumping is currently the broadly used method for oil extraction worldwide. A pumpjack shutdown can be incurred by failures from the load, corrosion, work intensity, and downhole working environment. In this study, the duration of uninterrupted pumpjack operation is defined as the pump inspection cycle. Accurate prediction of the pump inspection cycle can extend the lifespan, reduce unexpected pump accidents, and significantly enhance the production efficiency of the pumpjack. To enhance the prediction performance, this study proposes an improved two-layer stacking ensemble model, which combines the power of the random forests, light gradient boosting machine, support vector regression, and Adaptive Boosting approaches, for predicting the pump inspection cycle. A big pump-related oilfield data set is used to demonstrate the proposed two-layer stacking ensemble model can significantly enhance the prediction quality of the pump inspection cycle.

Suggested Citation

  • Hua Xin & Shiqi Zhang & Yuhlong Lio & Tzong-Ru Tsai, 2024. "Predicting Pump Inspection Cycles for Oil Wells Based on Stacking Ensemble Models," Mathematics, MDPI, vol. 12(14), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2231-:d:1437178
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    References listed on IDEAS

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    1. Li, Kun & Xu, Haocheng & Liu, Xiao, 2022. "Analysis and visualization of accidents severity based on LightGBM-TPE," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
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