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<p>In order to reasonably split power curve characteristic parameters and predict well conditions, this study first analyzes the method of splitting oil well power curve characteristic parameters. Based on this, the big data-based well condition prediction algorithm is explored. Finally, the prediction effect of well conditions is analyzed. The research lays the foundation for accurately determining the operating status of downhole equipment and optimizing the pump parameters. The study shows that the local characteristic parameters of the oil well power curve are interrelated yet have their own characteristics. By calculating their real-time values and comparing them with the historical normal range, it is possible to accurately predict various conditions, such as insufficient fluid supply and wax deposition. During the well condition comprehensive diagnosis and prediction process, when the results indicate insufficient fluid supply, gas interference, or wax deposition, the system will mark the well as pending comprehensive diagnosis. If other issues are detected, an alarm will be triggered directly. For wells under comprehensive diagnosis, the system calculates the daily power curve characteristic parameters at 0:00 each day and compares them with the normal power curve parameters. If any parameter exceeds the normal range, a categorized alarm will be triggered. A large load difference indicates wax deposition; a reduction in effective stroke signals insufficient fluid supply; other parameter changes indicate corresponding real-time diagnosis results. The research concludes that accurately predicting the condition of the oil well can help optimize oil extraction strategies, improve liquid production capacity, extend equipment lifespan, and enhance the overall recovery rate of the oil field.</p>

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Prediction of Oil Well Condition on the Splitting of Well Power Curve Characteristics

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Chao Meng,
  • Qing Chen,
  • Yongfang Jiang,
  • Longbo Lin,
  • Xiaohua Yan

摘要

In order to reasonably split power curve characteristic parameters and predict well conditions, this study first analyzes the method of splitting oil well power curve characteristic parameters. Based on this, the big data-based well condition prediction algorithm is explored. Finally, the prediction effect of well conditions is analyzed. The research lays the foundation for accurately determining the operating status of downhole equipment and optimizing the pump parameters. The study shows that the local characteristic parameters of the oil well power curve are interrelated yet have their own characteristics. By calculating their real-time values and comparing them with the historical normal range, it is possible to accurately predict various conditions, such as insufficient fluid supply and wax deposition. During the well condition comprehensive diagnosis and prediction process, when the results indicate insufficient fluid supply, gas interference, or wax deposition, the system will mark the well as pending comprehensive diagnosis. If other issues are detected, an alarm will be triggered directly. For wells under comprehensive diagnosis, the system calculates the daily power curve characteristic parameters at 0:00 each day and compares them with the normal power curve parameters. If any parameter exceeds the normal range, a categorized alarm will be triggered. A large load difference indicates wax deposition; a reduction in effective stroke signals insufficient fluid supply; other parameter changes indicate corresponding real-time diagnosis results. The research concludes that accurately predicting the condition of the oil well can help optimize oil extraction strategies, improve liquid production capacity, extend equipment lifespan, and enhance the overall recovery rate of the oil field.