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<p>Drill pipes are critical components in oil and gas exploration, and their structural integrity directly affects operational safety and economic efficiency. Traditional management practices primarily rely on experience-based assessments, which makes it difficult to accurately evaluate fatigue damage and residual load-bearing capacity, often leading to either excessive utilization or overly conservative decisions. To address these challenges, this study develops an intelligent drill pipe management system that integrates finite element–based mechanical analysis with condition monitoring. The system architecture comprises a sensing layer, data storage layer, data analysis layer, decision-support layer, and user interface layer, ensuring efficient data acquisition and utilization. At its core, a fatigue life prediction model combines simulation-derived stress and fatigue parameters with real-time monitoring data, enabling accurate estimation of the remaining fatigue life in allowable rotational cycles. Experimental validation confirmed the effectiveness of the model, achieving a coefficient of determination (R<sup>2</sup>) of 0.92 and a mean absolute error (MAE) below 0.10, compared with an R<sup>2</sup> of 0.71 and an MAE of 0.28 for traditional empirical formulas. In addition, Radio Frequency Identification (RFID) technology is employed for drill pipe identification, lifecycle tracking, and integration with the prediction model. Ultimately, the proposed system enables a quantitative evaluation of residual service capacity, thereby mitigating downhole fracture risks and reducing operational costs.</p>

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Research on the drilling pipe health monitoring and intelligent life prediction management platform

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月1日

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  • XiaoRong Gao,
  • XinYa Wu,
  • Qi Li,
  • Liupeng Wang,
  • ZhongAo Zhu,
  • FengTao Qu

摘要

Drill pipes are critical components in oil and gas exploration, and their structural integrity directly affects operational safety and economic efficiency. Traditional management practices primarily rely on experience-based assessments, which makes it difficult to accurately evaluate fatigue damage and residual load-bearing capacity, often leading to either excessive utilization or overly conservative decisions. To address these challenges, this study develops an intelligent drill pipe management system that integrates finite element–based mechanical analysis with condition monitoring. The system architecture comprises a sensing layer, data storage layer, data analysis layer, decision-support layer, and user interface layer, ensuring efficient data acquisition and utilization. At its core, a fatigue life prediction model combines simulation-derived stress and fatigue parameters with real-time monitoring data, enabling accurate estimation of the remaining fatigue life in allowable rotational cycles. Experimental validation confirmed the effectiveness of the model, achieving a coefficient of determination (R2) of 0.92 and a mean absolute error (MAE) below 0.10, compared with an R2 of 0.71 and an MAE of 0.28 for traditional empirical formulas. In addition, Radio Frequency Identification (RFID) technology is employed for drill pipe identification, lifecycle tracking, and integration with the prediction model. Ultimately, the proposed system enables a quantitative evaluation of residual service capacity, thereby mitigating downhole fracture risks and reducing operational costs.