钻井复杂事故预警技术研究进展

doi:10.12388/j.issn.1673-2677.2025.02.002

摘要/Abstract

摘要：

钻井作业过程中，井漏、卡钻及溢流等复杂事故显著影响钻井安全与经济成本，驱动行业积极探索高效预防措施。随着大数据与人工智能技术的快速发展，构建钻井复杂事故预警系统已成为钻井工程领域的关键技术挑战。研究针对井漏、卡钻及溢流等钻井复杂事故的预警技术进行了深入探讨，系统对比了各类算法模型的特征参数、预测准确性以及实际应用效果，揭示了不同模型在预警能力上的差异与优劣。研究发现，在特征参数需求低且数据来源稳定时，机器学习算法模型在钻井复杂事故预警中展现出突出的准确性与时效性；但面对计算复杂度高、数据质量不佳及来源不稳定等问题，尤其是在钻中井漏与溢流事故预警中，机器学习模型常伴随着高误报率与频繁漏报，且缺乏足够的现场应用实例。为促进钻井复杂事故预警技术的有效转化与数字化转型，亟需加强数据质量提升与模型算法优化研究。尽管面临诸多挑战，大数据和人工智能仍为钻井复杂事故预警技术开辟了广阔的发展空间。

关键词:

钻井复杂事故, 预警技术, 大数据, 人工智能, 机器学习

Abstract:

Complex accidents in drilling operations，such as lost circulation，pipe sticking，and well kick，bring about severe threats to drilling safety and significantly increase economic costs. Therefore，the petroleum industry is driven to actively seek efficient preventive measures. With the rapid development of big data and artificial intelligence technologies，developing an early warning system for drilling complex accidents has become a core technical challenge in drilling engineering. This study conducts an in-depth exploration of the early warning technologies for drilling complex accidents，including lost circulation，sticking，and well kick. It systematically compares the characteristic parameters，accuracy，and application performances of various algorithm models and reveals the differenc

es and advantages/disadvantages of different models in terms of early warning capabilities. It is found that in scenarios where the demand for characteristic parameters is low and the data sources are stable，machine learning algorithm models exhibit superior accuracy and timeliness in the early warning of drilling complex accidents. However，when faced with challenges such as high computational complexity，poor data quality，and unstable data sources，especially in predicting lost circulation during drilling and early-warning complex well kick accidents，the models suffer from high rates of false alarms and frequent missed alarms，and few field application cases are reported. To promote the field application of drilling complex accident early warning techniques and drilling digitalization，it is necessary to strengthen research on data quality improvement and algorithm optimization. Despite the numerous challenges，big data and artificial intelligence still open up broad prospects for early warning technologies.

Key words:

drilling complex accident, early warning technology, big data, artificial intelligence, machine learning

中图分类号:

TE28

刘颖彪, , 徐生江, , 田龙, , 钟尹明, , 白佳帅, , 钟润豪, .

钻井复杂事故预警技术研究进展

[J]. 新疆石油天然气, 2025, 21(2): 15-23.

LIU Yingbiao, , XU Shengjiang, , TIAN Long, , ZHONG Yinming, , BAI Jiashuai, , ZHONG Runhao, .

Research Progress on Early Warning Technology of Drilling Complex Accidents

[J]. Xinjiang Oil & Gas, 2025, 21(2): 15-23.

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