失效数据驱动方法在油气管道风险评价中的应用与发展
Application and development of failure data-driven method in the risk assessment of oil and gas pipeline
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- 引用格式:
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程可心,杨玉锋,郑文培.失效数据驱动方法在油气管道风险评价中的应用与发展[J].天然气与石油,2025,43(4):139-147.doi:10.3969/j.issn.1006-5539.2025.04.019
CHENG Kexin, YANG Yufeng, ZHENG Wenpei.Application and development of failure data-driven method in the risk assessment of oil and gas pipeline[J].Natural Gas and Oil,2025,43(4):139-147.doi:10.3969/j.issn.1006-5539.2025.04.019
- DOI:
- 10.3969/j.issn.1006-5539.2025.04.019
- 作者:
- 程可心1,2,3 杨玉锋1 郑文培2,3
CHENG Kexin1,2,3, YANG Yufeng1, ZHENG Wenpei2,3
- 作者单位:
- 1. 国家管网集团科学技术研究总院分公司, 天津 300457; 2. 中国石油大学(北京)安全与海洋工程学院, 北京 102249; 3. 中国石油大学(北京)油气安全与应急技术重点实验室, 北京 102249
1. PipeChina Science & Technology Research Institute, Tianjin, 300457, China; 2. College of Safety and Ocean Engineering, China University of Petroleum(Beijing), Beijing, 102249, China; 3. Key Laboratory of Oil & Gas Safety and Emergency Technology, China University of Petroleum(Beijing), Beijing, 102249, China
- 关键词:
- 油气管道;失效因素;数据驱动;机器学习;风险评价
Oil and gas pipelines; Failure factors; Data-driven; Machine learning; Risk assessment
- 摘要:
- 为提升油气管道风险评估的精度与时效性,从机理模型和数据驱动模型的角度,系统梳理了油气管道风险评价的主要方法,重点探讨了机器学习和深度学习算法在风险评价中的应用,并结合管道失效典型场景分析了数据驱动型评价方法的优势与局限性。结果表明,失效数据驱动的评价方法通过非线性建模与多源数据融合,能够有效捕捉动态风险,显著提升风险评估的精度和时效性。在地质灾害、第三方破坏和腐蚀风险评价方面,机器学习和深度学习算法展现了强大的预测能力,为油气管道的风险防控提供了有力支持。失效数据驱动方法为油气管道风险评价带来了新的机遇,但全面应用仍面临技术、制度和生态的挑战。未来研究应充分考虑风险因素之间的耦合和竞争关系,发展机理模型+数据双驱动体系,进一步优化集成学习和深度学习算法,以实现油气管道风险的精准预测和动态管控。
In order to improve the accuracy and timeliness of oil and gas pipeline risk assessment, this paper systematically reviewed the main methods of oil and gas pipeline risk assessment from the perspective of mechanistic model and data-driven model, focused on the application of machine learning and deep learning algorithms in risk assessment, and analyzed the advantages and limitations of data-driven evaluation methods combined with typical pipeline failure scenarios. The results show that the data-driven assessment method based on failure data can effectively capture the dynamic risk through nonlinear modeling and multi-source data fusion, and significantly improve the accuracy and timeliness of risk assessment. In terms of geological disasters, third-party damage and corrosion risk assessment, machine learning and deep learning models demonstrate strong predictive capabilities, providing strong support for the risk prevention and control of oil and gas pipelines. Failure data-driven methods bring new opportunities for oil and gas pipeline risk assessment, but their full application still faces technical, institutional and ecological challenges. Future research should fully consider the coupling and competition relationship between risk factors, develop the mechanistic model and data-driven system, and further optimize the ensemble learning and deep learning models to realize the accurate prediction and dynamic control of oil and gas pipeline risks.
