高端油气钻探装备智能安全运维关键技术研究

杨思思; 王金江; 孙雪皓; 张毅; 张凤丽

doi:10.3799/dqkx.2026.121

Volume 51 Issue 8

Aug. 2026

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Article Navigation > Earth Science > 2026 > 51(8): 2951-2966

Yang Sisi, Wang Jinjiang, Sun Xuehao, Zhang Yi, Zhang Fengli, 2026. Research on Key Technology of Intelligent and Safe Operation and Maintenance of High-End Drilling Equipment. Earth Science, 51(8): 2951-2966. doi: 10.3799/dqkx.2026.121

Citation:

Yang Sisi, Wang Jinjiang, Sun Xuehao, Zhang Yi, Zhang Fengli, 2026. Research on Key Technology of Intelligent and Safe Operation and Maintenance of High-End Drilling Equipment. Earth Science, 51(8): 2951-2966. doi: 10.3799/dqkx.2026.121

Citation:

Yang Sisi, Wang Jinjiang, Sun Xuehao, Zhang Yi, Zhang Fengli, 2026. Research on Key Technology of Intelligent and Safe Operation and Maintenance of High-End Drilling Equipment. Earth Science, 51(8): 2951-2966. doi: 10.3799/dqkx.2026.121

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Research on Key Technology of Intelligent and Safe Operation and Maintenance of High-End Drilling Equipment

doi: 10.3799/dqkx.2026.121

Yang Sisi^{1, 3
,
,},
Wang Jinjiang^{2, 3
,
,
,},
Sun Xuehao³,
Zhang Yi^{2
,
,
,},
Zhang Fengli³

1.
College of Safety and Ocean Engineering, China University of Petroleum Beijing, Beijing 102249, China
2.
Scientific Drilling Technology Research Department, Chinese Academy of Geological Sciences, Beijing 100032, China
3.
Key Laboratory of Oil and Gas Production Equipment Quality Inspection and Health Diagnosis, State Administration for Market Regulation, Beijing 102249, China

Received Date: 2026-02-15
Publish Date: 2026-08-25

Abstract

Abstract

As the demand for deep and ultra-deep energy resource exploration continues to rise, high-end oil and gas drilling equipment faces significant challenges, including harsh environmental conditions, extreme operating scenarios, fault coupling, and maintenance difficulties. The current operation and maintenance practices largely rely on manual experience and periodic inspections, which fail to enable early fault prediction and timely response, leading to prominent safety concerns. To address the issues and challenges encountered during the operation and maintenance of high-end oil and gas drilling equipment, this paper proposes an innovative intelligent and safe operation and maintenance technology. Aiming for comprehensive, element-wide, and full life cycle operation and maintenance, the study constructs a closed-loop intelligent operation and maintenance process based on life cycle management and proactive maintenance optimization. By integrating digital empowerment technologies with domain-specific operation and maintenance technologies, the approach promotes the transformation from traditional to intelligent and safe operation and maintenance models for oil and gas drilling equipment. An intelligent and safe operation and maintenance system for drilling equipment is developed, incorporating functional modules for condition monitoring, health assessment, fault diagnosis, and intelligent decision-making. The system is successfully applied to the intelligent operation and maintenance of drilling equipment on an offshore drilling platform, providing a valuable reference for the rapid application of intelligent and safe operation and maintenance technologies in the oil and gas industry.

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References(58)

References

Carpenter, C., 2021. Digital-Twin Approach Predicts Fatigue Damage of Marine Risers. Journal of Petroleum Technology, 73(10): 65-66. https://doi.org/10.2118/1021-0065-jpt

Gao, Y. D., Wang, S. Y., Chang, B. T., et al., 2022. An Integrated Identification Approach of Abnormally High-Pressure Gas Zones Based on the Look-Ahead while Drilling Technology. Natural Gas Industry, 42(10): 98-106(in Chinese with English abstract).

Gao, D. L., Huang, W. J., 2024. Basic Research Progress and Prospect in Deep and Ultra-Deep Directional Drilling. Natural Gas Industry, 44(1): 1-12(in Chinese with English abstract).

Geng, L. D., 2021. Application Status and Development Suggestions of Big Data Technology in Petroleum Engineering. Petroleum Drilling Techniques, 49(2): 72-78(in Chinese).

He, D. F., Jia, C. Z., Zhao, W. Z., et al., 2023. Research Progress and Key Issues of Ultra-Deep Oil and Gas Exploration in China. Petroleum Exploration and Development, 50(6): 1333-1344. (in Chinese with English abstract). doi: 10.1016/S1876-3804(24)60470-2

He, T. T., Zhang, Q., 2024. Application Status and Development Trend of Knowledge Graph in Petroleum Exploration and Development. Natural Gas Industry, 44(9): 55-67(in Chinese with English abstract).

Hou, X. P., Lan, L., Tao, C., et al., 2024. Edge Intelligence and Collaborative Computing: Frontiers and Advances. Control and Decision, 39(7): 2385-2404(in Chinese with English abstract).

Huang, F. X., Wang, S. Y., Li, M. P., et al., 2024. Progress and Implications of Deep and Ultra-Deep Oil and Gas Exploration in PetroChina. Natural Gas Industry, 44(1): 86-96(in Chinese with English abstract).

Li, T. T., Zhang, Y., Lei, B., et al., 2026. A Cloud-Edge-End Collaborative Architecture for Intelligent Operation and Maintenance of High-End Drilling Equipment. Earth Science, 51(8): 2940-2950(in Chinese with English abstract).

Liu, C., Jiang, P. Y., Jiang, W. L., 2020. Web-Based Digital Twin Modeling and Remote Control of Cyber-Physical Production Systems. Robotics and Computer-Integrated Manufacturing, 64: 101956. https://doi.org/10.1016/j.rcim.2020.101956

Ma, Z. Z., Yuan, Z. M., Jia, Y., et al., 2023. Development of Offshore Oil Real-Time Intelligent Drilling Auxiliary Decision-Making Technology. Offshore Oil, 43(3): 84-89(in Chinese with English abstract).

Min, C., Wen, G. Q., Li, X. G., et al., 2024. Research Progress and Application Prospect of Interpretable Machine Learning in Artificial Intelligence of Oil and Gas Industry. Natural Gas Industry, 44(9): 114-126(in Chinese with English abstract).

Ran, C. Q., Zhang, X. B., Han, S., et al., 2025. TPDNet: a Point Cloud Data Denoising Method for Offshore Drilling Platforms and Its Application. Measurement, 241: 115671. https://doi.org/10.1016/j.measurement.2024.115671

Ren, S., Wang, J., Zhao, X., et al., 2024. "Doubly-Fed" Manufacturing Service of Intelligent Design and Preventive Maintenance for Complex Products. Journal of Mechanical Engineering, 60(6): 127-136(in Chinese with English abstract). doi: 10.3901/JME.2024.06.127

Suvarna, M., Yap, K. S., Yang, W. T., et al., 2021. Cyber-Physical Production Systems for Data-Driven, Decentralized, and Secure Manufacturing: A Perspective. Engineering, 7(9): 1212-1223. https://doi.org/10.1016/j.eng.2021.04.021

Sheng, Y. N., Guan, Z. C., Luo, M., et al., 2019. A Quantitative Evaluation Method of Drilling Risks Based on Uncertainty Analysis Theory. Journal of China University of Petroleum (Edition of Natural Science), 43(2): 91-96(in Chinese with English abstract).

Tang, Y., Zou, Z. W., Jing, J. J., et al., 2015. A Framework for Making Maintenance Decisions for Oil and Gas Drilling and Production Equipment. Journal of Natural Gas Science and Engineering, 26: 1050-1058(in Chinese with English abstract). doi: 10.1016/j.jngse.2015.07.038

Muhammad, H., Even, F., Filippo, S., 2024. Exploring the Synergies between Collaborative Robotics, Digital Twins, Augmentation, and Industry 5.0 for Smart Manufacturing: a State-of-the-Art Review. Robotics and Computer-Integrated Manufacturing, 89: 102769. https://doi.org/10.1016/j.rcim.2024.102769

Ning, B., Sha, Z. B., Li. J., et al., 2025. The Development Status and Development Trends of Deep-Area Drilling Technology. Earth Science (in Chinese with English abstract).

Wang, D. Y., Wang, Y. H., Yu, X. J., 2017. Research and Development Trend of Domestic Automated Drilling Rig. China Petroleum Machinery, 45(5): 23-27(in Chinese with English abstract).

Wang, J. L., Wang, Y. Z., Qiu, W. H., et al., 2024. Drilling Intelligent Decision Support System Based on Big Data and Fusion Model. Petroleum Drilling Techniques, 52(5): 105-116(in Chinese with English abstract).

Wang, J. S., Kang, F. L., 2024. Implementation and Optimization of Edge Computing in Drilling Data Stream Processing. Information System Engineering, (11): 119-122(in Chinese with English abstract).

Wang, W. X., Cao, X. Y., Ma, J. G., et al., 2024. Development and Application of Automatic Drilling Rig for 12 000 m Extra-Deep Wells. Drilling Engineering, 51(4): 7-13(in Chinese with English abstract).

Xiao, L., Yang, C. S., Zhao, J. H., et al., 2015. Key Technologies of Drilling Engineering Decision Support Systems. Petroleum Drilling Techniques, 43(2): 38-43(in Chinese with English abstract).

Xu, Y. M., Liu, Z. P., Gao, M., et al., 2019. Key Technology of 9 000 m Intelligent Drilling Rig. China Petroleum Machinery, 47(9): 57-62(in Chinese with English abstract).

Xu, Z. G., Dang, Y. Z., 2023. Data-Driven Causal Knowledge Graph Construction for Root Cause Analysis in Quality Problem Solving. International Journal of Production Research, 61(10): 3227-3245. https://doi.org/10.1080/00207543.2022.2078748

Yang, A. X., Wu, M., Yu, W. K., et al., 2023. Fault Diagnosis of Drilling Process Based on Multi-Scale Decomposition and Decision Fusion. IFAC-PapersOnLine, 56(2): 8079-8084. https://doi.org/10.1016/j.ifacol.2023.10.956

Yang, B., Li, B. Y., Yang, D. G., et al., 2024. Development and Application of Fully Integrated Upgrading of Rigs. China Petroleum Machinery, 52(12): 17-22(in Chinese with English abstract).

Yin, Q. S., Yang, J., Hou, X. X., et al., 2020. Drilling Performance Improvement in Offshore Batch Wells Based on Rig State Classification Using Machine Learning. Journal of Petroleum Science and Engineering, 192: 107306. https://doi.org/10.1016/j.petrol.2020.107306

Zhang, C., Li, X., Ye, M., et al, 2024. Application of Physics-Informed Neural Network in Two-Phase Flow. CIESC Journal, 75(11): 3835-3856(in Chinese with English abstract).

Zhang, L. B., Wang, J. J., 2022. Intelligent Operation and Maintenance Technology of Oil & Gas Storage and Transportation Equipment Based on Industrial Internet. Oil & Gas Storage and Transportation, 41(6): 625-631(in Chinese with English abstract).

Zhang, L. B., Wang, J. J., 2023. Intelligent Safe Operation and Maintenance of Oil and Gas Production Systems: Connotations and Key Technologies. Natural Gas Industry B, 10(3): 293-303(in Chinese with English abstract). doi: 10.1016/j.ngib.2023.05.006

Zhao, C. L., Qu, Y., Wang, B., et al., 2022. A New Method for Predicting Drilling Accident Level Based on 2D-CNN Deep Learning. Natural Gas Industry, 42(12): 95-105(in Chinese with English abstract).

Zheng, Q. H., Liu, H., Gong, T. L., et al., 2023. Development and Prospect of Big Data Knowledge Engineering. Strategic Study of CAE, 25(2): 208-220(in Chinese with English abstract).

高德利, 黄文君, 2024. 深层、超深层定向钻井中若干基础研究进展与展望. 天然气工业, 44(1): 1-12.

高永德, 王世越, 常波涛, 等, 2022. 基于随钻前视探测技术的异常高压气层综合识别方法. 天然气工业, 42(10): 98-106.

耿黎东, 2021. 大数据技术在石油工程中的应用现状与发展建议. 石油钻探技术, 49(2): 72-78.

何登发, 贾承造, 赵文智, 等, 2023. 中国超深层油气勘探领域研究进展与关键问题. 石油勘探与开发, 50(6): 1162-1172.

和婷婷, 张强, 2024. 知识图谱在油气勘探开发中的应用现状与发展趋势. 天然气工业, 44(9): 55-67.

侯祥鹏, 兰兰, 陶长乐, 等, 2024. 边缘智能与协同计算: 前沿与进展. 控制与决策, 39(7): 2385-2404.

黄福喜, 汪少勇, 李明鹏, 等, 2024. 中国石油深层、超深层油气勘探进展与启示. 天然气工业, 44(1): 86-96.

厉曈曈, 张毅, 雷彪, 等. 2026. 云边端协同高端钻探装备智能运维体系架构. 地球科学, 51(8): 2940-2950.

马志忠, 袁则名, 贾雍, 等, 2023. 海洋石油实时智能钻井辅助决策技术进展. 海洋石油, 43(3): 84-89.

闵超, 文国权, 李小刚, 等, 2024. 可解释机器学习在油气领域人工智能中的研究进展与应用展望. 天然气工业, 44(9): 114-126.

宁波, 沙志彬, 李晶, 等, 2025. 深海钻探技术现状及发展动态. 地球科学.

任杉, 王晋, 赵欣, 等, 2024. 复杂产品智能设计与主动运维"双馈式"制造服务方法体系. 机械工程学报, 60(6): 127-136.

胜亚楠, 管志川, 罗鸣, 等, 2019. 基于不确定性分析的钻井工程风险定量评价方法. 中国石油大学学报(自然科学版), 43(2): 91-96.

王定亚, 王耀华, 于兴军, 2017. 我国管柱自动化钻机技术研究及发展方向. 石油机械, 45(5): 23-27.

王建龙, 王越支, 邱卫红, 等, 2024. 基于大数据与融合模型的钻井智能辅助决策系统. 石油钻探技术, 52(5): 105-116.

王建胜, 康芳玲, 2024. 边缘计算在钻井数据流处理中的实现与优化. 信息系统工程, (11): 119-122.

王维旭, 曹晓宇, 马继光, 等, 2024. 12 000 m特深井自动化钻机研制与应用. 钻探工程, 51(4): 7-13.

肖莉, 杨传书, 赵金海, 等, 2015. 钻井工程决策支持系统关键技术. 石油钻探技术, 43(2): 38-43.

许益民, 刘占鹏, 高猛, 等, 2019. 9000 m智能钻机关键技术. 石油机械, 47(9): 57-62.

杨斌, 李博洋, 杨德刚, 等, 2024. 石油钻机全集成化升级的开发与应用. 石油机械, 52(12): 17-22.

张来斌, 王金江, 2022. 工业互联网赋能的油气储运设备智能运维技术. 油气储运, 41(6): 625-631.

张来斌, 王金江, 2023. 油气生产智能安全运维: 内涵及关键技术. 天然气工业, 43(2): 15-23.

赵春兰, 屈瑶, 王兵, 等, 2022. 一种基于2D-CNN深度学习的钻井事故等级预测新方法. 天然气工业, 42(12): 95-105.

郑庆华, 刘欢, 龚铁梁, 等, 2023. 大数据知识工程发展现状及展望. 中国工程科学, 25(02): 208-220.

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Research on Key Technology of Intelligent and Safe Operation and Maintenance of High-End Drilling Equipment

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