基于可钻性知识图谱的深部在钻地层可钻性智能推断

doi:10.3799/dqkx.2026.144

基于可钻性知识图谱的深部在钻地层可钻性智能推断

doi: 10.3799/dqkx.2026.144

甘超^1,2,3,
刘勇^1,2,3,
汪拓^1,2,3,
曹卫华^1,2,3,,
张毅^4,

1. 中国地质大学(武汉）自动化学院, 湖北武汉 430074;
2. 复杂系统先进控制与智能自动化湖北省重点实验室, 湖北武汉 430074;
3. 地球探测智能化技术教育部工程研究中心, 湖北武汉 430074;
4. 中国地质科学院, 深地探测与矿产勘查全国重点实验室, 北京 100094

基金项目:

地球深部探测与矿产资源勘查国家科技重大专项(项目编号.2024ZD1000800,2024ZD1000806)

国家自然科学基金重点项目(No.62333019)。

详细信息

作者简介:
甘超(1990-),男,副教授,博士,主要从事复杂地质钻进过程建模与优化控制的研究工作.E-mail:ganchao@cug.edu.cn,ORCID:0000-0002-4460-2279

通讯作者:
曹卫华(1972-),E-mail:weihuacao@cug.edu.cn

张毅(1986-),E-mail:shidazhangyang@163.com

中图分类号: TE21
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出版历程
- 收稿日期: 2026-01-13
- 网络出版日期: 2026-06-29

An intelligent inferring method of deep formation drillability based on drillability knowledge graph

GAN Chao^1,2,3,
LIU Yong^1,2,3,
WANG Tuo^1,2,3,
CAO Weihua^{1,2,3
,},
ZHANG Yi^4
,

1. School of Automation, China University of Geosciences, Wuhan 430074, China;
2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China;
3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China;
4. Chinese Academy of Geological Sciences, Beijing 100094, China

摘要

摘要: 地层可钻性作为衡量钻进过程中地层被钻难易程度的关键指标，是合理选择钻进方法和优化钻进操作参数的重要依据。针对深部地质钻进过程中可钻性信息获取成本高昂、许多岩石没有对应的地层可钻性范围标准等问题，本文提出一种基于可钻性知识图谱的深部在钻地层可钻性智能推断方法。该方法首先构建包含岩石关键特征的可钻性知识图谱，设计基于岩石相似性度量的可钻性推理算法，将地矿行业标准中的67种地层可钻性级别推广至已知的292种岩石。其次，通过钻孔地层岩性动态分析与可钻性智能推断，实时分析地层岩性分布，推断在钻地层可钻性级别。最后，通过微钻实验和压入硬度实验验证所提方法的有效性。该方法为深部地质钻进过程智能控制奠定了重要基础。
- 深部地质钻进过程 /
- 地层可钻性信息获取 /
- 地层可钻性知识图谱
Abstract: The formation drillability, as a key indicator for measuring the ease or difficulty of drilling the formation during the drilling process, serves as an important basis for rationally selecting drilling methods and optimizing drilling operational parameters. Addressing the issues of high cost in acquiring drillability information during deep geological drilling and the lack of corresponding formation drillability range standards for many rocks, this paper proposes an intelligent inference method for deep drilling formation drillability based on a drillability knowledge graph. Firstly, a drillability knowledge graph incorporating the key characteristics of rocks is constructed, and a drillability reasoning algorithm based on rock similarity measurement is designed. This approach extends the 67 rock drillability levels specified in the geomining industry standards to 292 known types of rocks. Secondly, through dynamic analysis of borehole formation lithology and intelligent inference of drillability, the distribution of formation lithology is analyzed in real-time, and the drillability level of the formation being drilled is inferred. Finally, the effectiveness of the proposed method is verified through micro-drilling experiments and indentation hardness tests. This method lays an important foundation for the intelligent control of the deep geological drilling process.
- Deep geological drilling process /
- Acquisition of formation drillability information /
- Knowledge graph of rock drillability

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参考文献(37)

[1]	Deng H. J., Xiao Y. F., Zheng R. C., 2017. Concise Textbook of Petrology (4th Edition).Beijing: Geological Publishing House(in Chinese).
[2]	Fu M. C., 2020. Study on DC Index Correction.Mud Logging Engineering, 31(03): 8-13(in Chinese with English abstract).
[3]	Feng J. X., Yuan S. Y., Luo C. M., et al., 2025. Research and Application of Intelligent Evaluation Method for Formation Drillability Based on Multi-Source Data Fusion.Petroleum Science Bulletin, 10(5): 892-907(in Chinese with English abstract).
[4]	Gan C., Cao W. H., Wu M., et al., 2016. Intelligent Nadaboost-ELM Modeling Method for Formation Drillability Using Well Logging Data.Journal of Advanced Computational Intelligence and Intellige-nt Informatics, 20(7): 1103-1111. https://doi.org/10.20965/jaciii.2016.p1103.
[5]	Gan C., Cao W. H., Wu M., et al., 2017. An Online Modeling Method for Formation Drillability Based on OS-Nadaboost-ELM Algorithm in Deep Drilling Process. IFAC-PapersOnLine, 50(01): 12886-12891. https://doi. org/10.1016/j.ifacol.2017.08.1941.
[6]	Gan C., Cao W. H., Liu K. Z., et al., 2020. Spatial Estimation for 3D Formation Drillability Field: A New Modeling Framework.Journal of Natural Gas Science and Engineering, 84l: 103628. https://doi.org/10.1016/j.jn-g se.2020.103628.
[7]	Gan C., Cao W. H., Liu K. Z., et al., 2021. A New Spatial Modeling Method for 3D Formation Drillability Field Using Fuzzy C-Means Clustering and Random Forest.Journal of Petroleum Science and Engineering, 200: 108371. https://doi.org/10.1016/j.petrol.2021.108371.
[8]	Gan C., Wang X., Wang L. Z., et al., 2023. Dynamic Prediction Method of Drilling Rate in Deep Geological Drilling Based on Regional Multi-well Data Optimization and Model Pre-Training.Drilling Engineering, 50(04): 1-8(in Chinese with English abstract).
[9]	Li W. Y., 2022. Prediction of Rock Drillability of Gypsum Interlayer Based on Fractal Theory and Modified Drilling Rate Equation.West-China Exploration Engineering, 34(02): 52-54(in Chinese).
[10]	Li Y. B., Li Z. H., Hu P. J., et al., 2023. Evaluation and Application of Rock Drill-Resistant Characteristics Based on Mineral Composition.Drilling & Production Technology, 46(06): 177-183(in Chinese with English abstract).
[11]	Li F. Q., Su Q. Y., Liu S. P., et al., 2024. Study and Application of Drillability of Red Bed Rock Strata in Changsha Basin.Drilling Engineering, 51(01): 51-57(in Chinese with English abstract).
[12]	Liu X., 2025. Launch of the National Science and Technology Major Project for Deep Earth Exploration "Ultra-deep Scientific Drilling Technology and Equipment (over 10,000 meters)". China Natural Resources News(in Chinese).
[13]	Ma H., Wang Y. J., Wei M. A., et al., 2008. A New Method for Predicting Formation Drillability Grade.Acta Petrolei Sinica, (05): 761-765(in Chinese with English abstract).
[14]	Ma H., Wang Y. J., Hu R., et al., 2010. Prediction of Formation Drillability Grade Based on Relevance Vector Machine.Journal of China University of Petroleum (Natural Science Edition), 34(02): 67-70(in Chinese with English abstract).
[15]	Ministry of Natural Resources., 2010. DZ/T 0227-2010, Specifications for Geological Core Drilling.National Technical Committee for Standardization of Natural Resources and Territorial Spatial Planning(in Chinese).
[16]	National Energy Administration., 2016. SY/T 5426-2016, Methods for Determination and Classification of Rock Drillability.Petroleum Drilling Engineering Professional Standardization Committee(in Chinese).
[17]	Qin Y., Yang H., Cui L., et al., 2025. Construction Method of Knowledge Graph for Associated and Symbiotic Minerals Driven by Data-Knowledge Collaboration.Earth Science, 1-19(in Chinese with English abstract).
[18]	Wang D., He Y. X., 2014. Geological Drilling Handbook.Changsha: Central South University Press(in Chinese).
[19]	Xiao Y. Z., Zheng S. A., Shi J. C., et al., 2023. Knowledge Graph-Based Manufacturing Process Planning: A State-of-the-Art Review. Journal of Manufacturing Systems, 70:417-435.https://doi.org/10.1016/j.jmsy. 2023.08.006.
[20]	Zhang Y. X., Gao X. Y., 2011. Study on Rock Drillability Based on Logging Data.Information System Engineering, (09): 97-98(in Chinese).
[21]	Wu R. Z., Li H., Mei H. B., et al., 2025. A Landslide Monitoring and Early Warning System with Retrieval-Augmented Generation Enhanced by Knowledge Graph.Earth Science,50(10): 4125-4136. (in Chinese with English abstract).
[22]	邓红江, 肖渊甫, 郑荣才, 2017. 岩石学简明教程第4版. 北京: 地质出版社.
[23]	冯建祥, 袁三一, 骆春妹, 等, 2025. 多源数据融合的地层可钻性智能评估方法研究及应用. 石油科学通报, 10(5): 892-907.
[24]	付明川, 2020. dc指数校正研究. 录井工程, 31(03): 8-13.
[25]	甘超, 汪祥, 王鲁朝, 等, 2023. 基于区域多井数据优选与模型预训练的深部地质钻探过程钻速动态预测方法. 钻探工程, 50(04): 1-8.
[26]	国家能源局, 2016. SY/T 5426-2016, 岩石可钻性测定及分级方法. 石油钻井工程专业标准化委员会.
[27]	李嵬岩, 2022. 分形理论及修正钻速方程预测石膏夹层岩石可钻性. 西部探矿工程, 34(02): 52-54.
[28]	李玉波, 李忠慧, 胡棚杰, 等, 2023. 基于矿物组分的岩石抗钻特性评价及应用. 钻采工艺, 46(06): 177-183.
[29]	李奋强, 粟琼玉, 刘素平, 等, 2024. 长沙盆地红层岩石地层可钻性研究及应用. 钻探工程, 51(01): 51-57.
[30]	刘烜, 2025. 深地国家科技重大专项“超万米科学钻探技术与装备”项目启动. 中国自然资源报.
[31]	马海, 王延江, 魏茂安, 等, 2008. 地层可钻性级值预测新方法.石油学报, (05): 761-765.
[32]	马海, 王延江, 胡睿, 等, 2010. 基于相关向量机的地层可钻性级值预测. 中国石油大学学报(自然科学版), 34(02): 67-70.
[33]	自然资源部, 2010. DZ/T 0227-2010, 地质岩心钻探规程. 全国自然资源与国土空间规划标准化技术委员会.
[34]	秦颖, 杨慧, 崔柳, 等, 2025. 数据-知识协同驱动的共伴生矿产知识图谱构建方法. 地球科学, 1-19.
[35]	王达, 何远信, 2014. 地质钻探手册. 长沙:中南大学出版社.
[36]	张亚旭, 高小燕, 2011. 基于测井资料的岩石可钻性研究. 信息系统工程, (09): 97-98.
[37]	吴润泽, 李浩, 梅红波, 等, 2025. 基于知识图谱检索增强生成的滑坡监测预警系统.地球科学, 50(10): 4125-4136.

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基于可钻性知识图谱的深部在钻地层可钻性智能推断

doi: 10.3799/dqkx.2026.144

作者简介:
甘超(1990-),男,副教授,博士,主要从事复杂地质钻进过程建模与优化控制的研究工作.E-mail:ganchao@cug.edu.cn,ORCID:0000-0002-4460-2279

通讯作者:
曹卫华(1972-),E-mail:weihuacao@cug.edu.cn

张毅(1986-),E-mail:shidazhangyang@163.com

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An intelligent inferring method of deep formation drillability based on drillability knowledge graph

计量

目录

留言板

基于可钻性知识图谱的深部在钻地层可钻性智能推断

doi: 10.3799/dqkx.2026.144

作者简介: 甘超(1990-),男,副教授,博士,主要从事复杂地质钻进过程建模与优化控制的研究工作.E-mail:ganchao@cug.edu.cn,ORCID:0000-0002-4460-2279

通讯作者: 曹卫华(1972-),E-mail:weihuacao@cug.edu.cn 张毅(1986-),E-mail:shidazhangyang@163.com

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出版历程

An intelligent inferring method of deep formation drillability based on drillability knowledge graph

计量

出版历程

目录

作者简介:
甘超(1990-),男,副教授,博士,主要从事复杂地质钻进过程建模与优化控制的研究工作.E-mail:ganchao@cug.edu.cn,ORCID:0000-0002-4460-2279

通讯作者:
曹卫华(1972-),E-mail:weihuacao@cug.edu.cn

张毅(1986-),E-mail:shidazhangyang@163.com