基于可钻性知识图谱的深部在钻地层可钻性智能推断

甘超; 刘勇; 汪拓; 曹卫华; 张毅

doi:10.3799/dqkx.2026.144

Volume 51 Issue 8

Aug. 2026

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Gan Chao, Liu Yong, Wang Tuo, Cao Weihua, Zhang Yi, 2026. An Intelligent Inferring Method of Deep Formation Drillability Based on Drillability Knowledge Graph. Earth Science, 51(8): 3065-3075. doi: 10.3799/dqkx.2026.144

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Gan Chao, Liu Yong, Wang Tuo, Cao Weihua, Zhang Yi, 2026. An Intelligent Inferring Method of Deep Formation Drillability Based on Drillability Knowledge Graph. Earth Science, 51(8): 3065-3075. doi: 10.3799/dqkx.2026.144

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An Intelligent Inferring Method of Deep Formation Drillability Based on Drillability Knowledge Graph

doi: 10.3799/dqkx.2026.144

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 Artificial Intelligence and 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.
State Key Laboratory of Deep Earth and Mineral Exploration, Chinese Academy of Geological Sciences, Beijing 100094, China

Received Date: 2026-01-21
Publish Date: 2026-08-25

Abstract

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.

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References(37)

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