地质钻进故障特征知识图谱构建及智能诊断方法

杨豫龙; 曹卫华; 黎育朋; 甘超

doi:10.3799/dqkx.2026.112

Volume 51 Issue 8

Aug. 2026

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Article Navigation > Earth Science > 2026 > 51(8): 3076-3086

Yang Yulong, Cao Weihua, Li Yupeng, Gan Chao, 2026. Fault Feature Knowledge Graph Construction for Geological Drilling and Its Application to Intelligent Diagnosis. Earth Science, 51(8): 3076-3086. doi: 10.3799/dqkx.2026.112

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Yang Yulong, Cao Weihua, Li Yupeng, Gan Chao, 2026. Fault Feature Knowledge Graph Construction for Geological Drilling and Its Application to Intelligent Diagnosis. Earth Science, 51(8): 3076-3086. doi: 10.3799/dqkx.2026.112

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Fault Feature Knowledge Graph Construction for Geological Drilling and Its Application to Intelligent Diagnosis

doi: 10.3799/dqkx.2026.112

Yang Yulong^{1, 2, 3
,
,},
Cao Weihua^{1, 2, 3
,
,
,},
Li Yupeng^{1, 2, 3},
Gan Chao^{1, 2, 3}

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

Received Date: 2026-02-13
Publish Date: 2026-08-25

Abstract

Abstract

Geological drilling involves high complexity and risk, making interpretable and interactive fault diagnosis systems essential for ensuring operational safety and efficiency. This paper proposes a structured representation framework for the complex knowledge and failure mechanisms of geological drilling to enable structured representation and storage of drilling knowledge. To construct a knowledge graph for the characteristics of geological drilling faults, long-range dependency analysis and composite-structure entity recognition are developed to automatically extract professional knowledge from drilling fault literature. Based on the constructed knowledge graph, a knowledge-driven drilling fault diagnosis system is further developed.Knowledge extraction from 21 publicly available publications results in a fault feature knowledge graph containing 3 121 nodes and 1 301 relations. Experimental results demonstrate that the proposed system achieves performance comparable to ChatGPT-5.2 on five drilling fault knowledge query tasks and outperforms mainstream commercial large language models on two fault diagnosis tasks.
- Geological drilling,
- Fault early warning,
- Knowledge graph,
- Large language model,
- Agent

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References(34)

References

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