地质钻进故障特征知识图谱构建及智能诊断方法

doi:10.3799/dqkx.2026.112

地质钻进故障特征知识图谱构建及智能诊断方法

doi: 10.3799/dqkx.2026.112

杨豫龙^1,2,3,
曹卫华^1,2,3, ,,
黎育朋^1,2,3,
甘超^1,2,3

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

基金项目:

国家自然科学基金项目(No.62333019,62503440).湖北省自然科学基金项目(No.2025AFB022).湖北省中央引导地方科技发展专项(No.2025CSA122).

详细信息

作者简介:
杨豫龙（2000-）,男,博士研究生,研究方向为地质勘探智能感知与决策系统.ORCID:0009-0009-9025-8657.E-mail： yul@cug.edu.cn

通讯作者:
曹卫华（ 1972-）,男,教授,ORCID:0000-0002-9677-9586.E-mail:weihuacao@cug.edu.cn

中图分类号: P634
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出版历程
- 收稿日期: 2026-01-14
- 网络出版日期: 2026-05-13

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

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

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

摘要

摘要: 地质钻进工程难度大、风险高，设计可解释、可交互的钻进故障诊断系统，帮助司钻人员及时发现和排除井下故障，对保障钻进过程的安全和效率意义重大。围绕这一目标，本文提出一套针对地质钻进复杂知识和故障机理的结构化表征框架，收集整理钻进故障文献库，针对文献上下文关联强、专业名词形式复杂的特点设计长程依赖关系分析和复合结构实体识别算法，抽取文献中的知识，自动构建钻进故障特征知识图谱，实现知识驱动的钻进故障智能诊断。对21篇公开出版物进行知识抽取，构建了一个具备3,121个节点、1,301则关系的钻进故障特征知识图谱，故障诊断系统在5项钻进故障知识查询任务上性能达到ChatGPT-5.2同等水平，在2项故障诊断任务上表现优于主流商业大语言模型。
- 地质钻进 /
- 故障预警 /
- 知识图谱 /
- 大语言模型 /
- 智能体
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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参考文献(34)

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地质钻进故障特征知识图谱构建及智能诊断方法

doi: 10.3799/dqkx.2026.112

作者简介:
杨豫龙（2000-）,男,博士研究生,研究方向为地质勘探智能感知与决策系统.ORCID:0009-0009-9025-8657.E-mail： yul@cug.edu.cn

通讯作者:
曹卫华（ 1972-）,男,教授,ORCID:0000-0002-9677-9586.E-mail:weihuacao@cug.edu.cn

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

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目录

留言板

地质钻进故障特征知识图谱构建及智能诊断方法

doi: 10.3799/dqkx.2026.112

作者简介: 杨豫龙（2000-）,男,博士研究生,研究方向为地质勘探智能感知与决策系统.ORCID:0009-0009-9025-8657.E-mail： yul@cug.edu.cn

通讯作者: 曹卫华（ 1972-）,男,教授,ORCID:0000-0002-9677-9586.E-mail:weihuacao@cug.edu.cn

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出版历程

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

计量

出版历程

目录

作者简介:
杨豫龙（2000-）,男,博士研究生,研究方向为地质勘探智能感知与决策系统.ORCID:0009-0009-9025-8657.E-mail： yul@cug.edu.cn

通讯作者:
曹卫华（ 1972-）,男,教授,ORCID:0000-0002-9677-9586.E-mail:weihuacao@cug.edu.cn