钻探工程2026，Vol.53Issue(2)：57-67,11.DOI:10.12143/j.ztgc.2026.02.006
基于知识图谱的钻井阻卡监测与分析方法
A knowledge graph-driven approach for drilling stuck pipe detection and analysis
摘要
Abstract
To address the frequent occurrence of stuck pipe incidents during drilling,the reliance on empirical diagnosis,and the lack of interpretability in intelligent models,this paper proposes a knowledge graph-based monitoring and analysis method for stuck pipe.Given the multi-source,heterogeneous,and highly specialized nature of stuck-pipe-related knowledge,a systematic workflow was established for knowledge graph construction,comprising:ontology design,multi⁃source data preprocessing,knowledge extraction,and graph visualization.Through a top-down ontology design,core entities such as stuck-pipe types,influencing factors,characteristic features,and mitigation measures were defined.Based on this framework,a BERT-BiLSTM-CRF model was employed to extract knowledge from unstructured texts,achieving an F1-score of 88.2％.Approximately 2000 structured entities were derived from 327 historical cases and integrated with structured time-series stuck-pipe sample data to construct a multimodal knowledge graph for stuck-pipe analysis.Furthermore,a stuck-pipe identification method combining data similarity computation and knowledge graph retrieval was introduced,significantly enhancing the interpretability of the diagnostic process.In addition,an intelligent question-answering system with strong human-machine interaction capabilities was developed for field applications.Adopting an"input parsing-intent classification-knowledge retrieval-answer generation"architecture,the system can quickly provide outputs including stuck-pipe types,causal analysis,and control recommendations.This research achieves effective integration of textual drilling knowledge and real-time monitoring data,markedly improving the intelligence and interpretability of stuck-pipe diagnosis.It offers a novel technical approach and engineering reference for the safe and efficient drilling of deep,ultra-deep,and unconventional oil and gas wells.关键词
知识图谱/智能钻井/卡钻/知识抽取/智能问答系统Key words
knowledge graph/intelligent drilling/stuck pipe/knowledge extraction/intelligent question-answering system分类
能源科技引用本文复制引用
张诚恺,刘子豪,宋先知,祝兆鹏,王建龙,贾亿博,朱林,刘慕臣,王正..基于知识图谱的钻井阻卡监测与分析方法[J].钻探工程,2026,53(2):57-67,11.基金项目
油气重大专项(编号:2025ZD1404600) （编号:2025ZD1404600）
中国石油大学(北京)科研基金(编号:2462025XKBH009) （北京）
国家自然科学基金项目(编号:52474015) （编号:52474015）
