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:

为应对钻井过程中卡钻事故频发、诊断依赖经验、智能模型可解释性不足等问题，本文提出了一种基于知识图谱的钻井阻卡监测与分析方法。针对卡钻知识的多源、异构及专业性强等特点，形成了“本体设计-多源数据预处理-知识抽取-图谱可视化”的知识图谱构建流程。通过自顶向下的本体设计定义卡钻类型、影响因素、表征特征与处置措施等核心节点。在此基础上，利用BERT-BiLSTM-CRF模型实现非结构化文本知识抽取，F1分数达88.2%，从327例历史案例中提取约2000个结构化实体，并结合结构化时序卡钻样本数据，构建阻卡分析多模态知识图谱。进一步提出了一种融合数据相似度计算与知识图谱检索的阻卡识别方法，有效提升了诊断过程的可解释性。同时，设计了面向现场应用且具备良好人机交互性能的智能问答系统，该系统采用“输入解析-意图分类-知识检索-答案生成”架构，能够快速输出阻卡类型、成因分析与调控建议。本研究实现了钻井文本知识与实时监测数据的有效融合，显著提升了阻卡诊断的智能化水平与决策的可解释性，为深层、超深层及非常规油气的安全高效钻井提供了新的技术手段和工程参考。

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.