基于知识图谱的钻井阻卡监测与分析方法OA

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.