A knowledge graph-driven approach for drilling stuck pipe detection and analysis

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.