Research on Knowledge-Embedded Digital Twin Modeling Approach for Drilling Wellbores

Current drilling operations face challenges such as dispersed multi-source data, severe information isolation, and the disconnection between design and field implementation, which lead to insufficient decision-making basis, low operational efficiency, and delayed risk response. Digital twin technology provides a feasible approach to address these issues. However, existing wellbore digital twin models still suffer from problems of delayed data updating, insufficient multi-source data integration, and loose profession cooperation, making it difficult to meet the requirements of precise and real-time intelligent decision-making. To address these problems, a knowledge-embedded digital twin modeling method for drilling wellbores was proposed. Focusing on a wellbore stability mechanics model, the method integrated mechanics-based modeling, data-driven algorithms, and natural language processing techniques and incorporated ontologies, knowledge graphs, and rule bases to achieve semantic enhancement and semantic alignment of multi-source information. A multi-layer architecture including data acquisition, storage, modeling analysis, and decision support was constructed. This method achieved real-time mapping of wellbore states and automatically parsed unstructured records such as drilling design documents to generate structured knowledge and optimize operational workflows. A case study on a coalbed methane well was conducted to validate the method in wellbore stability prediction, rate of penetration optimization, and automated operation instruction generation. The results show that the model can monitor downhole performance in real time, accurately predict potential risks, and generate optimized decision-making schemes. The rate of penetration in the test interval increases by 12.1%, and the efficiency of operational document processing improves by more than 80%. The proposed method significantly enhances the perception, prediction, and operational applicability of digital twin systems, providing a new technical pathway for safety assurance, speed and efficiency improvement, and intelligent decision-making in complex drilling operations.