王钧泽,李黔,尹虎. 基于数字孪生技术的钻井复杂风险智能预警系统架构[J]. 石油钻探技术,2024,52(5):1-10. DOI: 10.11911/syztjs.2024082
引用本文: 王钧泽,李黔,尹虎. 基于数字孪生技术的钻井复杂风险智能预警系统架构[J]. 石油钻探技术,2024,52(5):1-10. DOI: 10.11911/syztjs.2024082
WANG Junze, LI Qian, YIN Hu. Intelligent Risk Early Warning System Architecture for Complex Drilling Based on Digital Twin Technology[J]. Petroleum Drilling Techniques, 2024, 52(5):1-10. DOI: 10.11911/syztjs.2024082
Citation: WANG Junze, LI Qian, YIN Hu. Intelligent Risk Early Warning System Architecture for Complex Drilling Based on Digital Twin Technology[J]. Petroleum Drilling Techniques, 2024, 52(5):1-10. DOI: 10.11911/syztjs.2024082

基于数字孪生技术的钻井复杂风险智能预警系统架构

Intelligent Risk Early Warning System Architecture for Complex Drilling Based on Digital Twin Technology

  • 摘要: 为降低深部地层不确定地质条件诱发的钻井复杂风险,采用数字孪生技术,构建了基于物理模型与数据驱动模型融合的钻井复杂风险数字孪生智能预警体系。为满足随钻预警、降低风险等实际需求,提出了基于微服务的数据集成、孪生体智能感知、多模态融合预警、孪生体智能诊断4项数字孪生预警系统支撑技术,建立了钻井数字孪生预警系统的整体架构并详述了其功能及模型设计,涉及物理设备层、虚拟实体层、孪生体数据层、孪生体算法模型层及孪生体预警服务层5层交互系统,设计了钻前预演规避风险、钻中实时预警、钻后分析区块风险情况,优化区块钻井设计的3种应用场景,实现了对多源异构数据的数字化集成,传统物理模型与智能模型的多重融合,以及溢流、井漏、卡钻的预警与类型识别等功能,从而达到降低深部钻井作业风险、优快钻井的目的。研究结果表明,基于“模型+数据”的数字孪生预警架构具有提前识别钻井过程风险和快速诊断风险类型的潜力,为智能钻井预警监测技术提供了新的方式和途径。

     

    Abstract: To reduce the complex risks of drilling induced by uncertain geological conditions in deep strata, a digital twin technology-based intelligent early warning system was developed. This system integrates physical models with data-driven models. To address practical needs such as real-time warnings and risk reduction, four supporting technologies were proposed for the digital twin early warning system: microservice-based data integration, intelligent perception of the digital twin, multimodal fusion warning, and intelligent diagnosis of the digital twin. The overall architecture of the drilling digital twin early warning system was established and its functions and model design were detailed. It includes a five-layer interaction system: physical device layer, virtual entity layer, digital twin data layer, digital twin algorithm model layer, and digital twin warning service layer. Three application scenarios were designed: pre-drilling risk avoidance, real-time warning during drilling, and post-drilling analysis of block risk situations to optimize block drilling design. This system achieves digital integration of multi-source heterogeneous data, fusion of traditional physical models with intelligent models, and functions such as overflow, wellbore loss, and stuck pipe warning and type identification. This reduces the risks associated with deep drilling operations and optimizes drilling efficiency. The study results indicate that the "model + data" digital twin early warning architecture has the potential for early identification of drilling process risks and rapid diagnosis of risk types. Digital twin technology provides new approaches and methods for intelligent drilling early warning and monitoring, which is significant for advancing its application in smart and autonomous drilling.

     

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