钻探装备虚实融合智能运维关键技术及应用研究

Intelligent operation and maintenance key technology and application of virtual–physical fusion for drilling equipment

摘要

随着深层–超深层钻探需求增加，复杂工况下钻探装备的智能运维面临效率与安全双重挑战，传统机理或数据驱动方法难以满足动态适应与闭环优化的要求，虚实融合技术成为赋能智能运维的必然选择. 本文综述了钻探装备在深层–超深层环境下运维的多重挑战，系统总结了虚实融合技术在提升多源数据挖掘潜力、机理–数据融合能力以及决策智能化水平的必要性，提出了数字孪生驱动的钻探装备虚实融合智能运维技术体系，通过钻前风险精准评估、钻中设备智能管理和钻后数据挖掘优化服务全面提升钻探装备运维水平. 最后，基于虚实融合智能运维技术体系在防喷器性能评估、提升系统智能监测以及钻井泵数据挖掘优化方面进行了实践应用，为虚实融合智能运维技术在油气领域应用提供参考.

Abstract

The growing demand for deep and ultra-deep drilling operations presents dual challenges in terms of efficiency and safety for the intelligent operation and maintenance of drilling equipment under complex working conditions. Conventional methodologies, whether relying solely on physical mechanisms or purely driven by data analytics, often fall short in delivering the dynamic adaptability and closed-loop optimization required in modern drilling contexts. Consequently, the integration of virtual and physical realms has emerged as an essential and enabling pathway toward achieving truly intelligent operation and maintenance. This study begins by systematically analyzing the multifaceted challenges encountered in the operation and maintenance of drilling equipment in deep and ultra-deep environments. These challenges span five critical and interconnected levels: at the equipment level, unclear failure propagation mechanisms in highly coupled systems; at the environmental level, extreme pressures, temperatures, and restricted sensing conditions; at the data level, heterogeneity, noise, and low utilization of multisource information; at the model level, the paradigm divide between physics-based and data-driven approaches; at the decision-making level, reliance on static rules and delayed human intervention. This analysis underscores the necessity of virtual–physical fusion technology that significantly enhances the potential for mining multi-source data, deepens the integration of mechanistic principles with data-driven insights, and elevates the overall intelligence of operational decisions. A digital twin-driven framework for virtual–physical fusion in intelligent operation and maintenance is proposed for drilling equipment. The framework is designed around four cohesive layers: intelligent perception, twin model, virtual–physical mapping, and operation and maintenance service. The intelligent perception layer employs advanced multidimensional data acquisition technologies, including physical sensors and virtual sensing techniques, to provide a real-time, comprehensive stream of status information from the formation, downhole tools, and surface equipment, thereby forming a reliable and holistic data foundation for the entire system. As the core of the framework, the twin model layer constructs high-fidelity multidomain digital twins that integrate mechanical, electrical, hydraulic, and control subsystems. These models achieve an accurate mapping of the state, behavior, and dynamic responses of the physical equipment, enabling precise simulation, fault propagation analysis, and forward-looking predictive assessment under diverse operational scenarios. The virtual–physical mapping layer ensures dynamic consistency and supports closed-loop optimization by maintaining a continuous bidirectional interaction and real-time state synchronization between the physical entity and its virtual counterpart. Furthermore, this layer enables model calibration and iterative refinement based on the streaming data, guaranteeing that the digital twin remains a faithful representation throughout the equipment lifecycle. Finally, the operation and maintenance service layer aims to reduce the drilling risk, enhance equipment reliability, and improve operational safety. The practical value of this virtual-physical fusion operation and maintenance framework is demonstrated through its application in several key areas: performance evaluation of blowout preventers under diverse shear conditions, intelligent monitoring and early fault detection for hoisting systems using digital twin-driven anomaly diagnosis, and data-driven optimization for drilling pumps through virtual–real data fusion and feature selection. These implementations validate the effectiveness of the framework in enhancing operational awareness, reducing unplanned downtime, and optimizing maintenance resources. Furthermore, they provide a valuable reference for the broader adoption and continued development of intelligent virtual–physical fusion solutions in the oil and gas industry.

关键词

钻探装备 / 虚实融合 / 智能运维 / 数字孪生 / 智能管理

Key words

drilling equipment / virtual–physical fusion / intelligent operation and maintenance / digital twin / intelligent management

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基金资助

国家重点研发计划资助项目(2024YFB3409303)

国家自然科学基金资助项目(52474274)