深部钻探装备智能运维实践探索

A practical exploration of intelligent operation and maintenance system for deep drilling equipment

摘要:

深部钻探作业环境极端，关键部件易产生磨损、疲劳与密封失效等问题，传统以定期检修和事后维修为主的运维模式难以满足连续作业与安全运行要求。本文面向深部钻探装备智能运维需求，系统分析了用户角色、系统部署、数据采集与传输、应用场景及功能配置等关键需求；提出了多源异构数据集成与传输技术方案，形成了WITSML/ETP、OPC UA与Kafka协同的集成方法；构建了“云-边-端”协同的分布式弹性架构，引入K3s与KubeEdge实现边缘服务部署与边云协同，采用时序数据库与图数据库支撑数据与知识管理。在此基础上，完成智能运维系统原型软件实现，集成数字孪生、实时监测与故障预警等功能，并以钢丝绳电磁检测为例验证了系统在工程条件下的适用性。研究为深部钻探装备智能运维提供了可落地的技术架构与实现路径，对提升极端工况下装备运行可靠性与运维效率具有工程参考价值。

Abstract:

The operating environment for deep drilling is extreme， where critical components are prone to issues such as wear， fatigue， and seal failure. Traditional operation and maintenance （O&M） models， primarily based on scheduled maintenance and reactive repair， struggle to meet the requirements for continuous operation and operational safety. Addressing the intelligent O&M requirements for deep drilling equipment， this paper systematically analyzes key requirements including user roles， system deployment， data acquisition and transmission， application scenarios， and functional configuration. It proposes a technical scheme for multi-source heterogeneous data integration and transmission， establishing an integrated approach that coordinates WITSML/ETP， OPC UA， and Kafka. A distributed and elastic "cloud-edge-device" collaborative architecture is constructed， introducing K3s and KubeEdge for edge service deployment and cloud-edge collaboration， while employing time-series databases and graph databases for data and knowledge management. Based on this， a prototype software for the intelligent O&M system is developed， integrating functions such as digital twin， real-time monitoring， and early fault warning. The applicability of the system under engineering conditions is validated using steel wire rope electromagnetic inspection as a case study. This research provides an implementable technical architecture and pathway for intelligent O&M of deep drilling equipment， offering engineering reference value for enhancing equipment operational reliability and O&M efficiency under extreme conditions.