深部钻探装备智能运维实践探索OA

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.