Application of Hierarchical Colored Petri Nets for Real-Time Condition Monitoring of Internal Blowout Prevention (IBOP) in Top Drive Assembly System

摘要

Offshore oil drilling is a complex process that requires a careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring signs of changes in operating conditions of a machine are critical to the overall oil production cycle. In this paper we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work we aim to design an intelligent system for monitoring the health of IBOP system based using multisensory data. The process comprises two steps: 1) produce IBOP system logical behavior analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop a pattern recognition Neural Networks system for activity monitoring and fault detection for the top drive assembly. HCPN allows simulation and graphical visualization of dynamic discrete process and provides means to identify bottlenecks, deadlocks and optimization parameters. This work presents preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP vale in top drive assembly. The effects of failure rate and repair time of each component on system performance are researched.