Offshore oil drilling is a complex process that requires 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 operating conditions of drilling systems are critical to the overall 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 using discrete event systems (DES) based control method in combination with multivariate time series classification deep learning method. The proposed system comprises two stages: 1) produce IBOP system logical behaviour analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop an activity detection or a classifier module using reservoir computing framework for classification of multivariate time series for activity monitoring and fault detection for the top drive assembly.
The combination of these methods would enable automation of monitoring and early detection of incidents during drilling operations. We present the preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP valve in top drive assembly and activity classification of activities relevant to IBOP condition monitoring. The effects of failure rate and repair time of each component on system performance are to be researched at a later stage.
Petri Nets, Blowout prevention system, automated control and monitoring, Oil and Gas Safety, Discrete Event Systems, Machine Learning
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.