A Review and Optimisation of Risk Management Protocols for ROVs in Oil and Gas operation on the Norwegian Continental Shelf.

Abstract

The oil and gas industry is a rapidly evolving sector. As technologies advance, there is a corresponding need to develop or adapt new techniques to address emerging challenges. This study presents and critically examines the theoretical basis of risk management and static risk assessment methods, such as Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA), in the context of managing risks in complex processes involving remotely operated vehicles (ROVs) on the Norwegian continental shelf. It outlines all factors and limitations associated with such operations and explores potential improvements to optimise these processes.

The study goes beyond merely reviewing the limitations of these static methods. It interprets semi-quantitative results and, importantly, provides a theoretical foundation for optimising and improving operational management ROV risk management. This is done by considering the interaction between human, machine, and environmental factors through dynamic risk assessment frameworks developed to address the limitations of static risk assessments, which often fail to adapt to the dynamic nature of certain systems.

Interactions and limitations from external and internal factors are captured through various methods, including Fault Tree Analysis, the GO method, the human reliability method CREAM, and the Bayesian Network. These are fused to create a dynamic Bayesian Network model designed to address the limitations presented by each individual method. Consequently, risk management optimisation for ROV operations can be achieved through dynamic modelling processes tailored to the environmental conditions of the chosen geographical location.