Utilising Bayesian networks to demonstrate the potential consequences of a fuel gas release from an offshore gas-driven turbine

Abstract

This research proposes the application of Bayesian networks in conducting quantitative risk assessment of the integrity of an offshore gas driven turbine, used for electrical power generation. The focus of the research is centred on the potential release of fuel gas from a turbine and the potential consequences that follow the said release, such as fire, explosion and damage to equipment within the electrical generation module. The Bayesian network demonstrates the interactions of potential initial events and failures, hazards, barriers and consequences involved in a fuel gas release. This model allows for quantitative analysis to demonstrate partial verification of the model. The verification of the model is demonstrated in a series of test cases and through sensitivity analysis. Test case 1 demonstrates the effects of individual and combined control system failures within the fuel gas release model; 2 demonstrates the effects of the 100% probability of a gas release on the Bayesian network model, along with the effect of the gas detection system not functioning; and 3 demonstrates the effects of inserting evidence as a consequence and observing the effects on prior nodes.