Insurability risk assessment of oil refineries using Bayesian Belief Networks

Refineries are highly complex installations and a potential source of major hazards. Due to the large volumes flammable toxic substances present, an accident in refinery may have multidimensional consequences. This includes severe property damages, injuries personnel, releases chemicals causing adverse health effects on nearby residents environment, business interruption losses that lead company bankruptcy. paper looks at risk profile refineries from insurers’ perspective. A top down approach is employed derive key performance indicators (KPIs) for two types events historically known as main causes accidents refineries, i.e. fire vapor cloud explosion. Bayesian Belief Networks (BBNs) used develop probabilistic model quantifying indication explosion via structured elicit synthesize available knowledge domain experts. Three KPIs modelled BBN nodes: quantitative, qualitative directional linked technical, human change trend factors, respectively. The proposed has twofold practical use: i) support insurers assess which plants low exposures; ii) inform about their own profile, thus supporting them with assessment implementation reduction measures. To ensure applicability across industry, systematic development detailed extension inclusion modules accounting further discussed.