In this work, we integrate in the multistate Bayesian Network (BN) modelling approach developed in (Di Maio et al., 2020a) i) the Modelling of Ignition Sources on Offshore oil and gas Facilities (MISOF) for characterizing the mitigative safety barriers and ii) a probit modelling for ultimately evaluating the severity of the accident scenarios (namely, Flash Fire (FF), Jet Fire (JF), Pool Fire (PF), Explosion (EX) or Toxic Dispersion (TX)) and properly assessing the probability of fatality following an accident by considering the actual effects of the mitigative safety barriers in place. The proposed approach is applied to a case study concerning a Loss of Primary Containment (LOPC) accident in the slug catcher of a representative onshore Oil & Gas (O&G) plant.
A multistate Bayesian Network integrating MISOF and probit modelling for the risk assessment of oil and gas plants
Di Maio F.;Scapinello O.;Zio E.;
2021-01-01
Abstract
In this work, we integrate in the multistate Bayesian Network (BN) modelling approach developed in (Di Maio et al., 2020a) i) the Modelling of Ignition Sources on Offshore oil and gas Facilities (MISOF) for characterizing the mitigative safety barriers and ii) a probit modelling for ultimately evaluating the severity of the accident scenarios (namely, Flash Fire (FF), Jet Fire (JF), Pool Fire (PF), Explosion (EX) or Toxic Dispersion (TX)) and properly assessing the probability of fatality following an accident by considering the actual effects of the mitigative safety barriers in place. The proposed approach is applied to a case study concerning a Loss of Primary Containment (LOPC) accident in the slug catcher of a representative onshore Oil & Gas (O&G) plant.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
