The BAseline Risk assessment Tool (BART) is currently used by the Eni oil and gas company for the living risk assessment of oil and gas upstream plants. BART combines a simplified Quantitative Risk Assessment (QRA) with a Bow-Tie (BT) approach. In this work, we implemented in BART the capabilities for considering the degradation of the barriers, which affects the safety performance. For this, we resort to a multistate Bayesian Network (BN), which maps the BT of BART and whose nodes correspond to the safety barriers, each one characterized by a Health State (HS) and by a Failure Probability (FP). HS is assessed on the basis of specific Key Performance Indicators (KPIs), whereas FP is quantified from failure datasets (for technical barriers), Human Reliability Analysis (HRA) (for operational and organizational barriers) or the Analytic Hierarchy Process (AHP) based on expert elicitation (for barriers for which data are lacking). The proposed BN approach is applied to the barriers designed for limiting the consequences of a release in the slug catcher (i.e., Flash Fire (FF), Jet Fire (JF), Pool Fire (PF), Explosion (EX) or Toxic Dispersion (TX)) of the upstream onshore plant. The results of the assessment are benchmarked with those obtained with the original BART and show that the BN approach adds the capability of providing an accurate and updatable description of the barrier conditions in the risk assessment of the plant during its life.

A multistate bayesian network for accounting the degradation of safety barriers in the living risk assessment of oil and gas plants

Di Maio F.;Scapinello O.;Zio E.;
2020-01-01

Abstract

The BAseline Risk assessment Tool (BART) is currently used by the Eni oil and gas company for the living risk assessment of oil and gas upstream plants. BART combines a simplified Quantitative Risk Assessment (QRA) with a Bow-Tie (BT) approach. In this work, we implemented in BART the capabilities for considering the degradation of the barriers, which affects the safety performance. For this, we resort to a multistate Bayesian Network (BN), which maps the BT of BART and whose nodes correspond to the safety barriers, each one characterized by a Health State (HS) and by a Failure Probability (FP). HS is assessed on the basis of specific Key Performance Indicators (KPIs), whereas FP is quantified from failure datasets (for technical barriers), Human Reliability Analysis (HRA) (for operational and organizational barriers) or the Analytic Hierarchy Process (AHP) based on expert elicitation (for barriers for which data are lacking). The proposed BN approach is applied to the barriers designed for limiting the consequences of a release in the slug catcher (i.e., Flash Fire (FF), Jet Fire (JF), Pool Fire (PF), Explosion (EX) or Toxic Dispersion (TX)) of the upstream onshore plant. The results of the assessment are benchmarked with those obtained with the original BART and show that the BN approach adds the capability of providing an accurate and updatable description of the barrier conditions in the risk assessment of the plant during its life.
2020
Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference
978-981-14-8593-0
Bayesian Networks
Degradation
Multistate Variables
Risk Assessment
Safety Barriers
Slug Catcher
Upstream Onshore Plant
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1181045
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