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.

In oil and gas upstream plants, several barriers (technical, procedural and organizational) are in place to prevent and mitigate accidents. Proper safety barriers functionality is, then, important to control the risk during the life of the plants. Safety barriers modelling is, then, required for risk assessment. In this work, we model the barriers functionality by discrete Health States (HSs) and their stochastic process of transition by a multistate Bayesian Network (BN). For each barrier, the HS is defined with reference to properly defined Key Performance Indicators (KPIs). Here, for technical barriers that can be continuously monitored, we propose a specific KPI based on Probabilistic Safety Margins (PSMs). Its application is illustrated with respect to the Process Control System (PCS) of the slug catcher of the upstream plant, which continuously controls the process pressure of the system within a specific operational range.

The Analytical Hierarchy Process (AHP) is a decision-making method capable of handling qualitative and quantitative elements. In this work, it is used to estimate the Failure Probabilities (FPs) of safety barriers (technical, procedural and organizational), within the framework of risk assessment of an oil and gas upstream plant. The barriers are modelled by a multistate Bayesian Network (BN) and FPs are defined for each barrier relative to their Health States (HSs). Expert elicitation procedures are adopted for feeding the AHP and a practical illustration is provided with regards to the safety barriers of the slug catcher of the upstream plant.

An effective process safety management in O&G assets is ensured through multiple and independent barriers aimed at preventing the occurrence of major accidents and/or limiting the severity of consequences that such events may cause on people, environment, asset and Company reputation. The evaluation of the barrier integrity and the identification of performance requirements of barriers elements has been part of the risk methodology developed to assess the risk of major accidents in accordance with the EU Directive on offshore safety. The barriers, identified by the bow-tie methodology, are evaluated considering the effective level of their implementation and the global effectiveness of each barrier with a dedicated performance card, linking the evaluation criteria with specific performance factors, derived from SPAR-H methodology, based on the Functionality (F), Availability (A), Reliability (R), Survivability (S) and Interdependency (I) criteria. In the evaluation of hardware/physical barriers, system reliability and availability are also included in the analysis in order to consider the highest level of integrity associated to hardware contributions.

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