Abstract
For the design and operation of Oil and Gas (O&G) facilities, a Quantitative Risk Assessment (QRA) should be performed to quantify the risk of major accidents due to multiple hazards and sources at the plant level, thus allowing the effective identification and allocation of safety barriers. In this work, a novel approach for the multi-hazard and multi-source aggregation of risks is proposed, accounting for the uncertainties typically unexpressed in a conventional QRA (both on the frequency and severity of the accidental scenarios). The multi-hazard risk assessment framework proposed is applied to assess the Location-Specific Individual Risk (LSIR) for a representative Upstream O&G plant (case study), using a model based on multistate Bayesian Networks (BNs) for different functional units, each one undergoing an initiating event of Loss Of Primary Containment (LOPC). Estimates of frequency and severity for each possible accident scenario are aggregated to eventually calculate the overall LSIR. Moreover, LSIR's confidence intervals are provided to describe the uncertainty associated to the estimates, and the frequency and severity contributions to risk are derived for targeted prioritization of the safety barriers in view of the risk reduction.
Safety barriers in the chemical process industries: A state-of-the-art review on their classification, assessment, and management
