Probabilistic Risk Assessment of Oil and Gas Infrastructures for Seismic Extreme Events

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Abstract

Modern economy and society are constantly highly dependent on a variety of critical infrastructures (CIs) over the last decades. However an observation and review of the extreme events that occurred during the last two decades reveal that while the interdependencies between the CIs are growing and getting more complex there is an increasing gap between the growing risk and the actual preparedness. Therefore, there is an utmost importance to ensure reliable and robust performance of critical infrastructures on a continuous basis, particularly during and after the occurrence of extreme events. A decision support tool for decision makers to appraise and mitigate the risk of CIs after the occurrence of seismic events is developed. The methodology analyzes the damage of critical infrastructure components by Fault-Tree-Analysis, Decision Trees and Fragility Curves. Though the methodology is suitable for a variety of critical infrastructures, this study will focus on critical Oil and Gas Network Systems, which are vital to the energy supply infrastructure of Israel. In order to assess the risk that Oil and Gas Critical Infrastructures are exposed to in case of seismic extreme events, fragility curves are derived and adjusted to different components of the Oil and Natural Gas systems. Subsequently, a variety of possible seismic scenarios are examined and analyzed in order to determine the damage of the components. The overall expected damage of the Oil or Gas system is assessed by considering the damage state of all components of the system. The expected damage states of the components disclose and emphasize the most vulnerable parts of the system. Also discussed are guiding principles for decision makers for risk informed mitigation on benefit-cost-ratio analysis.

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APA

Urlainis, A., Shohet, I. M., & Levy, R. (2015). Probabilistic Risk Assessment of Oil and Gas Infrastructures for Seismic Extreme Events. In Procedia Engineering (Vol. 123, pp. 590–598). Elsevier Ltd. https://doi.org/10.1016/j.proeng.2015.10.112

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