Dynamics of severe accidents in the oil & gas energy sector derived from the authoritative ENergy-related severe accident database

Abstract

Organized into a global network of critical infrastructures, the oil & gas industry remains to this day the main energy contributor to the world’s economy. Severe accidents occasionally occur resulting in fatalities and disruption. We build an oil & gas accident graph based on more than a thousand severe accidents for the period 1970–2016 recorded for refineries, tankers, and gas networks in the authoritative ENergy-related Severe Accident Database (ENSAD). We explore the distribution of potential chains-of-events leading to severe accidents by combining graph theory, Markov analysis and catastrophe dynamics. Using centrality measures, we first verify that human error is consistently the main source of accidents and that explosion, fire, toxic release, and element rupture are the principal sinks, but also the main catalysts for accident amplification. Second, we quantify the space of possible chains-of-events using the concept of fundamental matrix and rank them by defining a likelihood-based importance measure γ. We find that chains of up to five events can play a significant role in severe accidents, consisting of feedback loops of the aforementioned events but also of secondary events not directly identifiable from graph topology and yet participating in the most likely chains-of-events.

Risk assessment of methanol storage tank fire accident using hybrid FTA-SPA

“…One of the analytical tools is deductive FTA which is broadly employed to clarify the direct and indirect root causes [54].…”

Involving Risk Reduction Practitioners and Other Experts in the Management of Super-Catastrophes via an Online Interactive Platform

“…In practice, we can estimate the probability of a chain-ofk-events Z k (z 1 , z 2 , /, z k ) using the Markov property so that p Z k k−1 τ 1 a zτ ,zτ+1 . The probability of a specific chain can then be ranked against all other chains-of-k-events (Mignan et al, 2022). Note that for the conservation of probabilities, an outflow event representing an absorbing state at which the cascade dies off must be added to the adjacency matrix .…”

“…Generic examples are used to illustrate the range of possible interactions, which are physically plausible and may occur at the macro-scale or microscale. By micro-scale, we mean the interactions that occur within an event, specifically in critical infrastructure failures (e.g., Matos et al, 2015;Mignan et al, 2022-…”

“…Worldwide, many other perils can lead to domino effects, such as epidemics and wildfires to only cite a few more. Man-made disasters directly triggered by malicious acts, malfunction, or human error (Chernov and Sornette, 2016) are more localized, but can also trigger numerous negative chains-of-events (e.g., Mignan et al, 2022).…”

A Digital Template for the Generic Multi-Risk (GenMR) Framework: A Virtual Natural Environment

“…Past developments include the basic GenMR framework with initial tests on abstract data models [ 26 ], an application on a generic hydro-dam [ 28 ], an application for large earthquake clustering and damage-dependent vulnerability [ 27 ], and various other subprojects dedicated to the description of the GenMR adjacency matrix. Those include the exploration of the space of possibilities in cascading disasters within and across the natural, technological, and socio-economic systems (~20 perils) [ 25 ], the estimation of conditional probabilities for chains-of-accidents in the oil and gas sector [ 29 ], and methods to build and fill the adjacency matrix using expert judgment, reasoned imagination, and wisdom of the crowd [ 24 , 30 ]. Another subproject investigated how GenMR could be integrated under the umbrella of multi-risk governance [ 3 , 5 , 31 ].…”