RESUMO
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.
Assuntos
Acidentes de Trabalho/estatística & dados numéricos , Acidentes/estatística & dados numéricos , Bases de Dados Factuais , Indústrias Extrativas e de Processamento/estatística & dados numéricos , Campos de Petróleo e Gás/química , Humanos , Fatores de RiscoRESUMO
We analyze the risk of severe fatal accidents causing five or more fatalities and for nine different activities covering the entire oil chain. Included are exploration and extraction, transport by different modes, refining and final end use in power plants, heating or gas stations. The risks are quantified separately for OECD and non-OECD countries and trends are calculated. Risk is analyzed by employing a Bayesian hierarchical model yielding analytical functions for both frequency (Poisson) and severity distributions (Generalized Pareto) as well as frequency trends. This approach addresses a key problem in risk estimation-namely the scarcity of data resulting in high uncertainties in particular for the risk of extreme events, where the risk is extrapolated beyond the historically most severe accidents. Bayesian data analysis allows the pooling of information from different data sets covering, for example, the different stages of the energy chains or different modes of transportation. In addition, it also inherently delivers a measure of uncertainty. This approach provides a framework, which comprehensively covers risk throughout the oil chain, allowing the allocation of risk in sustainability assessments. It also permits the progressive addition of new data to refine the risk estimates. Frequency, severity, and trends show substantial differences between the activities, emphasizing the need for detailed risk analysis.
Assuntos
Acidentes de Trabalho , Teorema de Bayes , Indústrias Extrativas e de Processamento , Petróleo , Medição de Risco/métodos , Bases de Dados Factuais , HumanosRESUMO
The oil spill in the Gulf of Mexico that followed the explosion of the exploration platform Deepwater Horizon on 20 April 2010 was the largest accidental oil spill so far. In this paper we evaluate the risk of such very severe oil spills based on global historical data from our Energy-Related Severe Accident Database (ENSAD) and investigate if an accident of this size could have been "expected". We also compare the risk of oil spills from such accidents in exploration and production to accidental spills from other activities in the oil chain (tanker ship transport, pipelines, storage/refinery) and analyze the two components of risk, frequency and severity (quantity of oil spilled) separately. This detailed analysis reveals the differences in the structure of the risk between different spill sources, differences in trends over time and it allows in particular assessing the risk of very severe events such as the Deepwater Horizon spill. Such top down risk assessment can serve as an important input to decision making by complementing bottom up engineering risk assessment and can be combined with impact assessment in environmental risk analysis.
Assuntos
Acidentes , Poluição por Petróleo , Risco , Bases de Dados Factuais , MéxicoRESUMO
This study gives a global overview of accidental oil spills from all sources (> or =700t) for the period 1970-2004, followed by a detailed examination of trends in accidental tanker spills. The present analysis of the number and volume of tanker spills includes temporal and spatial spill trends, aspects of spill size distribution as well as trends of key factors (i.e., flag state, hull type, tanker age, accident cause and sensitivity of location). Results show that the total number and volume of tanker spills have significantly decreased since the 1970s, which is in contrast to increases in maritime transport of oil and to popular perceptions following recent catastrophic events. However, many spills still occur in ecologically sensitive locations because the major maritime transport routes often cross the boundaries of the Large Marine Ecosystems, but the substantially lower total spill volume is an important contribution to potentially reduce overall ecosystem impacts. In summary, the improvements achieved in the past decades have been the result of a set of initiatives and regulations implemented by governments, international organizations and the shipping industry.