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.
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.
Offshore wind deployment has greatly increased in the last decade. The number and size of installations have historically determined the requirement for jack up vessels, as installation was the only driver for heavy lift vessel requirement. The next phase of offshore wind development will change this situation, as jack up requirement for operations and maintenance will certainly increase as assets age, and in parallel larger sites continue to be installed and commissioned. The approach taken in this paper is to develop a long-term vessel demand model. This is achieved by a two stage process: firstly an installation jack up demand model is developed using UK offshore wind data for projects installed in the period 2003-2016. This model is utilized in tandem with an operations and maintenance jack up requirement model which is based on published reliability figures and failure duration data. The combined model captures the requirement for jack up vessels in the period 2012-2030. The paper concludes that demand for jack up vessels in UK waters will ramp up significantly in this decade, with an initial peak in 2014. A secondary peak around 2028 is highly dependent on assumptions regarding the trajectory of the turbine failure rate over time.
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