Hurricanes threaten the petroleum industry in the United States and are expected to be influenced by climate change. This study presents an integrated framework for hurricane risk assessment of petroleum infrastructure under changing climatic conditions, calculating risk in terms of monetary loss. Variants of two synthetic probabilistic storms and one historical storm (Hurricane Ike) are simulated using the SWAN+ADCIRC model, representing a range of potential scenarios of impacts of a changing climate on hurricane forward speed and sea-level rise given uncertainties in climate projections. Model outputs inform an infrastructure impact and cascading economic loss analysis that incorporates various sources of uncertainty to estimate five types of losses sustained by petroleum facilities in surge events: land value loss, process-unit damage loss, cost of spill clean-up and repair of aboveground storage tanks, productivity loss, and civil fines. The proposed risk assessment framework is applied as a case study to seven refineries along the Houston Ship Channel (HSC), a densely-industrialized corridor in Texas. The results reveal that either an increase in mean sea level or a decrease in storm forward speed increases the maximum water elevations in the HSC for storms that produce maximum wind setup in Galveston Bay (FEMA 33 and FEMA 36), resulting in larger economic loss estimates. The role of refinery features such as storage capacity and average elevation of the refinery and its critical equipment in the refinery response to hurricane hazards is studied, and the probability distribution of refinery total loss and the loss risk profile in different hurricane scenarios are discussed. Loss estimates are presented, demonstrating the effects of hurricane forward speed and sea level on the losses for the refineries as well as the HSC. Such a framework can enable hurricane risk assessment and loss estimation for petroleum infrastructure to inform future policies and risk mitigation strategies. Potential policy implications for a region like the HSC are highlighted herein as an illustration.

US Gulf Coast refineries account for over half of the total refining capacity of the nation. However, less than a third of products refined in this region is used to supply local markets. Due to the highly centralized nature of the US petroleum distribution network, disruptions affecting Gulf Coast refineries can have widespread impacts. The objective of this study is to develop a sufficient predictive model for the likelihood and expected duration of refinery shutdowns under hurricane hazards. Such models are currently lacking in the literature yet essential for risk modeling of the cascading consequences of refinery shutdown ranging from resilience analyses of petroleum networks to potential health effects on surrounding communities tied to startup and shutdown activities. A database of empirical refinery downtime and storm hazards data is developed, and statistical analyses are conducted to explore the relationship between refinery and storm characteristics and shutdown duration. The proposed method with the highest predictive accuracy is found to be a model comprised of a logistic regression binary classification component related to refinery shutdown potential and a Poisson distribution generalized linear model component related to downtime duration determination. To illustrate the utility of the newly developed model, a case study is conducted exploring the impact of two storms affecting the Houston Ship Channel and surrounding region. Both the regional refining resilience as well as the distribution network resilience are quantified, including uncertainty propagation. Such analyses reveal local community to nationwide impacts of refining disruptions and can support resilience enhancement decisions.

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