Development and Application of a Predictive Model for Estimating Refinery Shutdown Duration and Resilience Impacts Due to Hurricane Hazards

Abstract

U.S. 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 are used to supply local markets. Due to the highly centralized nature of the U.S. 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.