Date of Award
Doctor of Philosophy in Civil Engineering - (Ph.D.)
Civil and Environmental Engineering
Fadi A. Karaa
John R. Schuring
The oil and gas sector faces a broad array of risks and uncertainties, affecting short- and long-term planning, and causing adverse effects in the energy sector. To predict and minimize them, risk based supply chain models were developed for the oil and gas supply chain critical infrastructure.
First, the events and activities were categorized as short term and long term, and their risk ratings derived, by analyzing data from past events and site visits. The result showed that events like the Israel – Arab war, successful hurricane, and production increase or decrease, had a risk rating of approximately 7.
Second, network reliability and connectivity were analyzed using the risk based minimum cut-set model, and its associated algorithms and simulation, for tactical and short term planning. The impact of link failures, due to the risk and risk ratings associated with certain events and activities determined above, which can affect each sourcedemand pair (i.e., from the crude storage tank to the refinery or from the refinery to the product storage tank) of the network or the whole network, were determined. The result showed that for a real world petroleum supply chain network like the Petrobas (Brazil), this model could identify critical nodes/links in the supply chain network(s) that can be severely affected by failure.
Third, a risk based LP Supply Chain Model (SCM) was developed, and used to analyze the supply chain (SC), for strategic and long term scenarios. The average expected risk ratings obtained above was used as one of the constraints in simulating different risk scenarios. It was also used to forecast their likely impact on the supply chain, and to come up with alternative ways to manage/minimize risk. The study showed that for a generalized oil and gas supply chain like the Gulf coast area of the US, a very critical (in terms of risk rating), and very severe (in terms of duration) event at the crude source - like crisis in Nigeria or Iraq, occurring during the fall season could likely cause an approximately 35% drop/loss in production of the supply chain. The study also showed that other events like a refinery explosion/fire, tank leak/crack, or pipeline fire/attack that is also very critical and severe, occurring during the fall season, could also lead to an approximately 40% loss/drop in production of the supply chain.
Last, Fault Tree Analysis (FTA) and Model Based Vulnerability Analysis (MBVA) were carried out on the supply chain, to determine whether each source-demand pair analyzed, failed or not, due to the likely impact of any event/threat scenario analyzed above. The analysis were also carried out to show how scarce resources can be allocated for optimum results in protecting these oil and gas supply chain nodes/links from failure. Using the supply chain (SC) of the Gulf coast area as a case study, the result of the simulation showed that investing at least $200 million to provide Critical Infrastructure Protection (CIP) in the Gulf coast area, can lower vulnerability to as little as 11%, and prevent the potential for huge price increase on the consumers in particular, and the economy in general.
Achebe, Kingsley Oseloka, "Risk based models for the optimization of oil and gas supply chain critical infrastructure" (2010). Dissertations. 233.