This paper represents a collective effort by the Supply Analysis Group of the M.I.T. World Oil Project and was partly supported by the U.S. National Science Foundation under Grant no. SIA75-00739. / U.S. National Science Foundation under Grant no. SIA75-00739.

M.Sc.

When making economic analyses about a certain industry, it is essential to make some basic assumptions. One of these assumptions is either certainty or uncertainty. With certainty, predictions about economic behaviour can be made less cautiously. The petroleum industry is alleged to be influenced by elements of uncertainty, hence we expect that most of the decisions made in the industry are governed by expected uncertain outcomes. The task of this study, therefore, was to find out what the uncertainties might be in the petroleum industry of the developing countries. First, we discovered that governments of the developing countries may expect far too much from their oil resources. This expectation may be derived from various sources. One of them is the foreign exchange necessary to buy arms to defend their territories (like in the Middle East). Another source may be the pressing needs to accelerate economic growth. Finally, some of the countries may simply wish to be nationalistic. Regardless of the source, these expectations lead to some uncertainties in the industry. Secondly, some uncertainties are created by the oil producing companies themselves. By accumulating very large capital relative to companies in other industries, companies make themselves vulnerable to political policies. And one reason why this accumulation of large capital is possible is that their home governments have provided the economic incentives for them to expand. Thirdly, there is some degree of uncertainty specific to the petroleum industry. In the exploration stage, any addition to capacities is random. This randomness imposes uncertainties about the supply conditions in the industry. Also, there are a series of uncertainties about whether oil can be found in certain place or not, qualities and quantities that are of commercial values, the changes in the reservoir conditions during productions and the usual uncertainties which face any industries, i.e. price or demand conditions, and cost or supply conditions. Lastly, the governments and oil companies jointly agree to maximize the total profits of oil by seeking a common price policy but there is a reasonable degree of uncertainty about how to share this profit. We fall short of finding an optimum device for sharing the rent. However, we can conclude that the optimum rent sharing policy depends largely on the time horizon of the government of the developing country, and the type of economic growth it prefers. / Arts, Faculty of / Vancouver School of Economics / Graduate

Water demand has increased exponentially since 2007 in western North Dakota. This increase can largely be traced to the advancement of technology in hydraulic fracturing (fracking) which has led to one of the largest oil booms in the country. Along with the recent oil boom, water depots have expanded and played a significant role in providing water for fracking. Using decentralized agent-based modeling (ABM) to model water allocation among water depots, a scenario analysis obtains results for four scenarios. Policy suggestions, based on the scenario analysis, include allowing greater access to LSMR water sources and restricting SW and GW use for the oil industry to reduce water scarcity in the Bakken. These results support allowing greater access to LSMR water sources for the oil industry as desired by the North Dakota State Water Commission (SWC), and other elected officials in the past decade.

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Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1975 / Bibliography: leaves 145-156. / by Richard E. Brackeen. / M.S. / M.S. Massachusetts Institute of Technology, Sloan School of Management

Thesis (Ph. D.)--Harvard University, 1998. / Includes bibliographical references (leaves 361-367).

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 327-362).

Thesis (Ph. D.)--University of Southampton, 1987. / Includes bibliographical references (leaves 237-251).

The petroleum industry has one of the most complex supply chains in the world. A unique characteristic of Petroleum Supply Chain (PSC) is the high degree of uncertainty which propagates through the network. Therefore, it is necessary to develop quantitative models aiming at optimizing the network and managing logistics operations. This work proposes a deterministic Mixed Integer Linear Program (MILP) model for downstream PSC to determine the optimal distribution center (DC) locations, capacities, transportation modes, and transfer volumes. Three products are considered in this study: gasoline, diesel, and jet fuel. The model minimizes multi-echelon multi-product cost along the refineries, distribution centers, transportation modes and demand nodes. The relationship between strategic planning and multimodal transportation is further elucidated. Furthermore, this work proposes a two stage Stochastic Mixed Integer Linear Program (SMILP) models with recourse for PSC under the risk of random disruptions, and a two stage Stochastic Linear Program (SLP) model with recourse under the risk of anticipated disruptions, namely hurricanes. Two separate types of mitigation strategies – proactive and reactive – are proposed in each model based on the type of disruption. The SMILP model determines optimal DC locations and capacities in the first stage and utilizes multimode transportation as the reactive mitigation strategy in the second stage to allocate transfer volumes. The SLP model uses proactive mitigation strategies in the first stage and employs multimode transportation as the reactive mitigation strategy. The goal of both stochastic models is to minimize the expected total supply chain costs under uncertainty. The proposed models are tested with real data from two sections of the U.S. petroleum industry, PADD 3 and PADD 1, and transportation networks within Geographic Information System (GIS). It involves supply at the existing refineries, proposed DCs and demand nodes. GIS is used to analyze spatial data and to map refineries, DCs and demand nodes to visualize the process. Sensitivity analysis is conducted to asses supply chain performance in response to changes in key parameters of proposed models to provide insights on PSC decisions, and to demonstrate the impact of key parameters on PSC decisions and total cost. / Upper Great Plains Transportation Institute (UGPTI) / Mountain Plains Consortium (MPC)