Exploration and production (E&P) involves the upstream activities from looking for promising reservoirs to extracting oil and selling it to downstream companies. E&P is the most profitable business in the oil industry. However, it is also the most capital-intensive and risky. Hence, the proper assessment of E&P projects with effective management of uncertainties is crucial to the success of any upstream business. This dissertation is concentrated on developing portfolio optimization models to manage E&P projects. The idea is not new, but it has been mostly restricted to the conceptual level due to the inherent complications to capture interactions among projects. We disentangle the complications by modeling the project portfolio optimization problem as multistage stochastic programs with mixed integer programming (MIP) techniques. Due to the disparate nature of uncertainties, we separately consider explored and unexplored oil fields. We model portfolios of real options and portfolios of decision trees for the two cases, respectively. The resulting project portfolio models provide rigorous and consistent treatments to optimally balance the total rewards and the overall risk. For explored oil fields, oil price fluctuations dominate the geologic risk. The field development process hence can be modeled and assessed as sequentially compounded options with our optimization based option pricing models. We can further model the portfolio of real options to solve the dynamic capital budgeting problem for oil projects. For unexplored oil fields, the geologic risk plays the dominating role to determine how a field is optimally explored and developed. We can model the E&P process as a decision tree in the form of an optimization model with MIP techniques. By applying the inventory-style budget constraints, we can pool multiple project-specific decision trees to get the multistage E&P project portfolio optimization (MEPPO) model. The resulting large scale MILP is efficiently solved by a decomposition-based primal heuristic algorithm. The MEPPO model requires a scenario tree to approximate the stochastic process of the geologic parameters. We apply statistical learning, Monte Carlo simulation, and scenario reduction methods to generate the scenario tree, in which prior beliefs can be progressively refined with new information. / text

The work deals with the choice and implementation of an open source tool for managing a project portfolio management in the organization of o.s. Prostor. The first part of the work aims at theory description including examination of trends in project management tools and open source software. Project management and project portfolio management is increasingly important and increasingly used by companies and organizations in recent years. According to a new study made by Gartner, the use of open source software continues to grow and becomes competitive differently. The main aim of the work was to successfully implement one selected open source tool to the organization that I have found at an early stage. After finding the suitable company, I started to do the main part of my work i.e. the implementation of a selected open source tool for managing projects and its portfolio in this company. This phase started from the choice of the most suitable instrument, by means of analysis of the selected organization, o.s. Prostor, to the final design and implementation itself. I made analysis of the organization and on its basis I chose suitable criteria and their weights for the later selection of a suitable tool. I performed the choice based on multi-criteria analysis in order to maximum objectively select a suitable open source tool for implementation. None of the existing instruments fully met the requirements of the organization o.s. Prostor. Therefore, I assessed finally selected web2project tool in detail and made a proposal for additional programming of missing functionality. After completion of the missing functionality additional programming, I made a proposal of a web2project tool implementation procedure. I suggested four stages of the implementation procedure, which consists of the preparation for the installation environment, the way how to install the tool on the server, planning necessary capacity, and finally training including creation of a demonstration project. Finally, based on this proposal, I installed the tool web2project on the server and run testing and validation of the tool. When performing this work, I gained a lot of valuable experience in all relevant phases of the project cycle management from management to the implementation itself. The organization intends to fully operate the tool early next year, 2012, when they can begin to enter new projects.

The proposition of this dissertation is that superior capital budgeting solutions can be attained by not only analyzing projects individually but rather as part of a portfolio of projects that has the objective of maximizing the company’s range of multiple objectives, not only the economic benefit. The dissertation starts with a detailed study of current techniques and an assessment of flaws and shortcomings. This study concludes with the requirements that any new approach or model must address in order to improve on the current practices. Based on these requirements, a new model is developed based on the portfolio approach that integrates all the assumptions, constraints, project and variable interrelationships. An important feature of the model is that it selects its portfolio of capital projects in such a way that it optimizes support for the company’s multiple objectives, not only the economic objective. The dissertation concludes with the application of this model to a hypothetical case. It is concluded that, by developing and using this model, a company can improve the analysis required before capital budgets are finalized. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2006. / Industrial and Systems Engineering / unrestricted