Plan Portfolio Optimization for Upstream Petroleum Companies in a Volatile Oil Price Environment
| Author | Hasan, Mohamed Mansoor Ahmed | |
|---|---|---|
| metadata.dc.contributor.department | University of Bahrain, College of Engineering, Department of Mechanical Engineering | |
| metadata.dc.date.accessioned | 2026-09-20T15:45:05Z | |
| Publication date | 2023 | |
| Degree level | Master | |
| Abstract | Abstract : The health of global economy is directly and indirectly impacted by the petroleum industry performance. Unfortunately, this vital natural resource is non-renewable and eventually depletes over time. As a result, massive capitals are continually invested to meet rising worldwide demand. Regrettably, this substantial investment capital is coupled with a number of technical, operational, commercial, and geopolitical uncertainties. These uncertainties: however, are commonly disregarded in economic evaluations by petroleum firms, such as the one under study, that generate their annual business plans by using traditional deterministic excel models and ranking projects manually. Therefore, while making an investment decision, an effective assessment of the spectrum of uncertainties is crucial for improved decision making. This study uses a two-stage stochastic programming with recourse approach as an alternate way for business program selection to assist oil and gas companies in making a better investment decision under uncertainty. To simplify the approach, we looked at Company A' portfolio and picked development drilling program for optimization under future oil price uncertainty. Among several investment opportunities accessible, Company A decision makers have concerns in maximizing the Expected Net Present Value (ENPV) by drilling an optimal collection of development wells from a variety of formations and trajectory options considering several constraints. Furthermore, in light of future oil price volatility, the company would like to plan the optimum drilling rigs number, which can be adapted as part of annual operation strategy. This operational decision does not have to be made at the time the plan is being prepared; rather, it can be decided later when new information on actual oil prices is available. As a result, this becomes a sequential decision problem, and modelling the information structure of such a problem becomes crucial. A representative mathematical Mixed Integer Linear Programming (MILP) model was formulated, coded in GAMS, verified, and solved for Company A's oil development drilling program problem. After investigating the stochastic model's outcomes value over the deterministic model, the stochastic model was used to propose an optimal Annual Work Program and Budget (AWPB) and 5 years plan. Although, the Expected Value of Perfect Information (EVPI) is found 1.3% and less than 1% of the calculated portfolio value, which equates to over $9.8 million and $33.6 million for AWPB and 5 years plan, respectively. These figures indicate the motivating fact that even a small improvement in portfolio value measured in percentage represents a significant money. Furthermore, the optimized plan increased the ENPV by $67 million, or 9.3%, over Company A's AWPB original plan. In addition, the model revealed the potential of new facility investment. | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/id/21f4d253-54f8-41a6-acaa-2e72d0b1ae09 | |
| metadata.dc.identifier.other | Drupal node 2534 | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/en/dar/plan-portfolio-optimization-upstream-petroleum-companies-volatile-oil-price-environment | |
| metadata.dc.identifier.uri | http://165.227.56.122/handle/123456789/6284 | |
| Language | en | |
| Link | https://digitalrepository.uob.edu.bh/en/_flysystem/fedora/2024-12/plan-portfolio-optimization-for-upstream-petroleum-companies-in-a-volatile-oil-price-environment.pdf | |
| Title | Plan Portfolio Optimization for Upstream Petroleum Companies in a Volatile Oil Price Environment | |
| Type | Master's Thesis | |
| College | College of Engineering | |
| Department | Mechanical Engineering |
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