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A Model of Risk Analysis for Iran Petroleum Contracts (IPC)

Zohali, Daria | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 56812 (51)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Haji, Alireza; Ayatollahi, Shahab
  7. Abstract:
  8. The economic framework of the petroleum sector exhibits significant divergence from other businesses owing to the substantial risks and uncertainties inherent in oil and gas ventures, compounded by highly unpredictable price fluctuations. Moreover, the abundance of uncertainties in the data employed for investment decisions in petroleum projects is very large, thus exerting a significant impact on the decision-making processes. This research delves into the impact of diverse risk factors on oil contracts and scrutinizes their influence on contractual dynamics. The study unfolds in three segments, encompassing an examination of economic factors shaping oil contracts, model design, and the model's application to research queries. Through extensive inquiry, we pinpointed five pivotal risk factors: oil price, production levels, operational costs, capital costs, and field abandonment expenses. Employing six essential parameters (NCF, DCF, NPV, IRR, Payback Period, EMV) for project performance evaluation, we conducted a thorough analysis. Following the construction of the initial model and data input from a case study of IPC contracts, we employed Monte Carlo simulation for the five risk factors. Simultaneously assessing uncertainties' impact on decision parameters and plotting NPV and EMV histograms with 1000 iterations yielded reliable results at a 90% confidence level. A distinctive feature of this research is the utilization of the EMV parameter, considering project success and failure probabilities. The primary differentiator lies in scenario analysis, incorporating eight scenarios to evaluate decision parameters numerically and graphically, enabling decision-makers to align logical responses with Monte Carlo model outputs for more favorable decisions. Upon scrutinizing the model's outputs across the eight scenarios, Scenario 3, characterized by a 40% oil price increase, a 20% cost reduction, and a 20% production boost, emerged as the optimal scenario. This scenario minimizes the payback period, maximizes contract parties' profit, increases NPV by 5600 units, and elevates EMV by 3800 units, all falling within the range calculated by the Monte Carlo model. Conversely, Scenario 7, featuring a 40% oil price decrease, a 40% field cost increase, and a 40% production decrease, resulted in an extended payback period, a significant drop in contract parties' profit, a 5700-unit NPV reduction, and a negative EMV, indicating an undesirable outcome. The developed model facilitates informed decision-making, providing precise predictions and increased efficiency in analyzing and reviewing concluded contracts. Transparent analysis leading to the clear identification of project detriment factors enables specialists to effectively address and resolve these issues
  9. Keywords:
  10. Risk Analysis ; Risk Identification ; Iran Petroleum Contracts ; Scenario Analysis ; Monte Carlo Simulation ; Oil and Gas Contract ; Uncertainty ; Oil Price

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