Application: Monte Carlo Simulation in Risk Evaluation of E&amp;P Projects

Komlosi, Zsolt Peter

doi:10.2118/68578-ms

Cited by 5 publications

(2 citation statements)

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“…Studies advocating the use of probabilistic modeling have been instrumental in the acceptance of these techniques in the upstream oil and gas industry (e.g., Rose, 2001;Corre et al, 2000;Komlosi, 2001;Begg and Bratvold, 2002;Garcia et al, 2003). However, few studies have (1) explored the effect of dependencies between key value drivers on economic evaluations of E&P assets (Peterson et al, 2005) or (2) discussed the limitations of the application of fixed price decks.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Economic Modeling by Correlated Stochastic Models of E&P Costs and Hydrocarbon Prices: The Limitations of Fixed Price Decks and the Versatility of Least-Squares Monte Carlo Simulation

Willigers¹

2009

EUROPEC/EAGE Conference and Exhibition

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As decision-making processes in the E&P industry increasingly rely on probabilistic economic models, determining the accuracy of its methodologies becomes more problematic. Enhancements can be achieved by (1) better understanding the interdependencies between different sources of uncertainty and (2) the abandonment of fixed time series of either hydrocarbon prices or capital expenditures.Historical market data of hydrocarbon prices, steel prices, and daily rig rental rates can be used to establish the correlation between different sources of market risk. Uncertainties can be defined as "fixed" or "dynamic." Fixed uncertainties relate to factors that do not change over time, such as many geological parameters during the early stages of exploration. Most uncertainties that relate to market risk are dynamic, that is, they keep developing over time. For example, not only is the realized price of oil uncertain until the moment the oil has been sold, but the expectation of future oil prices changes. The recognition of this Bayesian property of hydrocarbon prices significantly affects projects with multiple decision points. The forecasted hydrocarbon price at a future decision point is a function of the simulated realized price at that given decision point. Traditional decision tree models apply the same series of static price decks at each decision point and therefore do not accurately reflect the impact of the evolving market outlook during the development of a project.The stochastic model developed in this study accounts for (1) the correlation between different uncertainties and (2) Bayesian price-cost forecasts. The versatility of the Least-Squares Monte Carlo simulation technique is demonstrated by a real option valuation of an asset subjected to a complex tax regime and two future stage-gate decision points. IntroductionOver the past decade, the upstream oil and gas industry has slowly adapted the application of probabilistic or stochastic modeling techniques in place of traditional deterministic economic analysis (Bickel and Bratvold, 2007). Probabilistic modeling dictates that in contrast to single-point estimates for key model variables, a probability-weighted spectrum of potential outcomes is analyzed in an economic cash flow model. The fundamental notion of probabilistic modeling is that given a range of feasible outcomes, some are more likely to occur than others. Probabilistic modeling allows for a much improved understanding of the potential range of outcomes and how certain developments impact the economic performance of a project (Schuyler, 1998;Rose, 2001;Willigers, 2008a).A common approach in risk modeling is to identify the key uncertainties, decisions, and value drivers of an asset and to model several discrete probability-weighted outcomes in a probability or decision tree. This process can also approximate continuous probability distributions. The spectrum of feasible price scenarios is typically approximated using a combination of probability-weighted price decks. Though this approach provides a ...

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Section: Introductionmentioning

confidence: 99%

Enhanced Economic Modeling by Correlated Stochastic Models of E&P Costs and Hydrocarbon Prices: The Limitations of Fixed Price Decks and the Versatility of Least-Squares Monte Carlo Simulation

Willigers¹

2009

EUROPEC/EAGE Conference and Exhibition

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“…provide simple models to construct averse measures of risk, of which only risk measures of CVaR type and of semi-L β ðΩÞ type with λ 2ð0; 1 are coherent-averse measures of risk: In light of these ideas, Capolei et al (2015b) assessed the validity of different measures in oil production optimization under the concept of coherent-averse measures of risk. Komlosi (2001), Marques et al (2014), andCapolei et al (2015b) applied the financial concepts Value at Risk (VaR) and Conditional Value at Risk (CVaR) in upstream petroleum projects.…”

Section: Measures Of Risk In Upstream Petroleum Investments: An Overviewmentioning

confidence: 99%

Decision analysis applied to the development of petroleum fields considering robustness, information, and flexibility

Santos¹

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Selecting a production strategy for oil field development is complex because multiple uncertainties affect decisions. Project value is maximized when uncertainty is managed by:(1) acquiring information to reduce reservoir uncertainty; (2) defining a flexible production strategy, allowing system modifications as uncertainty unfolds over time; or (3) defining a robust production strategy, ensuring good performance without system modifications after production has started. However, decision-making is many times subjective and based on intuition or professional experience because of the lack of objective criteria in the literature.In this study, we aimed to provide easy-to-apply decision criteria, while maintaining the complexity of the problem, reducing the subjectivity of the: (1) construction and assessment of the risk curve; (2) selection of the production strategy; and (3) selection of actions to manage uncertainty. For the construction of the risk curve, we compared two techniques: the well-established Monte Carlo with joint proxy models, with the recently proposed discretized Latin Hypercube with geostatistics, presenting their strengths and limitations. For the selection of the production strategy, we proposed a new function that combines the wellknown expected value with lower and upper semi-deviations from a benchmark return, quantifying downside risk and upside potential of production strategies. We applied this function to select production strategies and to estimate the expected values of information, flexibility, and robustness. We selected actions to manage uncertainty using predefined candidate production strategies, optimized for representative models of the uncertain system. We proposed probabilistic-based decision structures to assess the potential for information, flexibility, and robustness, incorporating (1) characteristics of the field and the type of uncertainties; (2) available resources and costs; and (3) decision maker's attitude and objectives. Finally, we proposed an integrated approach looking at project sensitivity to uncertainty and at the effects of uncertainties on production strategy selection. Thus, we identify the best course of action to manage uncertainty, either reducing it with information or protecting the system with robustness and flexibility.

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Quantitative risk management in gas injection project: a case study from Oman oil and gas industry

2017

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The purpose of this research was to study the recognition, application and quantification of the risks associated in managing projects. In this research, the management of risks in an oil and gas project is studied and implemented within a case company in Oman. In this study, at first, the qualitative data related to risks in the project were identified through field visits and extensive interviews. These data were then translated into numerical values based on the expert's opinion. Further, the numerical data were used as an input to Monte Carlo simulation. RiskyProject Professional TM software was used to simulate the system based on the identified risks. The simulation result predicted a delay of about 2 years as a worse case with no chance of meeting the project's on stream date. Also, it has predicted 8% chance of exceeding the total estimated budget. The result of numerical analysis from the proposed model is validated by comparing it with the result of qualitative analysis, which was obtained through discussion with various project managers of company.

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Application: Monte Carlo Simulation in Risk Evaluation of E&P Projects

Cited by 5 publications

References 1 publication

Enhanced Economic Modeling by Correlated Stochastic Models of E&P Costs and Hydrocarbon Prices: The Limitations of Fixed Price Decks and the Versatility of Least-Squares Monte Carlo Simulation

Enhanced Economic Modeling by Correlated Stochastic Models of E&P Costs and Hydrocarbon Prices: The Limitations of Fixed Price Decks and the Versatility of Least-Squares Monte Carlo Simulation

Decision analysis applied to the development of petroleum fields considering robustness, information, and flexibility

Quantitative risk management in gas injection project: a case study from Oman oil and gas industry

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