Abstract
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.
