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

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. 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 ...

Offshore Exploratory Drilling Campaigns During Oil Price Downturns: Maximizing Value Creation from Flexibility

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“…This, in turn, will exert pressure on the rig and service providers to reduce their rates, and this correlation is clearly observed in the market. Willigers (2009) studied the relationship between rig rates and oil price from 1995 to 2008. He determined correlation factors analyzing two types of rigs (jack-up and semi-submersible) in the Gulf of Mexico and the North Sea.…”

“…Based on historical market observations, drilling cost responds to changing oil prices with a one-year time lag (Willigers, 2009). To account for this, we correlate the short-term price factor in year t and the cost in year t + 1.…”

“…, 2004Brandao 2005aBrandao , 2005bLima, at al. 2005;Smith, 2005;Willigers, 2009;Willigers and Bratvold, 2009;Hem, at al. 2011;Willigers, et al 2011;Alkhatib and King, 2011;Jafarizadeh and Bratvold 2009, 2012Bratvold 2015, 2017).…”

Evaluation of tieback developments for marginal oil fields with timing flexibility

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