Quantification of Oil Price Uncertainty in Economic Evaluation Using Sequential Gaussian Simulation

Holmes, Jay; Mendjoge, Ashish; McVay, Duane A.

doi:10.1081/lft-200041207

Cited by 4 publications

(6 citation statements)

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“…To be consistent with forecasting methodologies presented by Holmes et al 12 and Akilu et al, 13 we opted to generate exclusively oil prices (i.e., no gas price forecasts) and limit project durations to ten years. We converted projected gas rates from the industry projects to oil rates using a gas-to-oil equivalence of 6 Mcf gas to 1 bbl oil.…”

Section: Application Methodologymentioning

confidence: 99%

“…The Historical method was recently presented by Holmes et al 12 In this approximate method, multiple realizations of future prices are obtained by simply selecting different "windows" of historical price data adjusted for inflation.…”

Section: Historical Forecastsmentioning

confidence: 99%

“…Holmes et al 12 recently applied SGS to model temporal correlation in oil price data and used it to generate multiple equi-probable future price realizations for quantifying price uncertainty.…”

Section: Sequential Gaussian Simulation Forecastsmentioning

confidence: 99%

“…The initial investments and incremental lifting costs were scaled with product prices using an index modified from the correlation presented by Holmes et al 12 The resulting index is equal to a value of "1" at $29.46/bbl (the December 2002 oil price) and is calculated on the basis of price changes relative to this base price (Fig. 12).…”

Section: Economic Analysismentioning

confidence: 99%

“…In an attempt to better quantify economic uncertainty resulting from future oil price variability, stochastic forecasting methods have been presented by McMichael 4 and Holmes et al 12 Stochastic techniques are somewhat time consuming to apply, however, and as a result are not used throughout the industry. In response, Akilu et al 13 presented the Inverted Hockey Stick (IHS) method, an easy-to-apply method for better quantifying the uncertainty in future oil and gas prices.…”

Section: Introductionmentioning

confidence: 99%

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Price Uncertainty Quantification Models Advance Project Economic Evaluations

Olsen¹,

Fariyibi²,

Lee³

et al. 2005

Proceedings of SPE Hydrocarbon Economics and Evaluation Symposium

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractConventional oil and gas price forecasts typically include pessimistic, most-likely, and optimistic cases in an attempt to quantify economic uncertainty. An analysis of forecasts presented by industry and governmental organizations illustrates that conventional forecasting methods typically underestimate significantly the full range of uncertainty in oil and gas price forecasts. Economic indicators calculated using such forecasts will not reliably quantify investment risk. In this investigation we compared and contrasted several recently developed methods for quantifying upstream petroleum investment risk due to uncertainty in future prices. We analyzed five forecasting techniques -conventional, bootstrap, Inverted Hockey Stick (IHS), historical, and Sequential Gaussian Simulation (SGS). These techniques were applied to three synthetic projects and 23 industry projects to examine the uncertainty associated with economic indicators such as net present value, investment efficiency, and internal rate of return. Across all 26 projects, the conventional forecasts predicted a narrower range of economic indicator values than the four alternative methods, indicating that conventional methods routinely underestimate uncertainty.All four alternative forecasting techniques can provide operators with more reliable quantification of the uncertainty inherent in their investment decisions than provided by conventional methods currently in widespread use. The four alternative methods have unique strengths and weaknesses that may affect their applicability in particular situations. The SGS methods is the most rigorous and accurate method; however, it is also the most difficult to apply. The IHS method serves as a reasonable approximation, and can be easily incorporated into existing procedures and software.

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Section: Application Methodologymentioning

confidence: 99%

Section: Historical Forecastsmentioning

confidence: 99%

Section: Sequential Gaussian Simulation Forecastsmentioning

confidence: 99%

Section: Economic Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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