Considering the important role played today by unconventional gas resources in North America and their enormous potential for the future around the world, it is vital to both policy makers and industry that the volumes of these resources and the impact of technology on these resources be assessed. To provide for optimal decision making regarding energy policy, research funding, and resource development, it is necessary to reliably quantify the uncertainty in these resource assessments.

Since the 1970's, studies to assess potential unconventional gas resources have been conducted by various private and governmental agencies, the most rigorous by the United States Geological Survey (USGS). The USGS employed a cell-based, probabilistic methodology, which incorporated simplifying assumptions to derive analytical equations to calculate distributions of the resources assessed. USGS assessments have generally produced distributions for potential unconventional gas resources that are unrealistically narrow for what are essentially undiscovered, untested resources.

In this paper we present an improved methodology to assess potential unconventional gas resources. Our methodology is a stochastic approach that includes Monte Carlo simulation and correlation between input variables. Application of the improved methodology to assessment of potential unconventional gas resources in the Uinta-Piceance province of Utah and Colorado with the USGS data validates the means and standard deviations of distributions produced by the USGS methodology, but reveals that these distributions are not right skewed, as expected for a natural resource. Our investigation indicates that the unrealistic shape and width of the gas resource distributions are not caused by lack of correlation between input variables, but rather the use of narrow triangular input parameter distributions.

The stochastic methodology proposed here is more versatile and robust than the USGS analytic methodology. Adoption of the methodology, along with a careful examination and revision of input distributions, will allow a more realistic assessment of the uncertainty surrounding potential unconventional gas resources. Reliable assessments of uncertainty are important in the decision-making process for exploration and development of potential unconventional gas resources, and will be valuable to both government and industry in the timely development of unconventional gas resources for the public benefit.

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