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The AAPG/Datapages Combined Publications Database

GCAGS Transactions


Gulf Coast Association of Geological Societies Transactions
Vol. 31 (1981), Pages 149-154

Estimation of Uncertainty in Coal Resources (1)

D. Mathew, M. P. Roberts (2)


The use of alternative energy sources to supplement dwindling domestic petroleum resources will increase in the future. Among alternative resources, coal will contribute greatly to the future energy mix. Official estimates of the United States coal resources published during the past 15 years vary from less than 1.7 to as much as 3.9 trillion short tons (1.5 to 3.5 trillion metric tons). These differences imply that a high degree of uncertainty exists in resource assessment. Sources of uncertainty in coal resource estimation are identified. A comparison of variability in coal resource estimates in areas of different ancient depositional environments is examined in an attempt to utilize depositional systems for improved coal resource estimates.

The Texas Gulf Coast basin was chosen to develop the methodology of resource evaluation because it exhibits a full range of ancient depositional environments. Two lignite deposits, one from the alluvial-plain setting of the Wilcox Group in east Texas and the other from the delta-plain setting of the Jackson Group in east Texas, are evaluated.

Important sources of uncertainty in resource estimation include variability of seam thickness and areal distribution. To test the degree of uncertainty caused by variations in seam thickness, the numbers of boreholes considered in each lignite deposit are reduced and resources are calculated for each reduction in data.

Classical statistical methods are used to determine the number of boreholes required to obtain resource estimates of individual seams within a given confidence interval and specified conditions. Geostatistics (variograms and kriging) are used to measure variability in resource estimates. These studies show that the minimum number of boreholes required to characterize coal resources within an accuracy of 20 percent, for example, is substantially less than might be expected intuitively, and that the degree of assuredness depends on the coefficient of variation of the data analyzed.

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