Probabilistic Methodology for the Assessment of Original and Recoverable Coal Resources, Illustrated with an Application to a Coal Bed in the Fort Union Formation, Wyoming
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- Document: Report (34.1 MB pdf)
- Data Release: USGS data release - Computer programs for the assessment of coal resources (ver. 2.0, April 2021): U.S. Geological Survey software release
- Download citation as: RIS | Dublin Core
Executive Summary
The U.S. Geological Survey (USGS) has been using its Circular 891 for evaluating uncertainty in coal resource assessments for more than 35 years. Calculated cell tonnages are assigned to four qualitative reliability classes depending exclusively on distance to the nearest drill hole. The main appeal of this methodology, simplicity, is also its main drawback. Reliability may depend so marginally on distance to the nearest drill hole that, over time, it has become evident that Circular 891 is inadequate for modeling reliability and is limited by other shortcomings. The present publication describes the use of geostatistics as an approach allowing a more satisfactory performance than that which is achieved following Circular 891. Geostatistics takes advantage of partly random and partly organized fluctuations in attributes such as coal thickness, coal density, and elevation of the top of a coal bed, borrowing concepts and tools that have been standard features in statistics and risk analysis for decades. Considering that readers interested in this study may not have the background to go directly into the details of the methodology, we start by explaining geostatistical concepts and modeling techniques. The remainder of the publication is devoted to formulating the assessment methodology, applying it to data from the Fillmore Ranch coal bed in the Fort Union Formation in Wyoming, and explaining the computer software applied for performing calculations and displays. The assessment methodology has been designed to report three different forms of resources: coal in place, coal mineable by surface mining methods, and coal mineable by underground mining methods. These three types of resources are reported graphically by displaying both the magnitude and the reliability of total coal resources and resources at the cell scale. In the case of the Fillmore Ranch coal bed example, there is a 90-percent probability that the resources in place are 9.687 ± 0.383 billion short tons (bst), while the coal available for underground mining is 2.279 ± 0.160 bst, and that available for surface mining is only 0.240 ± 0.025 bst because of the steep dip to the west away from the outcrop. These magnitudes are derived from numerical probability distributions not following any specific form.
Suggested Citation
Olea, R.A., Shaffer, B.N., Haacke, J.E., and Luppens, J.A., 2021, Probabilistic methodology for the assessment of original and recoverable coal resources, illustrated with an application to a coal bed in the Fort Union Formation, Wyoming: U.S. Geological Survey Techniques and Methods 6-G1, 55 p., https://doi.org/10.3133/tm6G1.
ISSN: 2328-7055 (online)
Study Area
Table of Contents
- Acknowledgments
- Executive Summary
- Introduction
- Review of Basic Concepts
- Probabilistic Method for Coal Assessment
- Practical Application of the Methodology
- Workflow
- Conclusions
- References Cited
- Index
Publication type | Report |
---|---|
Publication Subtype | USGS Numbered Series |
Title | Probabilistic methodology for the assessment of original and recoverable coal resources, illustrated with an application to a coal bed in the Fort Union Formation, Wyoming |
Series title | Techniques and Methods |
Series number | 6-G1 |
DOI | 10.3133/tm6G1 |
Year Published | 2021 |
Language | English |
Publisher | U.S. Geological Survey |
Publisher location | Reston, VA |
Contributing office(s) | Central Energy Resources Science Center, Eastern Energy Resources Science Center |
Description | Report: viii, 55 p.; Data Release |
Country | United States |
State | Wyoming |
Other Geospatial | Fort Union Formation |
Online Only (Y/N) | Y |
Google Analytic Metrics | Metrics page |