Sequential simulation approach to modeling of multi-seam coal deposits with an application to the assessment of a Louisiana lignite
Natural Resources Research
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Abstract
There are multiple ways to characterize uncertainty in the assessment of coal resources, but not all of them are equally satisfactory. Increasingly, the tendency is toward borrowing from the statistical tools developed in the last 50 years for the quantitative assessment of other mineral commodities. Here, we briefly review the most recent of such methods and formulate a procedure for the systematic assessment of multi-seam coal deposits taking into account several geological factors, such as fluctuations in thickness, erosion, oxidation, and bed boundaries. A lignite deposit explored in three stages is used for validating models based on comparing a first set of drill holes against data from infill and development drilling. Results were fully consistent with reality, providing a variety of maps, histograms, and scatterplots characterizing the deposit and associated uncertainty in the assessments. The geostatistical approach was particularly informative in providing a probability distribution modeling deposit wide uncertainty about total resources and a cumulative distribution of coal tonnage as a function of local uncertainty.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Sequential simulation approach to modeling of multi-seam coal deposits with an application to the assessment of a Louisiana lignite |
| Series title | Natural Resources Research |
| DOI | 10.1007/s11053-012-9185-1 |
| Volume | 21 |
| Issue | 4 |
| Year Published | 2012 |
| Language | English |
| Publisher | Springer |
| Contributing office(s) | Eastern Energy Resources Science Center |
| Description | 17 p. |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Natural Resources Research |
| First page | 443 |
| Last page | 459 |
| Country | United States |
| State | Louisiana |
| Google Analytic Metrics | Metrics page |