Sequential Simulation Approach to Modeling of Multi-seam Coal Deposits with an Application to the Assessment of a Louisiana Lignite
- 269 Downloads
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.
KeywordsKriging sequential simulation probability distribution coal bed assessment
- Deutsch, C. V. (2002). Geostatistical reservoir modeling. New York: Oxford University Press.Google Scholar
- Hengl, T. (2007). A practical guide to geostatistical mapping of environmental variables. Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy, 143 p. Accessed May 2012, from http://eusoils.jrc.ec.europa.eu/esdb_archive/eusoils_docs/other/EUR22904en.pdf.
- Isaaks, E. H., & Srivastava, R. M. (1989). Applied geostatistics. New York: Oxford University Press.Google Scholar
- Jones, T. A., Hamilton, D. E., & Johnson, C. R. (1986). Contouring geologic surfaces with the computer. New York: Van Nostrand Reinhold.Google Scholar
- Journel, A. G., & Kyriakidis, P. C. (2004). Evaluation of mineral reserves—a simulation approach. New York: Oxford University Press.Google Scholar
- Mwasinga, P. P. (2001). Approaching resource classification: general practices and the integration of geostatistics. In H. Xie, Y. Wang, & Y. Jiang (Eds.), Computer applications in the mineral industries (pp. 97–104). Rotterdam: A.A. Balkema Publishers.Google Scholar
- Olea, R. A. (2009). A practical primer on geostatistics. U.S. Geological Survey Open-File Report 2009-1103, 345 p. Accessed May 2012, from http://pubs.er.usgs.gov/usgspubs/ofr/ofr20091103.
- Remy, N., Boucher, A., & Wu, J. (2009). Applied geostatistics with SGeMS: A User’s Guide. New York: Cambridge University Press, 284 p, one CD-ROM.Google Scholar
- Schuenemeyer, J. H. (2005). Methodology for the 2005 USGS assessment of undiscovered oil and gas resources, Central North Slope, Alaska. U.S. Geological Survey Open File Report 2005-1410, 82 p.Google Scholar
- Srivastava, R. M. (1994). An overview of stochastic methods for reservoir characterization. In J. M. Yarus & R. L. Chambers (Eds.), Stochastic modeling and geostatistics (pp. 3–16). Tulsa: American Association of Petroleum Geologists.Google Scholar
- Tewalt, S. J., Bauer, M. A, Mathew, D., Roberts, M. P., Ayers, W. B., Jr., Barnes, J. W., & Kaiser, W. R. (1983). Estimation of uncertainty in coal resources: Bureau of Economic Geology, Report of Investigation no. 136, 137 p.Google Scholar
- Wood, G. H., Jr., Kehn, T. M., Carter, M.D., and Culbertson, W.C., 1983. Coal resources classification system of the U.S. Geological Survey. U.S. Geological Survey Circular, 891. 65 p.Google Scholar