Natural Resources Research

, Volume 21, Issue 4, pp 443–459 | Cite as

Sequential Simulation Approach to Modeling of Multi-seam Coal Deposits with an Application to the Assessment of a Louisiana Lignite

Article

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.

Keywords

Kriging sequential simulation probability distribution coal bed assessment 

References

  1. Caers, J. (2011). Modeling uncertainty in the earth sciences. Chichester: Wiley-Blackwell.CrossRefGoogle Scholar
  2. de Souza, L. E., Costa, J. L. C. L., & Koppe, J. C. (2004). Uncertainty estimate in resource assessment: A geostatistical contribution. Natural Resources Research, 13(1), 1–15.CrossRefGoogle Scholar
  3. Deutsch, C. V. (2002). Geostatistical reservoir modeling. New York: Oxford University Press.Google Scholar
  4. Deutsch, C. V. (2006). A sequential indicator simulation program for categorical variables with point and block data: BlockSIS. Computers & Geosciences, 32(10), 1669–1681.CrossRefGoogle Scholar
  5. 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.
  6. Heriawan, M. N., & Koike, K. (2008). Uncertainty assessment of coal tonnage by spatial modeling of seam distribution and coal quality. International Journal of Coal Geology, 76(3), 217–226.CrossRefGoogle Scholar
  7. Hohn, M. E., & McDowell, R. R. (2001). Uncertainty in coal property valuation in West Virginia: A case study. Mathematical Geology, 33(2), 191–216.CrossRefGoogle Scholar
  8. Isaaks, E. H., & Srivastava, R. M. (1989). Applied geostatistics. New York: Oxford University Press.Google Scholar
  9. Jones, T. A., Hamilton, D. E., & Johnson, C. R. (1986). Contouring geologic surfaces with the computer. New York: Van Nostrand Reinhold.Google Scholar
  10. Journel, A. G., & Kyriakidis, P. C. (2004). Evaluation of mineral reserves—a simulation approach. New York: Oxford University Press.Google Scholar
  11. Koike, K., & Matsuda, S. (2005). Spatial modeling of discontinuous geologic attributes with geotechnical applications. Engineering Geology, 78(1–2), 143–161.CrossRefGoogle Scholar
  12. Manchuk, J. G., Leuangthong, O., & Deutsch, C. V. (2009). The proportional effect. Mathematical Geosciences, 41(7), 799–816.CrossRefGoogle Scholar
  13. 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
  14. Nowak, M., & Verly, G. (2005). The practice of sequential Gaussian simulation. In O. Leuangthong & C. V. Deutsch (Eds.), Geostatistics Banff 2004 (pp. 387–398). Berlin: Springer.CrossRefGoogle Scholar
  15. 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.
  16. Olea, R. A. (2012). Building on crossvalidation for increasing the quality of geostatistical modeling. Stochastic Environmental Research and Risk Assessment, 26(1), 73–82.CrossRefGoogle Scholar
  17. Olea, R. A., Luppens, J. A., & Tewalt, S. J. (2011). Methodology for quantifying uncertainty in coal assessments with an application to a Texas lignite deposit. International Journal of Coal Geology, 85(1), 78–90.CrossRefGoogle Scholar
  18. 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
  19. 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
  20. Schuenemeyer, J. H., & Gautier, D. L. (2010). Aggregation methodology for the Circum-Artic resource appraisal. Mathematical Geosciences, 42(5), 583–594.CrossRefGoogle Scholar
  21. 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
  22. 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
  23. 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
  24. Zanon, S., & Leuangthong, O. (2005). Implementation aspects of sequential simulation. In O. Leuangthong & C. V. Deutsch (Eds.), Geostatistics Banff 2004 (pp. 543–548). Berlin: Springer.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geology (outside the USA) 2012

Authors and Affiliations

  1. 1.U.S. Geological SurveyRestonUSA

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