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

First Online: 23 August 2012 Received: 15 March 2012 Accepted: 02 August 2012 DOI :
10.1007/s11053-012-9185-1

Cite this article as: Olea, R.A. & Luppens, J.A. Nat Resour Res (2012) 21: 443. doi:10.1007/s11053-012-9185-1
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 Caers, J. (2011).

Modeling uncertainty in the earth sciences . Chichester: Wiley-Blackwell.

CrossRef Google Scholar 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.

CrossRef Google Scholar Deutsch, C. V. (2002).

Geostatistical reservoir modeling . New York: Oxford University Press.

Google Scholar Deutsch, C. V. (2006). A sequential indicator simulation program for categorical variables with point and block data: BlockSIS.

Computers & Geosciences,
32 (10), 1669–1681.

CrossRef 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 .

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.

CrossRef Google Scholar Hohn, M. E., & McDowell, R. R. (2001). Uncertainty in coal property valuation in West Virginia: A case study.

Mathematical Geology,
33 (2), 191–216.

CrossRef Google Scholar 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 Koike, K., & Matsuda, S. (2005). Spatial modeling of discontinuous geologic attributes with geotechnical applications.

Engineering Geology,
78 (1–2), 143–161.

CrossRef Google Scholar Manchuk, J. G., Leuangthong, O., & Deutsch, C. V. (2009). The proportional effect.

Mathematical Geosciences,
41 (7), 799–816.

CrossRef 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 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.

CrossRef 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 .

Olea, R. A. (2012). Building on crossvalidation for increasing the quality of geostatistical modeling.

Stochastic Environmental Research and Risk Assessment,
26 (1), 73–82.

CrossRef Google Scholar 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.

CrossRef Google Scholar 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.

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.

Schuenemeyer, J. H., & Gautier, D. L. (2010). Aggregation methodology for the Circum-Artic resource appraisal.

Mathematical Geosciences,
42 (5), 583–594.

CrossRef 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.

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.

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.

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

Authors and Affiliations 1. U.S. Geological Survey Reston USA