Application of the Global Estimation Variance and Sill Variogram Methods in Determining the Optimum Drill Hole Distance for the classification of Measured Coal Resources

Authors

DOI:

https://doi.org/10.11137/1982-3908_2025_48_60123

Keywords:

Geostatistics, Geological Complexity, SNI standard

Abstract

In the implementation of effective and efficient exploration of mineral and coal resources, it is required to carry out geostatistical analysis to determine the relative error value of the optimum spacing of the drilling and its thickness and quality distribution. This study uses the application of geostatistics with the sill variogram method and global estimation variance (GEV), based on the relative value of the error of the thickness of the coal seam. This research was conducted in the concession area of PT. Kaltim Prima Coal. Based on the case study, the coalfield consisted of two seams, namely the North BE and South BE seams, with moderate geological conditions. From the variogram analysis of the thickness of the northern BE layer, the range is 151, the sill is 0.65, and the Nugget Effect is 0.04, while the BE South has a range value of 209, the sill is 0.16, and the nugget effect is 0.26. Based on field data, the average drill hole distance in the North BE seam is 131 meters. The distance of 1/3 of the sill gets a value of 50 meters (measured resource), while for the South BE seam it is 126 meters. The distance of 1/3 sill gets a value of 60 meters (measured resource), the distance of 2/3 sill gets a value of 122 meters (resource indicated), and the distance of 3/3 sill gets a value of 210 meters (inferred resource). Based on global estimation variance analysis. For the North BE Seam, the drill hole spacing for the measured resource category was 350 meters with a total of 29 drills, while for the South BE Seam, the drill hole spacing for the measured resource category was 350 meters with a total of 104 drills. The results of this classification produce an area of influence that is smaller than the SNI standard of moderate geological complexity. By using the sill variogram analysis, the results of the drill hole spacing are dense when compared to the results of the GEV analysis. The results of this GEV classification produce an area of influence that is relatively like to the SNI standard of moderate geological complexity.

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Author Biographies

Wiwik Dahani , Trisakti University, Department of Mining

Trisakti University, Department of Mining, West Jakarta City, Indonesia

Subandrio Subandrio, Trisakti University, Department of Mining

Trisakti University, Department of Mining, West Jakarta City, Indonesia

Ahmad Azizi , Trisakti University, Department of Mining

Trisakti University, Department of Mining, West Jakarta City, Indonesia

Rhazes Eesha Gumay , University of Western Australia, Business Analytics

University of Western Australia, Business Analytics, Crawley, Australia

References

Annels, A.E. 2012, Mineral deposit evaluation: A practical approach, Springer Science & Business Media.

Badan Standar Nasional. 2011, SNI 5015; 2011 Pedoman pelaporan sumberdaya dan cadangan Batubara, Badan Standardisasi Nasional.

Bohling, G. 2005, Introduction to Geostatistics and Variogram Analysis.

Cornah, A., Vann, J. & Driver, I. 2013, “Comparison of three geostatistical approaches to quantify the impact of drill spacing on resource confidence for a coal seam (with a case example from Moranbah North, Queensland, Australia)”, International Journal of Coal Geology, vol. 112, pp. 114–124, DOI:10.1016/j.coal.2012.11.006.

Crozel, D. & David, M. 1985, “Global estimation variance: Formulas and calculation”, Journal of the International Association for Mathematical Geology, vol. 17, no. 8, pp. 785–796, DOI:10.1007/BF01034061.

David, M. 1982, Geostatistical Ore Reserve Estimation, Elsevier.

Deutsch, C & Journel, A.G. 1992, Geostatistical Software Library and User’s Guide, Oxford Univ. Press.

Faidatulaila, R., Marwanza, I. & Purwiyono, T.T. 2023, “Variography analysis on the assessment of coal deposit quality using the ordinary kriging method”, Proceedings of 4TH International Conference On Earth Science, Mineral And Energy, vol. 2598, no. 1, p. 060002, DOI: 10.1063/5.0126896

Heriawan, M.N., Pillayati, P., Widodo, L.E. & Widayat, A.H. 2020, “Drill hole spacing optimization of non-stationary data for seam thickness and total sulfur: A case study of coal deposits at Balikpapan Formation, Kutai Basin, East Kalimantan”, International Journal of Coal Geology, vol. 223, 103466, DOI:10.1016/j.coal.2020.103466

Isaaks, E.H. & Srivastava, R. 1989, An introduction to Applied Geostatistics, Oxford University Press.

Jeuken, R., Xu, C., & Dowd, P. 2020, “Improving coal quality estimations with geostatistics and geophysical logs”, Natural Resources Research, vol. 29, no. 4, pp. 2529-2546, DOI:10.1007/s11053-019-09609-y

Journel, A.G. & Huijbregts, C.J. 1978, Mining geostatistics, Academic Press.

Marwanza, I.R.F.A.N., Hamdani, A.H., Haryanto, I.Y.A.N., & Nas, C.H.A.I.R.U.L. 2016, “Classification Of Geological Conditions Using Geostatistics In Coal Field, Sangatta, East Kalimantan, Indonesia”, Journal of Research in Applied, Natural and Social Sciences, vol. 4, pp. 129-140.

Nas, C. 1994, “Spatial variations in the thickness and coal quality of the Sangatta Seam, Kutei Basin, Kalimantan, Indonesia”, University of Wollongong, Department of Geology.

Nengovhela, A. C. 2018, The application of geostatistics in coal estimation and classification, PhD Thesis, University of the Witwatersrand, South Africa.

Ramadhan, M. D., Marwanza, I., Nas, C., Azizi, M. A., Dahani, W. & Kurniawati, R. 2021, “Drill Holes Spacing Analysis for Estimation and Classification of Coal Resources Based on Variogram and Kriging”, IOP Conference Series: Earth and Environmental Science, vol. 819, no. 1, p. 012026, DOI:10.1088/1755-1315/819/1/012026

Sianturi, R.K., Heriawan, M., Syafrizal, S., Ardian, C., Amertho, S. & Lubis, I. 2021, “Perbandingan Tiga Pendekatan Geostatistik Untuk Memodelkan Ketidakpastian Dalam Estimasi Sumberdaya Timah Dan Mineral Ikutan Timah Pada Endapan Aluvial”, Indonesian Mining Professionals Journal, vol. 2, no. 2, pp. 65–74, DOI: 10.36986/impj.v2i2.34

Sianturi, R.K., Heriawan, M.N. & Syafrizal, S. 2020, “Analisis Spasi Lubang Bor Untuk Mengevaluasi Sumberdaya Timah Aluvial Dan Mineral Ikutannya Di Pulau Bangka Dengan Global Estimation Variance", RISET Geologi dan Pertambangan, vol. 30, no. 2, pp. 153, DOI:10.14203/risetgeotam2020.v30.1115

Silva, D.S.F. & Boisvert, J.B. 2014, “Mineral resource classification: a comparison of new and existing techniques”, Journal of the Southern African Institute of Mining and Metallurgy, vol. 114, no. 3, pp. 265-273.

Snowden, D.V. 1996, “Practical interpretation of resource classification guidelines”, AusIMM Annual Conference, Perth (vol. 68).

Snowden, D.V. 2001, Practical interpretation of mineral resource and ore reserve classification guidelines, Mineral Resource and Ore Reserve Estimation-The AusIMM Guide to Good Practice.

Soares, A., Nunes, R. & Azevedo, L. 2017, “Integration of Uncertain Data in Geostatistical Modelling”, Mathematical Geosciences, vol. 49, no. 2, pp. 253–273, DOI:10.1007/s11004-016-9667-5

Srivastava, R.M. 2013, “Geostatistics: A toolkit for data analysis, spatial prediction and risk management in the coal industry”, International Journal of Coal Geology, vol. 112, pp. 2–13, DOI:10.1016/j.coal.2013.01.011

Vann, J., Jackson, S. & Bertoli, O. 2003, “Quantitative kriging neighbourhood analysis for the mining geologist-a description of the method with worked case examples”, Proceedings 5th international mining geology conference, vol. 8, pp. 215-223.

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Published

2025-12-18

How to Cite

Marwanza, I. (2025) “Application of the Global Estimation Variance and Sill Variogram Methods in Determining the Optimum Drill Hole Distance for the classification of Measured Coal Resources”, Anuário do Instituto de Geociências. Rio de Janeiro, BR, 48. doi: 10.11137/1982-3908_2025_48_60123.