Distance of drill-hole determination based on global estimation variance of coal resources classification

Satria Abadi

Abstract

Based on SNI 5015:2019, the estimation of coal resources is based on geological conditions, as well as the distance of information points for each geological condition and resource class. PT. AMNK is a coal mining company and has now started mining. The mines are divided into three blocks, north, middle, and south. Drilling activities have been carried out in the middle block, with 66 drilling points resulting in 4 main seams namely, H seam, I seam, J seam, and K seam. The average drill hole distance is 150 m. This type of coal deposit belongs to the moderate geological group characterized by the presence of a fault in the middle block. Further analysis of optimum drill hole distances is carried out as a basis for the estimation and classification of resources, which in 2024 will be used as an exploration target for the southern block. Analysis of the optimum drill hole distance based on the "relative error" value using the GEV (Global Estimation Variance) method and the size of the sill variogram. Based on the analysis of the K seam, the optimum borehole distances were 385 m, 750 m, and 1750 m, measured, indicated, and inferred categories, respectively. 2023, Penerbit Akademia Baru. All rights reserved.

References

Indonesia, Standar Nasional. "Pedoman pelaporan hasil eksplorasi, sumber daya, dan cadangan batubara." (2019).

Martínez-Vargas, Adrian. "Optimizing grade-control drillhole spacing with conditional simulation." Minería y Geología 33, no. 1 (2017): 1-12.

Bertoli, Olivier, Andrew Paul, Zach Casley, and Doug Dunn. "Geostatistical drillhole spacing analysis for coal resource classification in the Bowen Basin, Queensland." International Journal of Coal Geology 112 (2013): 107-113. https://doi.org/10.1016/j.coal.2012.12.010

Dominy, Simon C., Alwyn E. Annels, G. Simon Camm, Paul Wheeler, and Suzanne P. Barr. "Geology in the resource and reserve estimation of narrow vein deposits." Exploration and Mining Geology 4, no. 6 (1997): 317-333.

Fytas, K., N-E. Chaouai, and M. Lavigne. "Gold deposits estimation using indicator kriging." CIM bulletin 83, no. 934 (1990): 77-83.

Santesteban, L. G., S. Guillaume, J. B. Royo, and Bruno Tisseyre. "Are precision agriculture tools and methods relevant at the whole-vineyard scale?." Precision Agriculture 14 (2013): 2-17. https://doi.org/10.1007/s11119-012-9268-3

Schabenberger, Oliver, and Carol A. Gotway. Statistical methods for spatial data analysis. CRC press, 2017. https://doi.org/10.1201/9781315275086

Sianturi, Raymond Kosher, Mohamad Nur Heriawan, and Syafrizal Syafrizal. "Analisis Spasi Lubang Bor Untuk Mengevaluasi Sumberdaya Timah Aluvial Dan Mineral Ikutannya Di Pulau Bangka Dengan Global Estimation Variance." Riset Geologi dan Pertambangan-Geology and Mining Research 30, no. 2 (2020): 153-169. https://doi.org/10.14203/risetgeotam2020.v30.1115

Matheron, Georges. "Principles of geostatistics." Economic geology 58, no. 8 (1963): 1246-1266. https://doi.org/10.2113/gsecongeo.58.8.1246

Sarma, Dhavala Dattatreya. Geostatistics with applications in earth sciences. Springer Science & Business Media, 2010. https://doi.org/10.1007/978-1-4020-9380-7

Cornah, Alastair, John Vann, and Ian Driver. "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 112 (2013): 114-124. https://doi.org/10.1016/j.coal.2012.11.006

Wellmer, Friedrich-Wilhelm. Statistical evaluations in exploration for mineral deposits. Springer Science & Business Media, 2012.

Cambardella, Cynthia A., T. B. Moorman, J. M. Novak, T. B. Parkin, D. L. Karlen, R. F. Turco, and A. E. Konopka. "Field‐scale variability of soil properties in central Iowa soils." Soil science society of America journal 58, no. 5 (1994): 1501-1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x