Optimization of Information Point Distances in the Estimation and Classification of Coal Resources Using Geostatistical Methods Compared to SNI 5015:2019 for Moderate Geological Conditions

Authors

  • Michael Jerrycho Purba Universitas Pembangunan Nasional Veteran Yogyakarta
  • Eko Wicaksono Universitas Pembangunan Nasional Veteran Yogyakarta

DOI:

https://doi.org/10.31315/mtj.v1i1.6826

Keywords:

Coal Estimation, Global Estimation Variance, Kriging, Classification

Abstract

The reference currently used in estimating and classifying coal resources in Indonesia is SNI 5015:2019, which refers to the Australian Guidelines for Estimating and Reporting of Inventory Coal, Coal Resources, and Coal Reserves, 2003 Edition. However, several changes have emerged with the issuance of The JORC Code, 2012 Edition and Australian Guidelines for The Estimation and Classification of Coal Resources, 2014 Edition. One of the changes is in calculating geostatistical aspects in the estimation and classification of coal resources. In this study, we will discuss the need for the use of geostatistical methods and evaluation of SNI 5015:2019. The purpose of this study is to determine the optimal borehole spacing, determine the classification and estimation of resources using the geostatistical method, and compare it with SNI 5015:2019. The method used is the kriging relative error method and global estimation variance. The two methods give different results from SNI 5015:2019. This thing exactly gives different resource estimation results. This difference indicates the need to evaluate the classification and estimation system of SNI 5015:2019, especially related to the use of geostatistical methods accompanied by geological interpretations that describe the actual state of the research location.

References

1. Annels, A.E. 1991. Mineral Deposit Evaluation: A Practical Approach.Netherl ands: Springer.

2. Badan Standarisasi Nasional Tentang Pedoman Pelaporan Sumberdaya, dan Cadangan Batubara, SNI 5015:2019.

3. Bertoli, O., Paul, A., Casley, Z., dan Dunn, D. 2013. Geostatistical Drillhole Spacing Analysis for Coal Resource Classification in the Bowen Basin, Queensl and. International Journal of Coal Geology 112, pp. 107-113.

4. Coalfields Geology Council of New South Wales dan the Queensland Resources Council. 2014. Australian Guidelines for The Estimation and Classification of Coal Resources, 2014 Edition. Sydney, Australia.

5. Cornah, A., Vann, J., dan 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 112, pp. 114-124.

6. De Souza, Costa and Koppe, Uncertainty Estimate in Resourches Assesment: A Geostatistical Contribution, International Association for Mathematic al Geology, 2004.

7. Diehl, P. and David, M., Classification of Ore Reserve/Resources Based on Geostatistic al Methods, CIM Bull, 1982.

8. Erika. 2017. Drill Hole Spacing Analysis with Geostatistics in Coal Resource Evaluation. ITB, Bandung.

9. Wintolo, Djoko. (2019). Introduction to Statistics and Geostatistics. Yogyakarta : Gadjah Mada University Press.9

Downloads

Published

2023-10-31

Issue

Section

Articles