Journal of Geological Resource and Engineering 6 (2018) 151-159 doi:10.17265/2328-2193/2018.04.003 Classification of Coal Resources Using Drill Hole Spacing Analysis (DHSA) Iskandar Zulkarnain and Waterman Sulistyana Bargawa Master of Mining Engineering, UPN Veteran Yogyakarta, Jl. SWK 104 Yogyakarta 55283, Indonesia Abstract: The classification of coal resources generally is based on geometric factors and the complexity of geological structures. The classification has not considered coal quality factors such as ash content, sulphur content, caloric value. The development of international classification standards has required a geostatistical analysis for the estimation and classification of coal resources. The purpose of this research is to apply geostatistics method to determine optimal drill hole distance, and to analyze classification of coal resource based on data of coal quality and quantity. Based on global estimation variance (GEV) approach from geostatistics, relative error value was obtained. Drill hole spacing analysis (DHSA) results in optimal drill hole spacing on each coal seam for the coal resources classification. Estimation using kriging block results in the value of kriging relative error. Coal resources classification was based on relative error of 0-10% for measured resources, 10-20% for indicated resources and > 20% for inferred resources. Based on a case study in a coal field consisting of three coal seams, the geostatistical approach produced the smallest distance on seam-3 as the optimal borehole range in the research area. This classification yields a greater area of influence than the SNI standard on simple geological complexity. Key words: Geostatistics, bore hole spacing analysis, SNI (5015:2011), GEV, kriging relative error. 1. Introduction Several international classification systems [1-4] have been developed in the past, the main ones are the American USGS Circular 831 (USGS, 1980) and SME Guide (SME, 1999), the South-African SAMREC Code (SAMREC, 2000), the Canadian CIM Guidelines (CIM, 2000) and National Instrument 43-101 (CSA, 2001), the European Code (EURO, 2002), the Australasian JORC Code (JORC, 2012), and Indonesia SNI (5015:2011). All these codes are broadly similar, although some differences in their definitions remain. The JORC code is with little doubt the one that has found wider acceptance in countries that do not have their own code. Generally, the basic classification of resources and reserves for coal is a factor of quantity, geometry, and the complexity of geological structures [5]. Limiting factors do not consider coal quality Corresponding author: Waterman Sulistyana Bargawa, Ph.D., Mr., research field: geostatistics and mining environment. factors such as ash content, sulphur content, caloric value. Geometry parameter, and coal quality aspects become an important aspect to determine the classification of coal resources [6]. Applying the approach to any coal basin with certain geological settings will have certain resource classification parameters as well. The area of influence on each coal basin will differ in different geological settings [7, 8]. The development of resource classification standards and coal reserves requires the use of geostatistical approaches. Coal resources classification research has been widely developed using a geostatistical approach [9-13]. 2. Objective The objective of this research is to make the classification of coal resources based on global estimation variance (GEV) approach related to drill hole spacing analysis. The results of the analysis will be compared with SNI 5015:2011 to evaluate the D DAVID PUBLISHING
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Journal of Geological Resource and Engineering 6 (2018) 151-159 doi:10.17265/2328-2193/2018.04.003
Classification of Coal Resources Using Drill Hole
Spacing Analysis (DHSA)
Iskandar Zulkarnain and Waterman Sulistyana Bargawa
Master of Mining Engineering, UPN Veteran Yogyakarta, Jl. SWK 104 Yogyakarta 55283, Indonesia
Abstract: The classification of coal resources generally is based on geometric factors and the complexity of geological structures. The classification has not considered coal quality factors such as ash content, sulphur content, caloric value. The development of international classification standards has required a geostatistical analysis for the estimation and classification of coal resources. The purpose of this research is to apply geostatistics method to determine optimal drill hole distance, and to analyze classification of coal resource based on data of coal quality and quantity. Based on global estimation variance (GEV) approach from geostatistics, relative error value was obtained. Drill hole spacing analysis (DHSA) results in optimal drill hole spacing on each coal seam for the coal resources classification. Estimation using kriging block results in the value of kriging relative error. Coal resources classification was based on relative error of 0-10% for measured resources, 10-20% for indicated resources and > 20% for inferred resources. Based on a case study in a coal field consisting of three coal seams, the geostatistical approach produced the smallest distance on seam-3 as the optimal borehole range in the research area. This classification yields a greater area of influence than the SNI standard on simple geological complexity. Key words: Geostatistics, bore hole spacing analysis, SNI (5015:2011), GEV, kriging relative error.
1. Introduction
Several international classification systems [1-4]
have been developed in the past, the main ones are the
American USGS Circular 831 (USGS, 1980) and SME
Guide (SME, 1999), the South-African SAMREC
Code (SAMREC, 2000), the Canadian CIM Guidelines
(CIM, 2000) and National Instrument 43-101 (CSA,
2001), the European Code (EURO, 2002), the
Australasian JORC Code (JORC, 2012), and Indonesia
SNI (5015:2011). All these codes are broadly similar,
although some differences in their definitions remain.
The JORC code is with little doubt the one that has
found wider acceptance in countries that do not have
their own code. Generally, the basic classification of
resources and reserves for coal is a factor of quantity,
geometry, and the complexity of geological structures
[5]. Limiting factors do not consider coal quality
Fig. 3 Drill hole spacing analysis (DHSA) graph of Seam-3.
0%
0%
1%
10%
100%
100
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800
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1000
1100
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1300
1400
1500
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1700
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1900
2000
2100
2200
2300
2400
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2900
3000
ER
RO
R R
EL
AT
IVE
(%
)
DRILL HOLE SPACING
D H S A : S E A M - 3
Thickness
Ash
CV
RD
TS
Classification of Coal Resources Using Drill Hole Spacing Analysis (DHSA)
157
Fig. 4 Area of influence (or distance) on coal resource classification.
Fig. 5 Various studies: Saraji, Bowen Basin (Bertolli 2013), coal guideline, and SNI 5015:2011 to compare the optimum drill range for resource classification based on relative error.
950
1650
2650
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1850
4300
750
1100
2150
250 500
1000
0
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4500
5000
MEASURED INDICATED INFERRED
Dis
tanc
e of
are
a of
infl
uenc
e (m
)
Distance of area of influencebased on relative error
Seam 1 Seam 2 Seam 3 SNI
Classification of Coal Resources Using Drill Hole Spacing Analysis (DHSA)
158
Table 6 Example results of estimation and resource classification on CV parameters of Seam-1.
Coal Guideline and SNI. As for the classification of
inferred resources, this research is more conservative
than other methods.
Here is a discussion of the value of kriging relative
error for classification of coal resources. Based on
Eq. (3) calculation of kriging value relative error
is obtained from standard deviation value of unit block
with 95% confidence interval. Table 6 shows the
results of resource classification based on the relative
error kriging value.
High value of kriging variance will cause high
relative error value. The highest relative error values in
coal quality parameters are total sulphur > 100% and
ash > 50% included in inferred resource classification.
Geologically high sulphur and ash contents are
associated with sediments deposited in
marine-brackish water environments. Fe element in the
marine-brackish water environment is present in large
numbers, whereas bacterial activity plays a major role
in the formation of high sulphur.
4. Conclusion
(a) Based on comparison of measured, indicated, and
inferred resource classification at the most optimum
distance at Seam-3 with distance of 750 m measured
resource classification at 10% relative error, indicated
1,100 m at 20% relative error, and inferred 2,150 m at
ER 50%.
(b) The results of this study indicate the area of
influence or distance is higher than SNI, but it is still
within range of other methods.
(c) High value of kriging variance will cause high
relative error value. The high relative error values in
coal quality in the study area were totally sulphur (>
100%) and ash (> 50%) included in inferred resource
classification. Geologically high sulphur and ash
contents are associated with sediments deposited in the
brackish-water environment.
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