Lignite resource estimations and seam modeling of Thar Field, Pakistan

International Journal of Coal Geology 140 (2015) 84–96

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Lignite resource estimations and seam modeling of Thar Field, Pakistan Fahad Irfan Siddiqui a,⁎, Abdul Ghani Pathan a, Bahtiyar Ünver b, Abdullah Erhan Tercan b, Mehmet Ali Hindistan b, Güneş Ertunç b, Fırat Atalay b, Suphi Ünal b, Yasin Kıllıoğlu b a b

Department of Mining Engineering, Mehran University of Engineering and Technology, Jamshoro, 76062, Pakistan Department of Mining Engineering, Hacettepe University, Beytepe, 06800 Ankara, Turkey

a r t i c l e

i n f o

Article history: Received 26 November 2014 Received in revised form 13 February 2015 Accepted 15 February 2015 Available online 23 February 2015 Keywords: Block model Geostatistics Ordinary kriging Thar field Solid model Spatial distribution Variograms

a b s t r a c t Thar lignite field in Pakistan contains more than 175 billion tons thus far not exploited. To date, the coalfield has been divided into 12 exploration blocks and each block is studied and investigated separately by different agencies. As a result of exploration programs, a large exploration database containing geological and coal quality information is constructed, but geostatistical studies regarding to quantify entire Thar lignite resources is not available yet. This paper aims to generate 3D solid model of main Thar seam and to produce spatial distribution maps for various coal quality attributes by geostatistical method, ordinary kriging. 3D seam model has a volume of 17.71 billion m3. The resulting spatial map of ash content shows structured distribution and LCV map shows fair agreement with ash map except in block VI where high and low values of ash and LCV occur in close proximity. The moisture distribution reveals higher values in northern and southern parts whereas the central portion possesses lower moisture values. The sulfur content shows homogenous distribution with some higher sulfur patches at places. The kriging variance maps are generated to delineate areas of higher uncertainty. These maps could be helpful to devise further exploration programs. Blocks were checked on the basis of global averages and swath plot comparison. © 2015 Elsevier B.V. All rights reserved.

1. Introduction At present, Pakistan is facing serious energy crisis. Severe shortage of electricity has badly affected the industrial and social activities. Pakistan is spending over US$ 14.5 billion on imports of crude oil, petroleum products, coal, LPG etc. to fulfill energy requirements (Hydrocarbon Development Institute of Pakistan, (Hydrocarbon Development Institute of Pakistan (HDIP, 2012)). Coal is a cheap source of energy and widely used throughout the world for power generation, cement and other process industry. The world electricity generation by fuel reveals that coal is the highest contributor in electricity generation and demand of coal will remain dominant in the worldwide electricity generation (Energy Information Administration, USA (EIA, 2011)). The installed electricity generation capacity of Pakistan is about 22.263 GW with 29.9% hydroelectric, 35.2% oil, 29% natural gas, 5.8% nuclear/imported and only 0.1% coal, as shown in Fig. 1 (HDIP, 2012). Share of coal in electricity generation in Pakistan is very negligible as compared to world average. Pakistan possesses the 7th largest lignite resource in the world with nearly 200 billion tons of coal, mainly concentrated in Thar region having more than 175 billion tons of lignite resources (Singh et al., 2011). Thar lignite field, the largest coal resource of Pakistan, is located

⁎ Corresponding author.

http://dx.doi.org/10.1016/j.coal.2015.02.003 0166-5162/© 2015 Elsevier B.V. All rights reserved.

in the eastern part of Sindh Province (shown in Fig. 2). Thar coalfield covers an area of approximately 9000 km2 and number of lignite seams lie at depths between 130 and 250 m. Cumulative seam thickness varies between 1.45 m and 42.6 m and the maximum thickness of an individual seam is 28.6 m (Pathan et al., 2013). The Thar lignite was fortuitously discovered by British Overseas Development Agency (BODA) and Sindh Arid Zone Development Authority (SAZDA) during drilling for water wells near the village of Khario Ghulam Shah, Tharparkar, in 1988 (Fassett and Durrani, 1994). Further exploration work was conducted by Geological Survey of Pakistan (GSP) and United States Geological Survey (USGS) under Coal Resources Exploration & Assessment Program (COALREAP) from 1989 to 1994. In this exploration program, total 21 boreholes were sunk at an average spacing of 20 km to define the extent of coal occurrence (Thomas et al., 1994). Total hypothetical lignite resources were estimated at 175.506 billion tons. Since then 12 exploration blocks have been investigated separately by various agencies viz: GSP, Shenhua (China), Rheinbraun(Germany), Sindh-Engro(SECMC), Deep Rock Drilling (DRD) from 1994 to 2012. As a result of these exploration programs, a large exploration data including geological and coal quality information, is available; however no resource modeling study has been conducted to quantify entire Thar lignite resources using geostatistical modeling. Several research studies on coal resource estimation and seam modeling have been carried out. Olea et al., 2011 combined different geostatistical methods for quantitative characterization for evaluating

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Fig. 1. Pakistan's electricity generation by fuel, 2011–12 (HDIP, 2012).

the uncertainty and indicated that distance to the neighboring drill hole is more or less completely dissimilar to the uncertainty, which demonstrate the deficiency of distance-based resource estimation approach. Hohn and Britton (2012) also demonstrated significance of geostatistical estimation over distance-based classification method. Tercan and Karayigit (2001) also estimated lignite reserves in Kalburcayiri field, Kangal basin, Sivas, Turkey using global estimation variance (GEV) approach. Pardo-Iguzquiza et al. (2013) assessed the risk associated with lignite reserves estimations at North-western Spain using semi-variograms and conditional simulation. Geostatistical modeling was conducted for quantification of error associated with reserves estimation at Jharia coalfield, India. The uncertainty maps were generated using kriging variances and areas of high uncertainty were

85

identified for additional sampling for precise estimation (Saikia and Sarkar, 2013). Hindistan et al. (2010) conducted case study on dilution problem at an underground coal mine in Turkey. The mean calorific values of the blocks inside the production panels were estimated using ordinary kriging to devise short term mine plan. Tercan et al. (2013) conducted thorough study at Eynez–Soma and Ömerler–Tunçbilek coal fields, Turkey for seam modeling and estimation of lower calorific value. The study sites were subjected to heavy tectonic movements and erratic coal quality. Falivene et al. (2014) carried out three-dimensional coal facies interpolations and simulations in a heterogeneous coal zone in the As Pontes Basin (North Western Spain) to forecast coal resources and reserves. This study ascertained that due to interpolation smoothing, the three-dimensional facies interpolation methods tend to overestimate coal resources and reserves whereas facies simulation methods yield similar resource predictions than conventional thickness map approximations. Vasquez and Nieto (2004) estimated calorific value and ash by weighting kriging estimation variances. Ertunc et al. (2013) estimated the lower calorific value, ash content, and moisture content of lignite deposit subjected to severe tectonic activity, by using covariance matching constrained kriging and ordinary kriging. It is found that covariance matching constrained kriging replicates the spatial variability better than ordinary kriging. Heriawan and Koike (2008a) and Heriawan and Koike (2008b) estimate thickness, ash, sulfur and calorific value in a multiple seam environment at East Kalimantan (Borneo, Indonesia) by means of ordinary kriging, cokriging and factorial Kriging. This paper aims to estimate various coal quality parameters using ordinary kriging technique that provides improved estimates and associated errors by means of kriging variances.

N

Pakistan

Sindh

Coal Boundary Indian Territory

Karachi

Tharparkar district Boundary

Cross-section AB shown in Fig. 4

Exploration Blocks

Rann-of-Kutch fault zone

Fig. 2. Location map of Thar Lignite Field, Pakistan.

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Era

Period

Epoch

Quaternary

Dune Sand

Sand, silt and clay

14-93 Unconformity

Sub-Recent

Alluvial Deposits

Sand, siltstone, claystone and Sandstone

11-209

Eocene/ Palaeocene

Bara Formaon

--

--

Claystone, shale, sand, Lignite seams, carbonaceous Claystone

52-95

Unconformity

PRE-CAMBRIAN

Lithology

Unconformity

Terary

CENOZOIC

Recent

Thickness (m)

Formation

Basement complex

--

- -- --- -- - -

Granite and quartz diorite

Fig. 3. Generalized stratigraphy of Thar Lignite Basin.

2. Case study 2.1. Geographical location & geological setting Thar coalfield is located at Tharparkar district of Sindh Province, Pakistan between latitudes 24° 15′N–25° 45′N and longitudes 69° 45′E– 70° 45′E. The Thar coal field is covered by thick dune sands with an average depth of 80 m resting upon the structural platform in the eastern part of the desert, underlain by relatively shallow granitic basement rocks of Precambrian age (Fassett and Durrani, 1994). The Thar coal field is structurally simple. Strata dips gently at around 2° to west-northwest. The Rann-of-Kutch fault zone is the only fault in the area, which occurs away from exploration blocks, in the most southern part of the field (Fig. 2). The Lignite bearing lower Indus basin contain sedimentary rock fill comprises Mesozoic and Cenozoic sections. The present relationship of the strata indicates the existence of a regional high in the Thar Coal Field area during Mesozoic, resulting in erosion of Palaeozoic rocks

and exposure of basement granites. The erosion cycle was followed by deposition during Palaeocene and Early Eocene. During this time the environment was suitable for the formation of the Thar coals. Subsequently the Thar Coal Field remained stable, resulting neither in the deposition of younger rocks nor in the erosion of the relatively thin sedimentary layer of Palaeocene–Early Eocene. During recent times the area subsided and was traversed by the Indus river system, which deposited the sub-recent alluvial sediments in the area. The dunes of the Recent-Formation formed about 20,000 years ago. Fig. 3 shows the generalized stratigraphic section of Thar basin. Lignite seams in the Thar area are found in Bara formation of Palaeocene/Eocene age. The Bara formation is some 95 m thick consisting of sandy/silty claystone and sandstone formation overlying the basement granite lying at a depth of 100 m to 220 m (Singh et al., 2010b). The basement rock is slightly to moderately weathered granite comprising fine to coarse grains of quartz. The overlying Bara formation consists of layers of carbonaceous clay stone, sandy clay stone and silty clay stone. The carbonaceous clay stone is medium light grey to brown

Fig. 4. General lithology of Thar lignite field, Pakistan (GSP, 2002).

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Table 1 Summary of drilling activities.

All 12 blocks

No. of drillholes

Average drillhole spacing (m)

Total drilled meters (m)

Minimum depth (m)

Maximum depth (m)

Average drillhole depth (m)

693

1400

166,675

109.78

319.68

240.51

in color containing carboniferous petrified roots, carbonaceous materials and rare sandy resin globules. The olive grey to dark-grey claystone containing petrified coal roots and pyretic resin globules overlies this sediment (Singh et al., 2010a). The sub-recent formation overlies the Bara formation. It comprises of siltstone, sandstone, and claystone, with an average thickness of 70 m and lies between the depth of 52 and 125 m. The recent formation overlying the sub-recent formation consists of sand dunes. This sand is fine to medium grained, yellowish grey in color containing subrounded and moderately sorted grains of ferromagnesian minerals. Fig. 4 shows the stratigraphy and lithology of the Thar coalfield.

2.2. Drillhole Database & Exploratory Data Analysis (EDA) The data used in this study were obtained from 693 boreholes sunk by various governmental and non-governmental agencies from 1994 to 2012. The obtained information includes, collar coordinates, geological intercepts, and coal quality parameters. The details of drilling activities are given in Table 1 and the location of drillholes in all blocks is shown in Fig. 5. The quality data of a total 6095 drill intersected lignite samples were collected. The quality data includes, moisture content, ash, fixed carbon, volatile matter, sulfur and lower calorific values (LCV) on as received

5 km

Fig. 5. Drillhole location in all 12 blocks of Thar lignite field, Pakistan.

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basis. In the present study, only ash content, moisture, sulfur and LCV are considered. A complete electronic database including all geologic and quality information, were prepared for solid modeling, variogram analysis, block modeling and ordinary kriging. The frequency distributions are presented in Fig. 6. The raw core intervals present a right skewed distribution with a mean of 1.52 m. The ash and sulfur show right skewed distribution as most values are concentrated on left of the mean value. LCV histogram represents normal distribution while moisture content shows left-skewed distribution. 2.3. Solid modeling A solid model is a three-dimensional triangulation of data. In general there are two approaches used in solid modeling i.e. section method and top–bottom surface method. In the section method coal seam is outlined in vertical sections and these sections are then combined to construct a 3D solid model. In the top–bottom surface method the No. of Samples Mean Std. Deviation Minimum Maximum

roof and floor surfaces of coal seam are triangulated or interpolated and then combined. In this study, Gemcom SURPAC 6.2 was used to create solid model by outlining the coal seam in numerous vertical sections drawn at perpendicular, parallel and oblique directions. Section method was selected due to structural simplicity of Thar coal field. The Bara formation (coal bearing formation) at Thar field contains number of coal seams. Due to larger drillhole spacing (average 1400 m), it is difficult to model all the seams; therefore this study is limited to model the main Thar seam which is the thickest seam and present in all exploration blocks. Fig. 7 shows complete process of generating 3D seam model in block II from geological information. Number of vertical sections (Fig. 7a) were taken in parallel (Fig. 7b) and perpendicular (Fig. 7c) directions. Two different solid models were generated using parallel and perpendicular sections, as shown in Fig. 7d and e. The two solids were then combined (shown in Fig. 7f) and further section was drawn considering two solids together (Fig. 7g). Final 3D model of the seam is generated by triangulating the final sections, as shown in No. of Samples Mean Std. Deviation Minimum Maximum

:6095 :1.52 :1.09 :0.04 :22.33

No. of Samples Mean Std. Deviation Minimum Maximum

:6095 :2799 :461 :417 :5277

No. of Samples Mean Std. Deviation Minimum Maximum

No. of Samples Mean Std. Deviation Minimum Maximum

Fig. 6. Raw data histograms.

:6095 :47.24 :5.98 :5.53 :68.58

:6095 :1.28 :1.22 :0.01 :15.64

:6095 :7.34 :4.8 :1.06 :49.51

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89

a

N

b

c

N

d

N

e

N

f

N

g

N

h

N

Fig. 7. Process of generating seam model in Block II, a) example of single verticals section, b) parallel sections, c) perpendicular sections, d) solid model generated from parallel sections, e) solid model generated from perpendicular sections, f) combining two solids, g) further sectioning of combined solid, and h) 3D solid seam model.

Fig. 7h. Similarly, the 3D seam models were generated individually in all remaining blocks and finally gap between the seam models was filled by further sectioning and triangulations. Fig. 8 presents individual seam models in different blocks and final seam model. The total volume of final 3D seam model is 17,716,072,160 m3 (17.716 billion m3). The seam thickness is derived by summing up individual block thickness

in block model (Tercan et al., 2013). Fig. 9 shows the spatial distribution of main seam thickness. The thickness map indicates that seam is thicker in block I, II, IV & VI and this portion possibly could be the center of the lignite depositional basin. The eastern part of block VIII also shows thickest seam zone whereas the boreholes in the central part of block VIII showed no coal intercept in the entire borehole column.

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200 m

20Xvertical exaggeration

0m

-250 m

200 m 0m -250 m

20Xvertical exaggeration

Fig. 8. Individual solid seam models (above) and combine master solid model (below).

2.4. Compositing Compositing is a length-weighted average sampling. The down hole composites of equal length are used as input for geostatistical estimations. The raw samples had variable intervals with a mean of 1.52 m hence composited to 2 m down-hole lengths. The minimum 75% of samples was considered for compositing. The mean ash value of composited data is decreased as compared to raw data. Similarly mean LCV value is increased. The frequency histograms of composite data are shown in Fig. 10. Frequency distribution of composite data is approximately similar to the raw data.

constructed in four different directions (i.e. 0°, 45°, 90°, and 135°) to detect anisotropy. Directional horizontal variograms reveal no anisotropy in the all variables; hence omni-directional variograms are selected. The experimental and model variograms of all variables in vertical and horizontal directions are shown in Fig. 11. Table 2 summarizes the model variogram parameters for all quality variables. The variogram ranges in vertical direction are considerably lower than the ranges in horizontal direction as it is quite expected in case of bedded deposit. The experimental variograms for all the variables are fitted with spherical models. Ash parameter exhibits lowest range in horizontal direction and highest nugget to sill ratio when compared to other variables. The horizontal ranges of LCV, moisture and sulfur are approximately same.

2.5. Semi-variography 2.6. Block kriging Experimental variograms were calculated from the composite data in lateral and vertical directions. Vertical/downhole experimental variograms were constructed to establish the nugget and sill values. No serious trend has been observed from the vertical variograms for all the quality variable. The horizontal experimental variograms were

The generated 3D solid seam model is divided into 288,423 blocks with size of 200 m × 200 m × 2 m and sub-blocks with size of 100 m × 100 m × 2 m. The size of the blocks is selected according to average drillhole spacing and composite length. Block mean values are

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91

Main Seam Thickness (m)

Fig. 9. Spatial distribution of main seam thickness.

No. of Samples Mean Std. Deviation Minimum Maximum

No. of Samples Mean Std. Deviation Minimum Maximum

:4900 :6.39 :3.75 :1.94 :35.45

:4900 :47.75 :5.15 :9.40 :67.03

Fig. 10. Composite data histograms.

No. of Samples Mean Std. Deviation Minimum Maximum

:4900 :2872 :418 :588 :5277

No. of Samples Mean Std. Deviation Minimum Maximum

:4900 :1.13 :0.96 :0.05 :15.64

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30

Ash

Ash 25

Semi-variance (%)2

Semi-variance (%)2

25

20

15

10

5

20

15

10 Omni-Directional Experimental

5

Downhole-Expermental

Omni-Directional Model

Downhole-Model

0

0 0

5

10

15

20

25

30

0

1000

2000

Distance, h (m)

3000

250000

250000

Semi-variance (kCal/kg)2

200000

150000

100000

50000

150000

100000

50000 Omni-Directional Experimental Omni-Directional Model

Downhole-Model

0

0 0

10

20

30

40

0

50

1000

2000

Distance, h (m) 30

3000

4000

5000

6000

Distance, h (m) 30

Moisture

Moisture

25

Semi-variance (%)2

25

Semi-variance (%)2

6000

200000

Downhole-Expermental

20

15

10

5

20 15 10 5

Downhole-Expermental

Omni-Directional Experimental Omni-Directional Model

Downhole-Model

0

0 0

10

20

30

40

50

0

60

1000

2000

Distance, h (m) 2

3000

4000

5000

6000

Distance, h (m) 2

Sulphur

1.8

Sulphur

1.8 1.6

Semi-variance (%)2

1.6

Semi-variance (%)2

5000

LCV

LCV Semi-variance (kCal/kg)2

4000

Distance, h (m)

1.4 1.2 1 0.8 0.6

1.4 1.2 1 0.8 0.6 0.4

0.4 Downhole-Expermental

0.2

Omni-Directional Experimental Omni-Directional Model

0.2

Downhole-Model

0

0 0

5

10

15

20

25

0

1000

2000

Distance, h (m)

3000

4000

5000

6000

Distance, h (m)

Fig. 11. Experimental variograms fitted with model variograms in vertical (left) and horizontal (right) directions for all quality variables.

estimated by employing ordinary kriging technique. Model variogram parameters were used to define maximum horizontal and vertical search distance, anisotropic ratios, bearing and dip during ordinary kriging. The minimum and maximum numbers of samples used in estimation are 3 and 16, respectively. The spatial distribution of ash, LCV, moisture and sulfur is shown in Fig. 12a, b, c and d respectively. The spatial map of ash content (Fig. 12a) shows structured distribution and LCV map (Fig. 12b) shows fair agreement with ash map except in block VI (refer Fig. 2 for block identification) where high and low values of ash and LCV are separated. The moisture map (Fig. 12c) reveals higher values in northern and southern

Table 2 Model variogram parameters for all quality variables.

Nugget, C0 Structure C1 Structure C2 Range, a1 (m) Range, a2 (m)

Ash %

Lower calorific value (kCal/kg)

Moisture %

Sulfur %

7 13.75 – 2600 21.5 (vert.) – –

50,000 20,000 80,000 1400 14.5 (vert.) 3500 48 (vert.)

2 5 12 500 6 (vert.) 3400 46 (vert.)

0 0.5 1 520 4 (vert.) 3350 23 (vert.)

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93

b

a

Ash %

Section AB

LCV (kCal/kg)

Section AB

c

d

Moisture % Sulphur %

Section AB

Section AB

Fig. 12. Spatial distribution of (a) ash content, (b) lower calorific value (LCV), (c) moisture content and (d) sulfur (cross-sections are 20× vertically exaggerated).

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LCV (kCal/kg)2

Ash %2

a Moisture % 2

b Sulphur % 2

c

d

Fig. 13. Kriging variance maps. (a) Ash content, (b) lower calorific value (LCV), (c) moisture content and (d) sulfur.

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and block estimates for all the quality variables. Averages of block model and composites are notably closer to each other. Block mean ash value is lower as compared to composite mean; correspondingly block mean LCV value is higher than composite mean value. The swath plots of block centroids and composites were constructed in northing, easting and downward directions. Trend comparison of block models and composites is shown in Fig. 14. The trend comparison shows that block averages estimated by ordinary kriging technique have almost same global pattern as the composite averages. The block averages are smoother than composite which is quite expected in case of ordinary kriging.

Table 3 Descriptive statistics of composite and block estimates.

Moisture % Sulfur %

Mean

Maximum

Std. deviation

1.94 2.45 588 883 9.40 17.93 0.05 0.32

6.39 6.32 2872 2909 47.75 47.25 1.13 1.12

20.51 35.45 5277 4305 67.03 59.57 15.64 8.28

3.75 2.18 418 345.25 5.15 4.61 0.96 0.48

parts whereas the central portion possesses lower moisture values. The spatial map of sulfur content (Fig. 12d) shows homogenous distribution with some higher sulfur patches at places (i.e. north-west of block I, western part of block II and north-eastern part of block III). Fig. 13a, b c, and d shows kriging variance for ash content, LCV, moisture and sulfur. The kriging variance maps are generated to delineate areas of higher uncertainty. These maps could be helpful to devise further exploration programs.

4. Conclusions Being an energy-deficient country, Pakistan must develop and exploit the indigenous and sustainable energy sources to fulfill its exponentially increasing energy needs. Thar coal has a potential to fullfil country's energy needs for years. The exploration blocks at Thar were studied separately but it is important to assess the Thar coalfield as a whole; otherwise, modeling and designing of mines separately in each block will certainly lead to serious problems in future. Therefore, this paper aims to generate 3D seam model and to produce spatial distribution maps for different quality variables using combine exploration data from all the blocks. 3D seam model has a volume of 17.71 billion m3. Ash content in whole field shows structured distribution and LCV distribution shows fair agreement with ash map except in block VI where

3. Kriging validation

5000

20 18 16 14 12 10 8 6 4 2 0

Composite

Block Model

3000 2500

1500 2360000

2370000

2380000 1000 2340000

790000 780000 770000 760000 750000 10

Ash %

800000 790000 780000

20

1 0.5 2370000

0 2340000

2380000

800000 790000 780000

790000 780000

760000

5000

750000 20

30

LCV (Kcal/kg)

4000

5000

50

60

0

70

-60

-60

-60

-80

-80

-80

-100

-100

-100

40

50

60

0

80

-160

-160

-160

-200

-200

Composite

2

3

-180 Composite Block Model

Block Model

-200

1

-120

-160

-180

3.5

-100

-120

Block Model

3

-80

-140

Composite

2.5

-60

-140

-180

2

-40

-40

-120

1.5

4

Sulphur % 70

-140

Composite Block Model

1

Sulphur %

-140

-180

0.5

Moisture %

6000

Depth

3000

30

-40

Depth

2000

40

Moisture %

Lower Calorific Value (Kcal/kg) 30

Block Model

800000

750000

4000

2380000

Composite

750000 3000

2370000

810000

770000

2000

2360000

820000

Composite Block Model

810000

760000 1000

2350000

Easting (m)

820000

1000 -40

-120

2 1.5

760000 0

Block Model

2.5

770000

15

Composite

3

770000

Ash % 10

4 3.5

Easting (m)

Composite Block Model

810000

Northing (m)

800000

0

2370000

820000

Composite Block Model

810000

5

2360000

Easting (m)

820000

0

2350000

Northing (m)

2350000

70 Composite Block Model 65 60 55 50 45 40 35 30 25 20 2350000 2360000 2380000 2340000

Moisture %

3500

2000

Easting (m)

Depth

Block Model

4000

2340000

Northing (m)

Composite

4500

LCV (Kcal/kg)

Ash %

Block values for ash, LCV, moisture and sulfur estimated by ordinary kriging are compared with composite values on the basis of global averages and trend plots. Table 3 presents descriptive statistics of composite

Sulphur %

LCV (Kcal/kg)

Minimum

Northing (m)

Composites Block model Composites Block model Composites Block model Composites Block model

Depth

Ash %

95

-200

Fig. 14. Trend comparison of various quality attributes in easting (upper), northing (middle) and elevation (lower).

4

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high and low values of ash and LCV occur in close proximity. The moisture distribution reveals higher values in northern and southern parts whereas the central portion possesses lower moisture values. The spatial map of sulfur content shows homogenous distribution with some higher sulfur patches at places. The kriging variance maps are generated to delineate areas of higher uncertainty. Accuracy of geostatistical estimations can be improved by additional sampling in the areas of higher uncertainty. Acknowledgment Authors are thankful to British Council, Pakistan, Higher Education Commission, Pakistan and Mehran University of Engineering & Technology, Jamshoro, Pakistan (SP-247) for providing financial support under INSPIRE program for this research. Thanks are also due to Thar Coal Energy Board (TCEB), Energy Department, Government of Sindh, Pakistan for providing exploration data, vital for the conduct of this academic research. Special acknowledgements to Hacettepe University, Ankara, Turkey for providing necessary support for smooth conduct of the research. Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.coal.2015.02.003. These include Google map of the most important areas described in this article. References EIA, 2011. International Energy Outlook 2011 vol DOE/EIA-0484(2011). Energy Information Administration, U.S. Ertunc, G., Tercan, A.E., Hindistan, M.A., Unver, B., Unal, S., Atalay, F., Kıllıoglu, S.Y., 2013. Geostatistical estimation of coal quality variables by using covariance matching constrained kriging. Int. J. Coal Geol. 112, 14–25. http://dx.doi.org/10.1016/j.coal. 2012.11.014.

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