Coal exploration modelling using geostatistics in Jharia coalfield, Indiaby Kalyan Saikia, B.C. Sarkar

International Journal of Coal Geology


Economic Geology / Geology / Fuel Technology / Stratigraphy



Characterization han of i in t ing stic co , Dh rel g O of estimation quantified by themagnitude of kriging variance. The uncertaintymaps generated using kriging varg areas of high uncertainty that decide on further sampling to improve the accuracy 2′; E86 state oking c t Cokin

To dat y vario ed (CMPDIL), Geological Survey of India (GSI), and BCCL among others. tionmodelling through simulated annealing; and (v) geological syntheInternational Journal of Coal Geology 112 (2013) 36–52

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International Journa j ourna l homepage: www.e lseThe exploration activities led to the generation of a huge exploration database. However, no exploration modelling work had been carried out using geostatistics. Owing to the presence of high quality coking coal in Jharia coalfield, it has been considered necessary to model and evaluate coal seam characteristics using geostatistics to provide improved means to coal resource characterisation. In the present work, geostatistical modelling and evaluation in terms of estimation and sis of the geostatistical model parameters. Such an integrated approach provides an objective means to geo-characterisation of coal basin.

The key objective of integrated deposit modelling is to provide block estimates withminimum variance. However, in solving for minimum variance, kriging provides estimates associated with smoothing process. To overcome such problem, geostatistical conditional simulation techniques are employed to generate infinite numbers of equi-⁎ Corresponding author. Tel.: +91 326 2235271/2296 2296563.

E-mail addresses: (K. Saikia), bh (B.C. Sarkar). 1 Present address: 2107 CityWest Blvd., BLD. 2, Roo

USA. 0166-5162/$ – see front matter © 2012 Elsevier B.V. All Coal Development d Design Institute Limitstatistical modelling; (iii) geostatistical modelling through semivariography and block kriging; (iv) geostatistical conditional simula-Exploration Corporation Limited (MECL), Na

Corporation (NCDC), Central Mine Planning an1. Introduction

Jharia coalfield (N23°27′ to N23°5 in Dhanbad district of the Jharkhand largest and the only source of prime c ently under the management of Bhara subsidiary of Coal India Limited (CIL). exploration holes have been drilled btative coal seam. The technique provided a means to heterogeneity modelling of coal seams and its advantage over the kriged estimates (smoothing effect) is highlighted. Integration of these geostatistical model parameters with the geology of Jharia coalfield aids in characterization of the coal depositional environment. © 2012 Elsevier B.V. All rights reserved. °06′ to E86°30′) located in India (Fig. 1A) is the oal in India and is presg Coal Limited (BCCL), a e, around 10,000 nos. of us agencies, viz. Mineral simulation have been carried out for ten representative coal seams within Barakar formation of the Jharia coalfield. The exercise aims at highlighting academic importance of such modelling technique and benefits of geostatistical techniques over the conventional technique for resource estimation which is presently practised in Jharia coalfield.

An integrated approach to modelling of coal exploration data of

Jharia coalfield has been conceived in the present study that combines (i) an understanding of the geology of the Jharia coalfield; (ii) classicalof estimation. Simulated annealing algorithm has been applied to carry out geostatistical imaging of a represen-Jharia coalfield iances are of aid in identifyinCoal exploration modelling using geostati

Kalyan Saikia 1, B.C. Sarkar ⁎

Department of Applied Geology, Indian School of Mines, Dhanbad-826004, Jharkhand, India a b s t r a c ta r t i c l e i n f o

Article history:

Received 2 June 2012

Received in revised form 14 November 2012

Accepted 15 November 2012

Available online 29 November 2012





Jharia coalfield, located in D coalfields in India in terms had carried out exploration ever, no exploration modell attempted to derive geostati respect to various proximate

Bharat Coaking Coal Limited into quantification of spatial quality estimation employin617; fax: +91 326 2296616/ m 9.49 B, Houston, TX 77042, rights reserved.ics in Jharia coalfield, India bad district of the Jharkhand state, is one of the most important Gondwana ts prime coking quality of coal and its vast coal resources. Various agencies he Jharia coalfield that led to generation of a huge exploration database. Howhad been carried out using geostatistics. In this context, the present paper has al models of coal seams using the exploration data of ten select coal seams with al constituents and seam thickness that were made available to the authors by anbad. Geostatistical structural modelling through semi-variography resulted ationship of coal quality parameters together with seam thickness. Block-wise rdinary Kriging technique provided improved estimates associated with error l of Coal Geology v ie r .com/ locate / i j coa lgeoprobable realisations, of which the one closest to the reality is chosen by conditioning the simulated values to be the same as that of the sample values at known data locations (Deutsch and Journel, 1997;

Goovaerts, 1997; Olea, 1999). The techniques aim at reproducing both the in situ variability and spatial continuity of the input data set (Costa et al., 2000; Lantuejoul, 2002). Some of the contributions made in the field of spatial modelling of coal resources by researchers include that of Falivene et al. (2007); Heriawan and Koike (2008a,b); Hindistan


JHA 37K. Saikia, B.C. Sarkar / International Journal of Coal Geology 112 (2013) 36–5228o NEW Det al. (2010); Karacan et al. (2012); Olea et al. (2011); andWatson et al. (2001) among others. To describe and emphasise the merit of simulation techniques over kriging, simulated annealing technique has been applied for a selected seam in the present study and results are compared with kriged estimates.