Coal exploration modelling using geostatistics in Jharia coalfield, India

Saikia, Kalyan; Sarkar, B. C.

doi:10.1016/j.coal.2012.11.012

Cited by 34 publications

(9 citation statements)

References 14 publications

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“…Resources comprise the estimated amount of coal in the subsurface. Typically, thickness maps have been used for assessing resources, either using: a) interpolation (Starks et al, 1982;Schuenmeyer and Power, 2000;Tercan and Karayigit, 2001;Watson et al, 2001;Koike, 2008a and2008b;Tercan et al, 2013;Saikia and Sarkar 2013), or b) geostatistical simulation methods (Costa et al, 2000;2001a and2001b;de Souza et al, 2004;Olea et al, 2011;Olea and Luppens 2012;Hohn and Britton, 2013;Cornah et al, 2013;Pardo-Iguzquiza et al, 2013). Interpolation methods applied in this context are appropriate when the goal is to provide a unique composite global estimate for resources.…”

Section: Coal Resources Reserves and 3d Facies Modelsmentioning

confidence: 99%

“…The next levels of complexity use a coal proportion map (2D-Pro, Figure 4B) or directly the common coal thickness map approach (2D-Thick, Figure 4C) (Starks et al, 1982;Schuenemeyer and Power, 2000;Hohn and McDowell, 2001;Tercan and Karayigit, 2001;Heriawan and Koike, 2008b;Saikia and Sarkar, 2013). For computing coal volumes using a coal proportion map the total thickness map of the coal zone ( Figure 4A) is also used.…”

Section: Coal Proportions Benchmarkmentioning

confidence: 99%

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Forecasting coal resources and reserves in heterogeneous coal zones using 3D facies models (As Pontes Basin, NW Spain)

Falivene

Cabrera

Sáez

2014

International Journal of Coal Geology

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Forecasting coal resources and reserves is critical for coal mine development. Thickness maps are commonly used for assessing coal resources and reserves; however they are limited for capturing coal splitting effects in thick and heterogeneous coal zones. As an alternative, threedimensional geostatistical methods are used to populate facies distribution within a densely sampled heterogeneous coal zone in the As Pontes basin (NW Spain). Coal distribution in this zone is mainly characterized by coal-dominated areas in the central parts of the basin interfingering with terrigenousdominated alluvial fan zones at the basin margins. Resultant models are applied to forecast coal resources and reserves. Predictions using subsets of available well data are also generated to understand performance under limited data constraints. Facies interpolation methods tend to overestimate coal resources and reserves due to interpolation smoothing. Facies simulation methods yield similar resource predictions than conventional thickness map approximations. Reserves predicted by facies simulation methods are mainly influenced by: a) the specific coal proportion threshold used to determine if a block can be recovered or not, and b) the method capability to reproduce areal trends in coal proportions and splitting between coal-dominated and terrigenous-dominated areas of the basin. Reserves predictions differ between the simulation methods, even with dense conditioning datasets. Simulation methods are ranked according to the correlation of their outputs with predictions to the directly interpolated coal proportions: with lowdensity datasets sequential indicator simulation with trends yields the best correlation, with highdensity datasets sequential indicator simulation with post-processing using maximum a posteriori selection yields the best correlation. AbstractForecasting coal resources and reserves is critical for coal mine development. Thickness maps are commonly used for assessing coal resources and reserves; however they are limited for capturing coal splitting effects in thick and heterogeneous coal zones. As an alternative, three-dimensional geostatistical methods are used to populate facies distribution within a densely sampled heterogeneous coal zone in the As Pontes basin (NW Spain). Coal distribution in this zone is mainly characterized by coaldominated areas in the central parts of the basin interfingering with terrigenousdominated alluvial fan zones at the basin margins. Resultant models are applied to forecast coal resources and reserves. Predictions using subsets of available well data are also generated to understand performance under limited data constraints.Facies interpolation methods tend to overestimate coal resources and reserves due to interpolation smoothing. Facies simulation methods yield similar resource predictions than conventional thickness map approximations. Reserves predicted by facies simulation methods are mainly influenced by: a) the specific coal proportion threshold used to determine if a ...

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Section: Coal Resources Reserves and 3d Facies Modelsmentioning

confidence: 99%

Section: Coal Proportions Benchmarkmentioning

confidence: 99%

Forecasting coal resources and reserves in heterogeneous coal zones using 3D facies models (As Pontes Basin, NW Spain)

Falivene

Cabrera

Sáez

2014

International Journal of Coal Geology

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“…CO 2 /CH 4 sorption ratio was reported to be 10:1 for low-rank coal and less than 2:1 for low-and medium-volatile bituminous coals (Deng et al 2015). Variation in gas sorption capacity of coal based on proximate analysis, ultimate analysis and maceral contents has been reported elsewhere (Saikia and Sarkar 2013;Ghosh et al 2014). The sorption isotherm gives estimated retention capacity of gas at in situ temperature and pressure of reservoir.…”

Section: Introductionmentioning

confidence: 98%

Sorption capacity of Indian coal and its variation with rank parameters

Kumar

Mishra

2019

J Petrol Explor Prod Technol

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The study of gas sorption characteristics is important for practical assessment of coal bed methane (CBM) production and CO 2 sequestration in coal seam. Adsorption isotherm is one of the critical parameters for the establishment of production as well as injection well. Adsorption isotherm provides information about the reservoir conditions and critical desorption pressure as well as volume of gas that can be sequestered in deep coal seam. Alteration in sorption isotherm reflects the increase or decrease of the gas production as well as CO 2 sequestration. Therefore, in this paper, experimental investigation was carried out to determine the CO 2 /CH 4 sorption capacity of five different coal samples taken from different locations of Jharia coalfield (Moonidih area) of Gondwana basin. Gas sorption capacity was determined at 27 °C temperature and up to 7.5 MPa pressure. CO 2 adsorption was observed to be higher than that of the CH 4. The sorption ratio of CO 2 /CH 4 varied from 1.6:1 to 1.2:1 for all coal samples. Furthermore, the experimental results were correlated using established Langmuir, Freundlich, Temkin and D-R isotherm models. Experimentally obtained values satisfactorily fitted to the Langmuir and Freundlich model with comparable accuracy. The excess adsorption capacity of coal was also compared with different rank parameters to understand the variation of sorption capacity with rank of coal.

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“…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 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.…”

Section: Introductionmentioning

confidence: 99%