The combination of sampling and kriging in the regional estimation of coal resources

Starks, T. H.; Behrens, N. A.; Fang, J. H.

doi:10.1007/bf01083946

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…Many efforts have been devoted to the assessment of coal resources through 2D interpolations, either by using thickness maps (Starks et al, 1982;Journel and Rossi, 1989;Schuenemeyer and Power, 2000;Hohn and McDowell 2001;Tercan and Karyigit, 2001) or percentage maps showing distribution of coal quality properties (Davis and Greenes, 1983;Bancroft and Hobbs, 1986;Watson et al, 2001;Turner and Richardson, 2004). Such approaches provide useful predictions when dealing with homogeneous coal seams, as well as for the general assessment of reserves.…”

Section: Problem Statementmentioning

confidence: 99%

Optimum and robust 3D facies interpolation strategies in a heterogeneous coal zone (Tertiary As Pontes basin, NW Spain)

Falivene

Cabrera

Sáez

2007

International Journal of Coal Geology

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Abstract:Coal exploration and mining in extensively drilled and sampled coal seams can benefit from 3D statistical facies interpolation. Starting from closely spaced core descriptions, and using interpolation methods, a 3D optimum and robust facies distribution model was obtained for a thick, heterogeneous coal seam zone deposited in the non-marine As Pontes basin (Oligocene-Early Miocene, NW Spain). Several grid layering styles, interpolation methods (truncated inverse squared distance weighting, truncated kriging, truncated kriging with an areal trend, indicator inverse squared distance weighting, indicator kriging, and indicator kriging with an areal trend) and searching conditions were experimented and the results compared. Facies interpolation strategies were evaluated using visual comparison and cross validation. Moreover, robustness of the resultant facies distribution with respect to variations in interpolation method input parameters was verified by taking into account several scenarios of uncertainty. The resultant 3D facies reconstruction improves the understanding of areal distribution and geometry of the coal facies. Furthermore, since some coal quality properties (e.g. calorific value or sulphur percentage) display a good statistical correspondence with facies, predicting the distribution of these properties using the Abstract:Coal exploration and mining in extensively drilled and sampled coal seams can benefit from 3D statistical facies interpolation. Starting from closely spaced core descriptions, and using interpolation methods, a 3D optimum and robust facies 2 correspondence with facies, predicting the distribution of these properties using the reconstructed facies distribution as a template proved to be a powerful approach, yielding more accurate and realistic reconstructions of these properties in the coal seam zone.

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Section: Problem Statementmentioning

confidence: 99%

Optimum and robust 3D facies interpolation strategies in a heterogeneous coal zone (Tertiary As Pontes basin, NW Spain)

Falivene

Cabrera

Sáez

2007

International Journal of Coal Geology

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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%

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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Stochastic Simulation of Coal Bed Thickness and Economic Decision-Making

Hohn

McDowell

2001

Geologic Modeling and Simulation

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The combination of sampling and kriging in the regional estimation of coal resources

Cited by 6 publications

References 1 publication

Optimum and robust 3D facies interpolation strategies in a heterogeneous coal zone (Tertiary As Pontes basin, NW Spain)

Optimum and robust 3D facies interpolation strategies in a heterogeneous coal zone (Tertiary As Pontes basin, NW Spain)

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

Stochastic Simulation of Coal Bed Thickness and Economic Decision-Making

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