Uncertainty assessment of coal tonnage by spatial modeling of seam distribution and coal quality

Heriawan, Mohamad Nur; Koike, Katsuaki

doi:10.1016/j.coal.2008.07.014

Cited by 32 publications

(12 citation statements)

References 7 publications

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“…However, interpolations provide a representation of facies distribution that can be used to improve the qualitative understanding of the broad spatial distribution on the coal zone (Falivene et al, 2007b;Heriawan and Koike 2008b; Figure 9). Deutsch and Wilde (2013) recently proposed a novel cell-based facies interpolation approach for modelling coal splitting with the potential for respecting coal proportions.…”

Section: Interpolation Methods Smoothing Coal Resources and Reservesmentioning

confidence: 99%

“…These result from the variability of sedimentary environments in which coal is generated and accumulated (Hacquebard, 1993;Hower et al, 1994;Thomas, 2003). In these cases reserves can be significantly reduced because thin coal beds cannot be economically mined (Heriawan and Koike, 2008b). The threedimensional effects of multiple coal splitting in heterogeneous coal zones are difficult to properly capture and quantify using maps ( Figure 1A).…”

Section: Coal Resources Reserves and 3d Facies Modelsmentioning

confidence: 99%

“…These have been widely used to reconstruct or simulate facies distributions in the subsurface, mostly in relation to hydrocarbon reservoirs or aquifers (for example: Johnson and Dreisss, 1989;Langlais and Doyle, 1993;Gotway and Rutherford, 1994;de Marsily et al, 1998 andLee et al, 2007). However, examples of their application to coal zones are limited (Falivene et al, 2007b;Heriawan and Koike, 2008b;Deutsch and Wilde, 2013). …”

Section: Coal Resources Reserves and 3d Facies Modelsmentioning

confidence: 99%

“…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: Interpolation Methods Smoothing Coal Resources and Reservesmentioning

confidence: 99%

Section: Coal Resources Reserves and 3d Facies Modelsmentioning

confidence: 99%

Section: Coal Resources Reserves and 3d Facies Modelsmentioning

confidence: 99%

Section: Coal Resources Reserves and 3d Facies Modelsmentioning

confidence: 99%

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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Applying spatial analysis techniques to assess the suitability of multipurpose uses of spring water in the Jiaosi Hot Spring Region, Taiwan

Jang

Huang

2017

Environ Monit Assess

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The Jiaosi Hot Spring Region is one of the most famous tourism destinations in Taiwan. The spring water is processed for various uses, including irrigation, aquaculture, swimming, bathing, foot spas, and recreational tourism. Moreover, the multipurpose uses of spring water can be dictated by the temperature of the water. To evaluate the suitability of spring water for these various uses, this study spatially characterized the spring water temperatures of the Jiaosi Hot Spring Region by integrating ordinary kriging (OK), sequential Gaussian simulation (SGS), and Geographic information system (GIS). First, variogram analyses were used to determine the spatial variability of spring water temperatures. Next, OK and SGS were adopted to model the spatial uncertainty and distributions of the spring water temperatures. Finally, the land use (i.e., agriculture, dwelling, public land, and recreation) was determined using GIS and combined with the estimated distributions of the spring water temperatures. A suitable development strategy for the multipurpose uses of spring water is proposed according to the integration of the land use and spring water temperatures. The study results indicate that the integration of OK, SGS, and GIS is capable of characterizing spring water temperatures and the suitability of multipurpose uses of spring water. SGS realizations are more robust than OK estimates for characterizing spring water temperatures compared to observed data. Furthermore, current land use is almost ideal in the Jiaosi Hot Spring Region according to the estimated spatial pattern of spring water temperatures.

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Geostatistical Modeling of Ore Grade Distribution from Geomorphic Characterization in a Laterite Nickel Deposit

Ilyas

Koike

2012

Nat Resour Res

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Uncertainty assessment of coal tonnage by spatial modeling of seam distribution and coal quality

Cited by 32 publications

References 7 publications

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

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

Applying spatial analysis techniques to assess the suitability of multipurpose uses of spring water in the Jiaosi Hot Spring Region, Taiwan

Geostatistical Modeling of Ore Grade Distribution from Geomorphic Characterization in a Laterite Nickel Deposit

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