Seam modeling and resource estimation in the coalfields of western Anatolia

Tercan, Adnan; Ünver, Bahtiyar; Hindistan, Mehmet Ali; Ertunç, Güneş; Atalay, F. Betul; Ünal, Suphi; Kıllıoğlu, Yasin

doi:10.1016/j.coal.2012.10.006

Cited by 36 publications

(15 citation statements)

References 12 publications

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“…There are several studies from the Soma Basin that mainly focused on geological setting, palaeopalynological, as well as environmental and economic aspects (Nebert, 1978;Akgün et al, 1986;Gemici et al, 1991;Takahashi and Jux, 1991;Akgün, 1993;İnci, 1998a, b, 2002Karayiğit et al, 2000Karayiğit et al, , 2006Vassilev et al, 2005;Tercan et al, 2013;Hokerek and Ozcelik, 2015;Baysal et al, 2016;Kayseri-Özer, 2016). Coal-petrographical and geochemical studies are limited (Karayiğit and Whateley, 1997;Karayiğit, 1998;Tuncalı et al, 2002;Bulut and Karayiğit, 2006;Toprak, 2009).…”

Section: Introductionmentioning

confidence: 99%

The Miocene coal seams in the Soma Basin (W. Turkey): Insights from coal petrography, mineralogy and geochemistry

Karayi̇ǧi̇t

Littke

Querol

et al. 2017

International Journal of Coal Geology

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The Soma Basin hosts three coal seams (kM2, kM3 and kP1) and several altered tuff layers within the Miocene sequences. This study focuses on the determination of coal petrography, Rock-Eval pyrolysis, mineralogy and elemental contents of the three coal seams from Eynez, Işıklar and Deniş sectors in the basin, and to identify the mineralogy of altered tuff layers within * Corresponding author, **visiting scholar

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Section: Introductionmentioning

confidence: 99%

The Miocene coal seams in the Soma Basin (W. Turkey): Insights from coal petrography, mineralogy and geochemistry

Karayi̇ǧi̇t

Littke

Querol

et al. 2017

International Journal of Coal Geology

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“…Approaches to achieve this transition range from one-dimensional mineable coal compositing of drill-hole data to more effective three-dimensional aggregation of mineable coal seams based on an appropriate stratigraphic model. Coal and lignite resource modelling has been covered in a number of studies (Tercan and Karayigit, 2001;Koike, 2008a, 2008b;Kapageridis and Kolovos, 2009;Olea et al, 2011 ;Hatton and Fardell, 2012;Roumpos, Liakoura, andBarmpas, 2011, 2014;Tercan, Ünver, and Hindistan, 2011;Deutsch and Wilde, 2013;Tercan et al, 2013) Thin-layered lignite deposits (known as Zebra deposits) are the main source of fuel for the production of electrical power in Greece (Figure 1). Mainly located in the northwest region of the country, in the Amyntaio-Ptolemais basin, Greek lignite deposits belong to the upper Pliocene.…”

Section: The Modelling Problemmentioning

confidence: 99%

A comparative study of lignite resource estimation based on 1D drill-hole mineable lignite compositing of uncorrelated seams and 3D mineable lignite aggregation of correlated seams

Kapageridis¹,

Iordanidis²

2019

J. S. Afr. Inst. Min. Metall.

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The majority of lignite deposits in Greece consist of multiple thin lignite layers and are traditionally estimated using a one-dimensional compositing approach that can potentially lead to large errors, particularly in the presence of medium to severe tectonic disturbance and uneven vertical distribution of the seams. Drill-holes are evaluated using mining and processing criteria leading to a number of mineable lignite 'packages' along each hole, the sum of which is reported as the total mineable lignite at each drillhole horizontal location. The total mineable lignite thickness values from the various drill-holes and associated weighted average qualities are interpolated horizontally, leading to a two-dimensional model of mineable lignite. A more advanced version of this one-dimensional approach has been applied in the past, with improved results. In this version, the one-dimensional approach was limited to a single mine bench and repeated separately for each bench, thus reducing the scale of potential errors and better approaching the vertical distribution of mineable lignite. Lignite deposits, such as the one examined in this paper, require the development of a thorough stratigraphic model to allow the reporting of accurate lignite resources and to form a solid basis for mine planning and the calculation of lignite reserves. The evaluation of mineable lignite using mining and processing criteria can then be applied to correlated and modelled lignite seams, leading to an overall three-dimensional model of the deposit that allows accurate calculation of lignite resources even in the presence of deformation. This paper presents all three modelling approaches through a case study based on part of a real lignite deposit. The effects of using each of the approaches are analysed and the benefits of the three-dimensional approach are clearly demonstrated.

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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%

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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Seam modeling and resource estimation in the coalfields of western Anatolia

Cited by 36 publications

References 12 publications

The Miocene coal seams in the Soma Basin (W. Turkey): Insights from coal petrography, mineralogy and geochemistry

The Miocene coal seams in the Soma Basin (W. Turkey): Insights from coal petrography, mineralogy and geochemistry

A comparative study of lignite resource estimation based on 1D drill-hole mineable lignite compositing of uncorrelated seams and 3D mineable lignite aggregation of correlated seams

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

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