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

机译:通过煤层分布和煤质空间模拟确定煤吨位的不确定度

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摘要

Coal reserve estimation requires comprehensive spatial analysis of geologic and geochemical data. For determination of coal reserve uncertainty associated with tonnage and grade (quality), we present a new approach that stands on spatial modeling of seam distribution and coal quality. A multilayer coal deposit in East Kalimantan, Indonesia was selected as a case study site. Coal quality was evaluated by ash and sodium contents, total sulphur, and heating value. Their spatial models were produced by geostatistical methods, ordinary kriging and sequential Gaussian simulation, which were effective to evaluate local or global uncertainty by honoring spatial correlation structures. The seam distribution was modeled by a binary transformation of the geologic data and the three-dimensional optimization method. It is demonstrated that the lower seam sequence has the highest degree of uncertainty because of low average coal accumulation (tonnage) and the sparseness of sample data points in the north part of the study area. This study demonstrates that uncertainty of coal accumulation is related to the seam distribution and that spatial variability patterns of coal qualities are opposing between ash content and total sulphur: this difference might have been caused by geological controls.
机译:煤炭储量估算需要对地质和地球化学数据进行全面的空间分析。为了确定与吨位和品位(质量)相关的煤炭储量不确定性,我们提出了一种基于煤层分布和煤质空间模型的新方法。选择了印度尼西亚东加里曼丹省的多层煤矿床作为案例研究地点。通过灰分和钠含量,总硫和热值评估煤炭质量。它们的空间模型是通过地统计学方法,普通克里金法和顺序高斯模拟生成的,它们通过尊重空间相关性结构可以有效地评估局部或全局不确定性。通过地质数据的二进制转换和三维优化方法对煤层分布进行建模。结果表明,由于研究区北部平均煤蓄积(吨数)低和样本数据点稀疏,下煤层序列具有最高的不确定性。这项研究表明,煤积累的不确定性与煤层分布有关,并且煤质的空间变异性在灰分含量和总硫含量之间是相反的:这种差异可能是由地质控制引起的。

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