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Electrofacies analysis for coal lithotype profiling based on high resolution wireline log data

机译:基于高分辨率电缆测井数据的煤岩型剖面电相分析

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The traditional approach to coal lithotype analysis is based on a visual characterisation of coal in core, mine or outcrop exposures. As not all wells are fully cored, the petroleum and coal mining industries increasingly use geophysical wireline logs for lithology interpretation.This study demonstrates a method for interpreting coal lithotypes from geophysical wireline logs, and in particular discriminating between bright or banded, and dull coal at similar densities to a decimetre level. The study explores the optimum combination of geophysical log suites for training the coal electrofacies interpretation, using neural network conception, and then propagating the results to wells with fewer wireline data. This approach is objective and has a recordable reproducibility and rule set.In addition to conventional gamma ray and density logs, laterolog resistivity, microresistivity and PEF data were used in the study. Array resistivity data from a compact micro imager (CMI tool) were processed into a single microresistivity curve and integrated with the conventional resistivity data in the cluster analysis. Microresistivity data were tested in the analysis to test the hypothesis that the improved vertical resolution of microresistivity curve can enhance the accuracy of the clustering analysis. The addition of PEF log allowed discrimination between low density bright to banded coal electrofacies and low density inertinite-rich dull electrofacies.The results of clustering analysis were validated statistically and the results of the electrofacies results were compared to manually derived coal lithotype logs. (C) 2016 Elsevier Ltd. All rights reserved.
机译:煤岩性分析的传统方法是基于岩心,矿井或露头暴露中煤的视觉特征。由于并非所有井都完全取芯,因此石油和煤炭开采行业越来越多地使用地球物理缆线测井仪进行岩性解释。本研究表明了一种从地球物理缆线测井仪解释煤岩性的方法,尤其是区分亮煤和带状煤与钝性煤。与分米相似的密度。该研究探索了使用神经网络概念来训练煤电相解释的地球物理测井套件的最佳组合,然后将结果传播到有线数据较少的井中。该方法是客观的,并且具有可记录的可重复性和规则集。除了常规的伽马射线和密度测井以外,研究中还使用了测井电阻率,微电阻率和PEF数据。将来自紧凑型微型成像仪(CMI工具)的阵列电阻率数据处理为单个微电阻率曲线,并在聚类分析中将其与常规电阻率数据集成在一起。在分析中测试了微电阻率数据,以检验以下假设:改进的微电阻率曲线的垂直分辨率可以提高聚类分析的准确性。 PEF log的添加可以区分低密度的亮带状煤电带状煤相和低密度的富含惰质石的钝性电相。对聚类分析的结果进行统计验证,并将电相的结果与人工获得的煤岩性测井进行比较。 (C)2016 Elsevier Ltd.保留所有权利。

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