胡亚斐, 张遂安, 吴峙颖. 基于地质多元统计分析的煤层气含量建模方法——以沁水盆地南部某区块3号煤层为例[J]. 煤田地质与勘探, 2013, 41(2): 33-36. DOI: 10.3969/j.issn.1001-1986.2013.02.008
引用本文: 胡亚斐, 张遂安, 吴峙颖. 基于地质多元统计分析的煤层气含量建模方法——以沁水盆地南部某区块3号煤层为例[J]. 煤田地质与勘探, 2013, 41(2): 33-36. DOI: 10.3969/j.issn.1001-1986.2013.02.008
HU Yafei, ZHANG Suian, WU Zhiying. The gas content modeling method based on geological statistical analysis: with seam No.3 in southern Qinshui basin as an example[J]. COAL GEOLOGY & EXPLORATION, 2013, 41(2): 33-36. DOI: 10.3969/j.issn.1001-1986.2013.02.008
Citation: HU Yafei, ZHANG Suian, WU Zhiying. The gas content modeling method based on geological statistical analysis: with seam No.3 in southern Qinshui basin as an example[J]. COAL GEOLOGY & EXPLORATION, 2013, 41(2): 33-36. DOI: 10.3969/j.issn.1001-1986.2013.02.008

基于地质多元统计分析的煤层气含量建模方法——以沁水盆地南部某区块3号煤层为例

The gas content modeling method based on geological statistical analysis: with seam No.3 in southern Qinshui basin as an example

  • 摘要: 煤层的气含量是煤层气评价选区中的重要指标,同时也是进行煤层气储量计算的重要参数。通过剖析三维地质建模原理和煤层气含量的制约因素,并以山西沁水盆地南部煤层气开发示范区为例,使用地质多元统计分析方法,建立了煤层气含量的预测模型。通过与气含量的实验数据对比表明,该方法的预测精度较高,误差均在10%以内,其中多元线性回归法的误差为4.50%,效果最好,具有实际意义。实例分析表明,选取适当的气含量制约因素,利用多元统计分析方法建立煤层气含量预测模型,对煤层气三维地质建模乃至煤层气开发都具有指导作用。

     

    Abstract: Coal bed gas content is an important index in the evaluation and regional election of coal bed gas, and it is also an important parameter of coal bed gas reserves calculation. This paper analyzes 3D geological modeling principle and controlling factors of coal bed gas content. In the southern Qinshui basin, a gas content prediction modeling method was established based on the geologic multivariate statistical analysis. The comparison of the gas content experimental data shows that the prediction accuracy is higher., the errors are within 10%, the error of multiple linear regression method is 4.5%, it is the best method. This method has practical significance. The example shows that selecting appropriate controlling factors of CBM and using the multivariate statistical analysis method to establish coal bed gas prediction model have guiding roles for coal bed 3D geologic modeling and CBM exploitation.

     

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