中文摘要：

煤储层渗透率是影响瓦斯抽采与煤层气开发的重要参数,快速评价不同结构煤体的渗透率对指导现场工作有重要意义。以寺家庄井田为例,为了优选出表征煤储层渗透率的合适方法,通过裂隙分形维数、声波速度和地质强度指标（GSI）对煤体结构进行定量表征,测试其渗透率,依据表征的难易程度和二者拟合系数进行对比。结果表明：裂隙分形维数与渗透率相关性显著,但是操作较为繁琐且不适用于软煤;声波速度法操作简单,但与渗透率关系不明显;地质强度指标与渗透率具有显著的相关性,操作简单,适用于所有煤体结构。因此,寺家庄井田煤储层渗透率表征的最佳方法为地质强度指标法。

英文摘要：

This paper is aimed at investigating and optimizing a method developed by us to characterize the permeability of coal min- ing fields in a quantitative approach by taking Sijiazhuang as the case study sample. Further comparative analysis has been done on the ba- sis of the difficulty grades of characterization and their fitting coeffi- cients according to the quantitative analysis results via the fractural dimension, acoustic velocity and geo-strength indexes（GIS） respec- tively and then via the experimental tests of the coal permeability. As is known, the permeability of the coal reservoir is a critical parameter that may account for the gas drainage and coal-seam methane growth. Therefore, it is of great positive instructive significance for rapid ef- fective evaluation of the permeability of various coals in view of the field working mechanism. The ,results of our investigation show that the fractal dimension bears a significant relevance to the permeability of the coal reservoir in which the correlation coefficients on the verti- cal and horizontal stratification have been found equal to 0. 684 2 and 0.858 respectively. However, the operations of the grid division and the fracture situation tend to be highly complicated. Besides, such operational procedures may not be suitable for the soft coal seams（ i. e. the granulitic and mylonitic coal）. On the other hand, though the acoustic velocity control method seems much easier in operation, it cannot be said well suitable for dealing with the problem of coal per- meability, which makes it unavailable for anticipating and evaluating the permeability of the coal reservoir desirably. It is just for such rea- sons that we have introduced the geological strength index（GSI） as a quantitative model for the given purpose because its fitting results with permeabihty representation help to make it reahstic to detect the regu- lar, normal-like distribution as the correlation coefficient, which is found as high as by 0.84 with the GIS peak value being 52.7. This G