煤田地质与勘探2017，Vol.45Issue(1)：45-50,55,7.DOI:10.3969/j.issn.1001-1986.2017.01.009
基于统计学方法的页岩孔容预测
Shale pore volume prediction based on statistical method
摘要
Abstract
The reservoir space of shale gas is closely related to the characteristics of the mineral characteristics of res-ervoir.The paper, taking the shale in Longmaxi Formation in the eastern margin of Sichuan basin as example, a series of tests were conducted. Based on the results of mineral composition, microelement analysis, and geochemical pa-rameters, as well as the low temperature nitrogen adsorption and high resolution images, a pore volume prediction equation was formulated through multivariate statistical method. Then the pore diameter distribution and its affecting factors were explored with this new model. It is showed that in Longmaxi shale formation the mineral composition has big difference in the middle and the bottom, and the abundant biogenic quartz is the main reason for the high quartz content in the bottom of Longmaxi Formation. The nano pores in shale mainly rank 2~5 nm, and contributes about 64.2%~70.1% of the total pore volume. The formulated equation indicates that pore volume has a strong correlation with shale mineral composition,and standardization of the regression coefficient shows that organic matter, clay min-erals and brittle minerals have a diminishing effects on pore volumes. Brittle mineral pores, inter-granular pores in clay pieces and intra-granular pores in clay minerals are the main pore types in clay-rich shale, and silt pores is the most common, usually larger than 2nm in diameter. The pore volume of organic pores is mainly controlled by the value of TOC, and the surface porosity which is primarily contributed by 2~5 nm pores is 8.8%~12.5%.The TOC value and thermal maturity of shale are the primary controlling factors for the pores smaller than 2nm in diameter, while the development of pores larger than 50 nm is mainly controlled by clay minerals, quartz and feldspar.关键词
页岩/页岩组分/孔隙结构/孔容预测Key words
shale/shale composition/pore structure/pore volume分类
天文与地球科学引用本文复制引用
游利军,刘雄辉,康毅力,陈明君,陈强,杨斌..基于统计学方法的页岩孔容预测[J].煤田地质与勘探,2017,45(1):45-50,55,7.基金项目
国家自然科学基金项目(51674209) （51674209）
国家科技重大专项课题(2011ZX05018-005) National Natural Science Foundation of China(51674209) （2011ZX05018-005）
National Science and Technology Major Project(2011ZX-05018-005) （2011ZX-05018-005）
