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沁水盆地海陆交互相页岩脆性指数预测与测井响应分析

付娟娟 郭少斌

石油实验地质2019,Vol.41Issue(1):108-112,5.
石油实验地质2019,Vol.41Issue(1):108-112,5.DOI:10.11781/sysydz201901108

沁水盆地海陆交互相页岩脆性指数预测与测井响应分析

Prediction of brittle index and its relationship with log data in marine-terrigenous shale of Qinshui Basin

付娟娟 1郭少斌1

作者信息

  • 1. 中国地质大学(北京) 能源学院, 北京 100083
  • 折叠

摘要

Abstract

Rock brittleness, as a key factor affecting the fracture properties of shale, is one of most important parameters for shale gas reservoir evaluation and "sweet spot" prediction. At present, the brittleness evaluation of shale reservoirs in the Qinshui Basin is mostly based on qualitative analysis because of its simple well logging series and limited number of cores for XRD based mineral composition. In this paper, a brittleness index (BI) was firstly calculated based on mineral composition information from XRD data. Then the relationship between logging data and BI was analyzed. Finally, a model for calculating the BI of the Carboniferous and Permian shale is established. The results show that the brittleness in the studied area is related to quartz, dolomite, pyrite and clays. The contents of quartz, dolomite and pyrite have a positive correlation with BI, whereas the clay minerals, which increase the ductility of rocks, are negatively correlated with BI. Different logging responses reflect different degrees of BI. The BI is positively related to the density, whereas it is negatively correlated with natural gamma signal. The relationship of BI with resistivity and interval transit time is not strong, showing a weakly negative correlation. The BI calculated using a multiple linear regression method is in a good agreement with that obtained by the mineral method.

关键词

脆性指数/脆性评价/矿物组成/页岩/海陆交互相/沁水盆地

Key words

brittle index/brittleness evaluation/mineral composition/shale/marine-terrigenous facies/Qinshui Basin

分类

能源科技

引用本文复制引用

付娟娟,郭少斌..沁水盆地海陆交互相页岩脆性指数预测与测井响应分析[J].石油实验地质,2019,41(1):108-112,5.

基金项目

国家科技重大专项"不同类型页岩生成机理与富集规律研究"(2016ZX05034-001)资助 (2016ZX05034-001)

石油实验地质

OA北大核心CSCDCSTPCD

1001-6112

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