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 郭少斌

FU Juanjuan GUO Shaobin

中国地质大学(北京) 能源学院, 北京 100083

School of Energy Resources, China University of Geosciences, Beijing, 100083, China

沁水盆地煤系地层勘探开发时间长，但测井系列简单，通过全岩分析获取矿物组分的岩心数量十分有限，因此，目前该地区页岩储层的脆性评价以定性为主，缺少定量表征。基于XRD全岩分析的矿物组分信息，通过矿物法计算了沁水盆地海陆交互相页岩岩心的脆性矿物指数，分析了页岩地层的测井响应特征及对脆性矿物指数的影响，采用多元回归方法，建立了该区石炭-二叠系页岩地层矿物指数的计算模型。研究区内石英、长石、菱铁矿及黄铁矿对储层脆性有主要贡献，其含量与页岩脆性指数成正比；而黏土矿物含量较高，有利于提高岩石塑性，不利于裂缝伸展。不同测井响应对脆性指数的反映程度不同，脆性矿物指数与密度值呈正相关，与自然伽马值呈负相关，而与电阻率和声波时差的关系不明显，呈微弱的负相关。多元线性回归方法计算得到的脆性矿物指数与矿物法得到的结果吻合度较好。

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.

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。

https://doi.org/10.11781/sysydz201901108