太行东麓Z区页岩黏土矿物和石英含量预测方法研究
|
摘要
为了做好太行东麓Z区页岩气储层测井评价工作,针对页岩主要矿物成分含量计算需求,本文在文献调研基础上,梳理了页岩主要矿物成分含量的一般计算方法,针对研究区常规测井资料实际开展了测井资料归一化工作,遵循岩心刻度测井的原则开展了岩心矿物含量测试数据与测井曲线敏感性分析,选择敏感测井曲线开展岩心主要矿物含量测井预测模型建模,进而利用所建模型在研究区开展了黏土矿物及石英含量预测.研究表明,页岩矿物含量计算一般包括"三孔隙度"测井、自然伽马能谱测井法、元素俘获能谱测井法等,其计算精度依次提高;煤田测井资料"归一化"预处理可以有效解决测井资料一致性问题;本区山西组自然电位、自然伽马、侧向电阻率对泥页岩黏土矿物含量敏感性好,声波时差、自然电位、侧向电阻率对石英含量敏感性好;基于多元回归分析建立了Z区山西组泥页岩黏土矿物及石英含量计算模型,其计算结果与X-衍射实验分数据对比表明,黏土矿物含量计算结果标准误差小于7.8%,石英含量计算结果误差小于9.6%,证明该计算方法对于研究区黏土矿物及石英含量计算效果明显.此外,研究结果表明,Z区石英含量从研究区西北部向东南部逐渐增加,黏土矿物含量从东南部向西北部增加,局部存在差异.
In order to finish the work of evaluation of shale gas reservoir in the east zone of Taihang, the paper combs the general calculation method of main mineral composition of shale content on the basis of literature research, according to the needing of main mineral content calculating. The work of Logging data normalization is finished, the sensitivity analysis is carried out between the cores mineral content and logging data following the principle of core scale logging. Then, the prediction model is established on selection of sensitive log and multiple regression analysis, the work of prediction on the clay minerals and quartz content is finished by means of the model. The study shows that the calculation of shale mineral content generally includes "three porosity" logging, natural gamma ray spectrometry logging method and element-capture spectral logging method. The calculation precision is improved in turn, the "normalization" pretreatment of the logging data of coalfield can effectively solve the problem of data consistency, the spontaneous potential and Gama ray and lateral resistivity is sensitive to the content of clay minerals in the mud shale, acoustic and the spontaneous potential and lateral resistivity is sensitive to the quartz content. Based on multiple regression analysis to establish the clay minerals and quartz calculation model of mud shale content of Z district Shan-xi formation, the calculation results with the X-ray diffraction experiment score according to the contrast, the clay mineral content calculation standard error less than 7.8%. What's more,the calculated error of quartz content is less than 9.6%. It is proved that the method is effective for the calculation of clay mineral and quartz content in the study area. In addition,the results showed that the quartz content in Z zone increased gradually from the northwest to the southeast of the study area, and the clay mineral content increased from the southeast to the northwest, with local differences.
In order to finish the work of evaluation of shale gas reservoir in the east zone of Taihang, the paper combs the general calculation method of main mineral composition of shale content on the basis of literature research, according to the needing of main mineral content calculating. The work of Logging data normalization is finished, the sensitivity analysis is carried out between the cores mineral content and logging data following the principle of core scale logging. Then, the prediction model is established on selection of sensitive log and multiple regression analysis, the work of prediction on the clay minerals and quartz content is finished by means of the model. The study shows that the calculation of shale mineral content generally includes "three porosity" logging, natural gamma ray spectrometry logging method and element-capture spectral logging method. The calculation precision is improved in turn, the "normalization" pretreatment of the logging data of coalfield can effectively solve the problem of data consistency, the spontaneous potential and Gama ray and lateral resistivity is sensitive to the content of clay minerals in the mud shale, acoustic and the spontaneous potential and lateral resistivity is sensitive to the quartz content. Based on multiple regression analysis to establish the clay minerals and quartz calculation model of mud shale content of Z district Shan-xi formation, the calculation results with the X-ray diffraction experiment score according to the contrast, the clay mineral content calculation standard error less than 7.8%. What's more,the calculated error of quartz content is less than 9.6%. It is proved that the method is effective for the calculation of clay mineral and quartz content in the study area. In addition,the results showed that the quartz content in Z zone increased gradually from the northwest to the southeast of the study area, and the clay mineral content increased from the southeast to the northwest, with local differences.
引文
HAO Jian-fei, ZHOU Can-can, LI Xia, et al. 2012. Summary of shale gas evaluation applying geophysical logging [J]. Progress in geophysics. (in Chinese), 27(4): 1624-1632, doi: 10.6038 /j.issn.1004-2903.2012.04.040.
HOU Jie, ZHOU ChangChun, YANG YuQing. 2012. Well Logging Method in the Analysis of the Mineralogical Composition of Shale Gas Reservoirs [J]. Chinese Journal of Engineering Geophysics (in Chinese), 9(5): 607- 613, doi: 10.3969/j.issn.1672-7940.2012.05.018.
HUANG Qian, LIU JingHua, WANG ZhuWen. 2007. The Application of Natural Gamma-Ray Logging Data in Determining the Content of Clay Minerals [J]. Journal of Jilin University (Earth Science Edition) (in Chinese), 37(s1): 143-146, 150, doi: 10.13278/j.cnki.jjuese.2007.s1.037.
Huang Xin, Hu Shouzhi, Li Shuifu et al. 2016. Forecast and distribution of brittle minerals in shales from the upper section of the third member of Hetaoyuan Formation in the deep sag area of the Biyang Depression [J]. Petroleum Geology & Experiment (in Chinese), 38(1): 48-55, doi: 10.11781/sysydz201601048.
JIANG Yanling, CHEN Liang, HAN Honglin, et al. 2017. Application of Formation Element Logging in the Shale Gas Interpretational Evaluation [J]. Journal of Chongqing University of Science and Technology (Natural Sciences Edition), 19(4): 42- 46, doi: 10.19406/j.cnki.cqkjxyxbzkb.2017.04.011.
LIU Jing-hua, WANG Zhu-wen, Yi Qing-pin. 2010. Determinating Clay Mineral Content by Natural Gamma-Ray Spectral Logging Data [J]. Journal of Jilin University (Earth Science Edition) (in Chinese), 40(1): 215-221, doi: 10.13278/j.cnki.jjuese.2010.01.026.
TAN Mao-jin, Zhang Song-yang. Progress in geophysical logging technology for shale gas researvoirs [J]. Progress in geophysics (in Chinese), 25(6): 2024-2030, doi: 10.3969/j.issn.1004-2903.2010.06.018.
WANG Ru-yue, DING Wen-long, WANG Zhe, et al. 2015. Progress of geophysical well logging in shale gas reservoir evaluation [J]. Progress in geophysics (in Chinese), 30(1): 0228- 0241, doi: 10.6038/pg20150134.
Wang Yan, Feng Minggang, Yan Wei, et al. 2016. A Method of Calculating Percent of Mineral Composition in Gas Reservoir by Apparent Skeleton Density of Clays: A Case of Lower Silurian Longmaxi Gas-bearing Shale Reservoir in Fuling Gas Field, Sichuan Basin [J]. MARINE ORIGIN PETROLEUM GEOLOGY (in Chinese), 21(4): 67-72, doi: 10.3969/j.issn.1672-9854.2016.04.008.
XING Pei-jun, SUN Jian-meng, WANG Ke-wen, et al. 2008. Comparison of determination methods for clay minerals using log data [J]. Journal of China University of Petroleum (EDITION OF NATURAL SCIENCE) (in Chinese), 32(2): 53-57.
YIN Cheng, GAO ShiKui, DONG DaZhong, et al. 2015. Influencing factors the development of shale gas industry [J]. Natural Gas Industry (in Chinese), 35(4): 117-125.
ZHANG JinChuan, JIN ZhiJun, YUAN MingSheng. 2004. Reservoiring mechanism of shale gas and its distribution [J]. Natural Gas Industry (in Chinese), 27(4): 15-18.
ZHANG JinChuan, XUE Hui, ZHANG DeMing, et al. 2003. Shale gas and.its reservoiring mechanism [J]. Geoscience (in Chinese), 17(4): 466.
ZHANG ZuoQing, SUN JianMeng. 2013. Progress of shale gas logging evaluation [J]. Journal of Oil and Gas Technology (in Chinese), 35(3): 90-95.
郝建飞, 周灿灿, 李霞, 等. 2012. 页岩气地球物理测井评价综述[J]. 地球物理学进展, 27(4): 1624-1632, doi: 10.6038/j.issn.1004-2903.2012.04.040.
侯颉, 邹长春, 杨玉卿. 2012. 页岩气储层矿物组分测井分析方法[J]. 工程地球物理学报, 9(5): 607- 613, doi: 10.3969/j.issn.1672-7940.2012.05.018.
黄茜, 刘菁华, 王祝文. 2007. 自然伽马能谱测井资料在确定黏土矿物含量中的应用[J]. 吉林大学学报(地球科学版), 37(s1): 143-146, 150.
黄新, 胡守志, 李水福, 等. 2016. 泌阳凹陷深凹区核三上段页岩层脆性矿物预测及分布[J]. 石油实验地质, 38(1): 48-55, doi: 10.11781/sysydz201601048.
姜艳玲, 陈亮, 韩红林, 等. 2017. 元素测井在页岩气解释评价中的应用[J]. 重庆科技学院学报(自然科学版), 19(4): 42- 46.
刘菁华, 王祝文, 易清平. 2010. 利用自然γ能谱测井资料确定黏土矿物的含量及其应用[J]. 吉林大学学报(地球科学版), 40(1): 215-221.
谭茂金, 张松扬. 2010. 页岩气储层地球物理测井研究进展[J]. 地球物理学进展, 25(6): 2024-2030, doi: 10.3969/j.issn.1004-2903.2010.06.018.
王濡岳, 丁文龙, 王哲, 等. 2015. 页岩气储层地球物理测井评价研究现状[J]. 地球物理学进展, 30(1): 0228- 0241, doi: 10.6038/pg20150134.
王燕, 冯明刚, 严伟, 等. 2016. 计算页岩气储层矿物成分含量的新方法: 黏土视骨架密度法—以涪陵页岩气田为例[J]. 海相油气地质, 21(4): 67-72, 10.3969/j.issn.1672-9854.2016.04.008.
邢培俊, 孙建孟, 王克文, 等. 2008. 利用测井资料确定黏土矿物的方法对比[J]. 中国石油大学学报(自然科学版), 32(2): 53-57.
殷诚, 高世葵, 董大忠, 等. 2015. 页岩气产业发展的影响因素[J]. 天然气工业, 35(4): 117-125.
张金川, 金之钧, 袁明生. 2004. 页岩气成藏机理和分布[J]. 天然气工业, 27(4): 15-18.
张金川, 薛会, 张德明, 等. 2003. 页岩气及其成藏机理[J]. 现代地质, 17(4): 466.
张作清, 孙建孟. 2013. 页岩气测井评价进展[J]. 石油天然气学报(江汉石油学院学报), 35(3): 90-95.
HOU Jie, ZHOU ChangChun, YANG YuQing. 2012. Well Logging Method in the Analysis of the Mineralogical Composition of Shale Gas Reservoirs [J]. Chinese Journal of Engineering Geophysics (in Chinese), 9(5): 607- 613, doi: 10.3969/j.issn.1672-7940.2012.05.018.
HUANG Qian, LIU JingHua, WANG ZhuWen. 2007. The Application of Natural Gamma-Ray Logging Data in Determining the Content of Clay Minerals [J]. Journal of Jilin University (Earth Science Edition) (in Chinese), 37(s1): 143-146, 150, doi: 10.13278/j.cnki.jjuese.2007.s1.037.
Huang Xin, Hu Shouzhi, Li Shuifu et al. 2016. Forecast and distribution of brittle minerals in shales from the upper section of the third member of Hetaoyuan Formation in the deep sag area of the Biyang Depression [J]. Petroleum Geology & Experiment (in Chinese), 38(1): 48-55, doi: 10.11781/sysydz201601048.
JIANG Yanling, CHEN Liang, HAN Honglin, et al. 2017. Application of Formation Element Logging in the Shale Gas Interpretational Evaluation [J]. Journal of Chongqing University of Science and Technology (Natural Sciences Edition), 19(4): 42- 46, doi: 10.19406/j.cnki.cqkjxyxbzkb.2017.04.011.
LIU Jing-hua, WANG Zhu-wen, Yi Qing-pin. 2010. Determinating Clay Mineral Content by Natural Gamma-Ray Spectral Logging Data [J]. Journal of Jilin University (Earth Science Edition) (in Chinese), 40(1): 215-221, doi: 10.13278/j.cnki.jjuese.2010.01.026.
TAN Mao-jin, Zhang Song-yang. Progress in geophysical logging technology for shale gas researvoirs [J]. Progress in geophysics (in Chinese), 25(6): 2024-2030, doi: 10.3969/j.issn.1004-2903.2010.06.018.
WANG Ru-yue, DING Wen-long, WANG Zhe, et al. 2015. Progress of geophysical well logging in shale gas reservoir evaluation [J]. Progress in geophysics (in Chinese), 30(1): 0228- 0241, doi: 10.6038/pg20150134.
Wang Yan, Feng Minggang, Yan Wei, et al. 2016. A Method of Calculating Percent of Mineral Composition in Gas Reservoir by Apparent Skeleton Density of Clays: A Case of Lower Silurian Longmaxi Gas-bearing Shale Reservoir in Fuling Gas Field, Sichuan Basin [J]. MARINE ORIGIN PETROLEUM GEOLOGY (in Chinese), 21(4): 67-72, doi: 10.3969/j.issn.1672-9854.2016.04.008.
XING Pei-jun, SUN Jian-meng, WANG Ke-wen, et al. 2008. Comparison of determination methods for clay minerals using log data [J]. Journal of China University of Petroleum (EDITION OF NATURAL SCIENCE) (in Chinese), 32(2): 53-57.
YIN Cheng, GAO ShiKui, DONG DaZhong, et al. 2015. Influencing factors the development of shale gas industry [J]. Natural Gas Industry (in Chinese), 35(4): 117-125.
ZHANG JinChuan, JIN ZhiJun, YUAN MingSheng. 2004. Reservoiring mechanism of shale gas and its distribution [J]. Natural Gas Industry (in Chinese), 27(4): 15-18.
ZHANG JinChuan, XUE Hui, ZHANG DeMing, et al. 2003. Shale gas and.its reservoiring mechanism [J]. Geoscience (in Chinese), 17(4): 466.
ZHANG ZuoQing, SUN JianMeng. 2013. Progress of shale gas logging evaluation [J]. Journal of Oil and Gas Technology (in Chinese), 35(3): 90-95.
郝建飞, 周灿灿, 李霞, 等. 2012. 页岩气地球物理测井评价综述[J]. 地球物理学进展, 27(4): 1624-1632, doi: 10.6038/j.issn.1004-2903.2012.04.040.
侯颉, 邹长春, 杨玉卿. 2012. 页岩气储层矿物组分测井分析方法[J]. 工程地球物理学报, 9(5): 607- 613, doi: 10.3969/j.issn.1672-7940.2012.05.018.
黄茜, 刘菁华, 王祝文. 2007. 自然伽马能谱测井资料在确定黏土矿物含量中的应用[J]. 吉林大学学报(地球科学版), 37(s1): 143-146, 150.
黄新, 胡守志, 李水福, 等. 2016. 泌阳凹陷深凹区核三上段页岩层脆性矿物预测及分布[J]. 石油实验地质, 38(1): 48-55, doi: 10.11781/sysydz201601048.
姜艳玲, 陈亮, 韩红林, 等. 2017. 元素测井在页岩气解释评价中的应用[J]. 重庆科技学院学报(自然科学版), 19(4): 42- 46.
刘菁华, 王祝文, 易清平. 2010. 利用自然γ能谱测井资料确定黏土矿物的含量及其应用[J]. 吉林大学学报(地球科学版), 40(1): 215-221.
谭茂金, 张松扬. 2010. 页岩气储层地球物理测井研究进展[J]. 地球物理学进展, 25(6): 2024-2030, doi: 10.3969/j.issn.1004-2903.2010.06.018.
王濡岳, 丁文龙, 王哲, 等. 2015. 页岩气储层地球物理测井评价研究现状[J]. 地球物理学进展, 30(1): 0228- 0241, doi: 10.6038/pg20150134.
王燕, 冯明刚, 严伟, 等. 2016. 计算页岩气储层矿物成分含量的新方法: 黏土视骨架密度法—以涪陵页岩气田为例[J]. 海相油气地质, 21(4): 67-72, 10.3969/j.issn.1672-9854.2016.04.008.
邢培俊, 孙建孟, 王克文, 等. 2008. 利用测井资料确定黏土矿物的方法对比[J]. 中国石油大学学报(自然科学版), 32(2): 53-57.
殷诚, 高世葵, 董大忠, 等. 2015. 页岩气产业发展的影响因素[J]. 天然气工业, 35(4): 117-125.
张金川, 金之钧, 袁明生. 2004. 页岩气成藏机理和分布[J]. 天然气工业, 27(4): 15-18.
张金川, 薛会, 张德明, 等. 2003. 页岩气及其成藏机理[J]. 现代地质, 17(4): 466.
张作清, 孙建孟. 2013. 页岩气测井评价进展[J]. 石油天然气学报(江汉石油学院学报), 35(3): 90-95.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |