Volume 44 Issue 5
Oct.  2021
Turn off MathJax
Article Contents
Abstract
Related Articles
LUO Bing, XIE Xiaoguo. Quantitative evaluation of reservoir heterogeneity of coal seam by using logging data[J]. COAL GEOLOGY & EXPLORATION, 2016, 44(5): 155-159. DOI: 10.3969/j.issn.1001-1986.2016.05.030
Citation: LUO Bing, XIE Xiaoguo. Quantitative evaluation of reservoir heterogeneity of coal seam by using logging data[J]. COAL GEOLOGY & EXPLORATION, 2016, 44(5): 155-159. DOI: 10.3969/j.issn.1001-1986.2016.05.030
shu

Quantitative evaluation of reservoir heterogeneity of coal seam by using logging data

  • 1. Sichuan Coal Geological Bureau, Chengdu 610072, China;

  • 2. Geophysical Institute of Chengdu University of Technology, Chengdu 610071, China

More Information
  • Received Date: September 21, 2015
  • Available Online: October 22, 2021
    Graphical Abstract
    • Abstract
  • Aiming at the big influence of heterogeneity on CBM extraction, through analyzing the permeability distribution law by using logging data, the planar and vertical heterogeneous degree of coal seam can be evaluated quantitatively. Darcy formula was used to determine the permeability of coal matrix, dual lateral logging data were used to estimate the permeability of fissures, the cumulative distribution curve corresponding to the contribution rate of seam permeability under the ordinal percentage coordinate was drawn, the slope of the cumulative distribution curve after transformation of definition coordinate is the coefficient of heyerogeneous degree. The actual application shows the coefficient of heterogeneous degree can reflect the horizontal and vertical difference of the phsical properties of coal seam, and then the hetrogeneity of seam can be evaluated quantitatively.
    • FullText(HTML)
    • References (0)
    • Related Articles

  • Related Articles

    [1]ZHANG Jingshuo, NI Xiaoming, LI Yang, LI Zongyuan, CAO Yunxing. Quantitative evaluation of the contamination degree of the drilling fluid for fractures of different scale in coal[J]. COAL GEOLOGY & EXPLORATION, 2020, 48(5): 64-72. DOI: 10.3969/j.issn.1001-1986.2020.05.008
    [2]WANG Qing, YANG Fei, GONG Weicheng, XU Tianxin, LI Yichao. Influencing factors of the dynamic permeability of CBM reservoir and CBM well drainage control measures[J]. COAL GEOLOGY & EXPLORATION, 2020, 48(2): 114-119. DOI: 10.3969/j.issn.1001-1986.2020.02.018
    [3]PEI Bailin, HAO Jie, ZHANG Suian, YANG Liyuan. New mathematical model of the influence of coal matrix swelling and shrinkage on reservoir permeability[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(1): 51-55. DOI: 10.3969/j.issn.1001-1986.2017.01.010
    [4]LIU Yanjie, LIU Chao, MA Bing, GUO Jing. Acidification experiment for enhancing the permeability of coal reservoir[J]. COAL GEOLOGY & EXPLORATION, 2016, 44(2): 46-49. DOI: 10.3969/j.issn.1001-1986.2016.02.009
    [5]GUAN Fujia, LI Xiangfang, AN Xiaoping. A new method for estimation formation radial effective permeability based on wireline formation testing[J]. COAL GEOLOGY & EXPLORATION, 2010, 38(6): 75-78. DOI: 10.3969/j.issn.1001-1986.2010.06.016
    [6]LI Xiang-chen, KANG Yi-li, LUO Ping-ya. The effects of stress on fracture and permeability in coal bed[J]. COAL GEOLOGY & EXPLORATION, 2009, 37(1): 29-32.
    [7]ZHANG Hui. One of parameters reflecting coal reservoir permeability——block coal rate[J]. COAL GEOLOGY & EXPLORATION, 2001, 29(6): 21-22.
    [8]FU Xue-hai, QIN Yong, LI Gui-zhong. An analysis on the principal control factor of coal reservoir permeability in Central and Southern Qinshui Basin[J]. COAL GEOLOGY & EXPLORATION, 2001, 29(3): 16-19.
    [9]LIANG Yalin, CHEN Jiliang. DIGITAL LOG DATA PREDICTING CBM PERMEABILITY AND RESERVOIR PRESSURE[J]. COAL GEOLOGY & EXPLORATION, 2000, 28(6): 30-31.
    [10]Wu Pin, Jie Guangxin. SOME FACTORS INFLUENCING THE PERMEABILITY MEASUREMENT IN COAL SEAMS[J]. COAL GEOLOGY & EXPLORATION, 1997, 25(6): 23-25.

  • Cited by

    Periodical cited type(17)

    1. 李昊，李叶繁，魏长婧，王磊杰，康利军，姜川. 基于SBAS-InSAR技术的登封市潜在地质灾害识别研究. 河南科学. 2024(08): 1170-1178 .
    2. 汪晨星，史凌亚，李瑞东. 基于Stacking-InSAR的煤矿沉降监测与综采面参数反演. 陕西煤炭. 2024(10): 14-20 .
    3. 张学辉，崔振东，张中俭，赵磊磊，魏涛，刘东旭，王龙灿. 基于SBAS-InSAR技术的新疆某煤矿长时序地表形变监测与分析. 新疆地质. 2024(03): 459-465 .
    4. 姜川，王磊杰，樊高强，李昊，李叶繁，苑雨，张曦. 基于SBAS-InSAR的郑州煤炭矿区地表沉降监测及演化规律分析. 中国煤炭. 2024(10): 158-165 .
    5. 任瑶瑶，刘国林，牛冲，韩宇，周一鸣. 基于MSBAS InSAR技术的沧州市地表形变监测与分析. 地球物理学进展. 2023(02): 588-599 .
    6. 孙晓云. 基于InSAR和微震技术矿区非法开采事件监测技术探讨和应用. 内蒙古煤炭经济. 2023(03): 113-117 .
    7. 于冰，胡云亮，刘国祥，罗小军，胡金龙. 时序InSAR反演唐山市二维地表形变时间序列. 测绘科学. 2023(06): 82-94+230 .
    8. 孙军，张锦. 基于SBAS-InSAR和偏移追踪技术的露天煤矿地面形变监测. 煤矿安全. 2022(03): 162-169 .
    9. 陈宗玥. 基于图像识别的大型建筑钢结构形变监测研究. 测绘技术装备. 2022(01): 17-21 .
    10. 高宏伟，史先琳，陈晨，尹勇，戴可人. 云南漾濞地震地表二维形变提取. 昆明理工大学学报(自然科学版). 2022(02): 57-64 .
    11. 王凤云，陶秋香，陈洋，韩宇，郭在洁. 基于InSAR的煤矿采空区地表形变监测与预警. 煤矿安全. 2022(06): 195-203 .
    12. 贺黎明，裴攀科，吴立新，张香凝. 基于时序InSAR的矿区滑坡前地表运动特征分析. 东北大学学报(自然科学版). 2022(09): 1314-1321+1368 .
    13. 胡华宗. 基于无人机遥感技术的矿井地面塌陷综合监测. 能源与环保. 2022(09): 85-89 .
    14. 刘健，周皓，张恩正. 基于机器学习的煤矿开采沉陷自动化监测系统. 信息技术. 2022(11): 143-148+154 .
    15. 白洁. 基于机器视觉的测绘工程地面位移形变测量方法. 经纬天地. 2021(02): 93-97 .
    16. 姚鑫，吴付英. 基于GIS技术的矿区开采沉陷形变监测系统设计. 矿产与地质. 2021(03): 549-553+573 .
    17. 高文，王华，侯凌志. 矿山地质灾害监测方法与自动化监测预警系统应用. 西部资源. 2020(06): 66-68 .

    Other cited types(3)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (100) PDF downloads (7) Cited by(20)
    Related

    Copyright of website design © Editorial Office of Coal Geology & Exploration 陕ICP备05001372号-6 陕公网安备 61019002002193号

    Address: Editorial Office of Coal Geology & Exploration, No.82 Jinye 1st Rd, High-Tech Zone, Xi'an City, Shaanxi, China

    Tel: 029-81778075 E-mail: ccrimtdzykt@vip.163.com

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return