Numerical simulation of azimuth electromagnetic wave response of complex geological model of horizontal hole in coal mines

被引：0
|
作者：
Zhang Y. [1 ,2 ]
Kang Z. [3 ]
Feng H. [2 ]
Han X. [4 ]
Chen G. [2 ]
机构：
[1] China Coal Research Institute, Beijing
[2] Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an
[3] School of Electronic Engineering, Xi’an Shiyou University, Xi’an
[4] China Petroleum Group Logging Co. LTD, Xi’an
来源：
Meitiandizhi Yu Kantan/Coal Geology and Exploration | 2022年 / 50卷 / 05期
关键词：
3D model; azimuth electromagnetic wave logging; coal-rock interface; forward modeling; horizontal hole;
D O I：
10.12363/issn.1001-1986.21.12.0854
中图分类号：
学科分类号：
摘要：
The advance and accurate identification of the coal-rock interface is one of the key technologies to realize automatic tunneling and intellectualization of coal mines. Azimuth electromagnetic wave tool has a large detection range and can distinguish the lithologic interface and interface orientation. It has been successfully applied in oilfield logging, but there are few researches on the coal mine measurement environment. In order to study the detection performance of azimuth electromagnetic wave logging in the complex 3D model of coal mines, the finite element numerical simulation method is used to investigate the influence of the drilling fluid in the borehole on azimuth electromagnetic wave logging with different transmission frequency and spacing in the coal mine measuring environment, and the influence of the mined-out area and the stratigraphic boundary on the azimuth electromagnetic wave measurement. The results show that the azimuth electromagnetic wave response is less affected by the borehole when the fluid in the borehole is air. The azimuth electromagnetic wave measurement signal can reflect the fluctuation of the stratum interface, and its detection ability is related to the transmission frequency and spacing. The azimuth electromagnetic wave is sensitive to the low-resistance goaf, but a phase difference signal can be used to detect high-resistance goaf in high-frequency transmission. The azimuth electromagnetic wave method provides a new technical means for the measurement of the coal-rock interface and goaf in the horizontal hole of coal mines, which has great application potential. Copyright © 2022 Meitiandizhi Yu Kantan/Coal Geology and Exploration
引用
收藏
页码：118 / 128
页数：10
相关论文
共 21 条
  • [1] GE Shirong, HAO Xuedi, TIAN Kai, Et al., Principle and key technology of autonomous navigation cutting for deep coal seam[J], Journal of China Coal Society, 46, 3, pp. 774-788, (2021)
  • [2] ZHANG Qiang, SUN Shao' an, ZHANG Kun, Et al., Coal and rock interface identification based on active infrared excitation[J], Journal of China Coal Society, 45, 9, pp. 3363-3370, (2020)
  • [3] WU Dezhong, LIU Quansheng, HUANG Xing, Et al., Coal–rock interface recognition method based on boundary tracking algorithm and artificial neural network[J], Coal Engineering, 53, 6, pp. 140-146, (2021)
  • [4] Li LI, LIU Jiapeng, WEI Wei, Signal processing method on ultrasonic echo from coal–rock interface based on EWT[J], Journal of China Coal Society, 44, Sup.1, pp. 370-377, (2019)
  • [5] WANG Li, SU Bo, Study on identification method of coal–rock interface in fully mechanized mining face[J], China Plant Engineering, 11, 1, pp. 215-216, (2020)
  • [6] WANG Lei, FAN Yiren, YUAN Chao, Et al., Selection criteria and feasibility of the inversion model for azimuthal electromagnetic logging while drilling(LWD)[J], Petroleum Exploration and Development, 45, 5, pp. 914-922, (2018)
  • [7] HU Xufei, FAN Yiren, WU Fei, Et al., Fast multiple parameter inversion of azimuthal LWD electromagnetic measurement[J], Chinese Journal of Geophysics, 61, 11, pp. 4690-4701, (2018)
  • [8] ZHANG Yi, FENG Hong, HAN Xue, Et al., Key problems in the development of petroleum electromagnetic logging[J], Oil Geophysical Prospecting, 56, 6, pp. 1430-1447, (2021)
  • [9] Mengbo ZHU, Jianyuan CHENG, ZHANG Zheng, Quality control of microseismic P–phase arrival picks in coal mine based on machine learning[J], Computers and Geosciences, 156, (2021)
  • [10] Mengbo ZHU, Jianyuan CHENG, Weixiong CUI, Et al., Comprehensive prediction of coal seam thickness by using in–seam seismic surveys and Bayesian kriging[J], Acta Geophysica, 67, 4, (2019)
123