Open AccessIdentifying coal structure using logging data
Author(s) -
Zhidi Liu,
Xiaoyan Tang,
Junru Yang,
Mengxuan Shi
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/526/1/012133
Subject(s) - coal , fracture (geology) , stability (learning theory) , coal mining , mining engineering , chart , petroleum engineering , geology , geotechnical engineering , computer science , engineering , mathematics , statistics , waste management , machine learning
Coal structure, which is important for the mining of CBM, is closely related to the coal reservoir fracture characteristics. This paper calculates the integrity coefficient, stability coefficient and fracture coefficient of coal, and constructs a chart confirming the division standard of the coal structure in the study area. The research results show that coal mechanical parameters can effectively represent coal structure. The integrity and stability coefficients of undeformed coal are the highest, and its fracture coefficient is the lowest; the integrity and stability coefficients of fragmented coal, granulated coal and mylonitized coal gradually decrease, and their fracture coefficients gradually increase. The methods described in the paper can classify coal structure relatively accurately and provide an effective new method for the delimiting of coal structure.