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Coal Macrolithotype Distribution and Its Genetic Analyses in the Deep Jiaozuo Coalfield Using Geophysical Logging Data
[Image: see text] Coal macrolithotypes are closely correlated with coal macerals and pore–fracture structures, which greatly influence the changes in gas content and the coal structure. Traditional macrolithotype identification in coalbed methane (CBM) wells mostly depends on core drilling observati...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8717541/ https://www.ncbi.nlm.nih.gov/pubmed/34984284 http://dx.doi.org/10.1021/acsomega.1c05012 |
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author | Hou, Haihai Zhang, Huajie Shao, Longyi Guo, Shuangqing Zhao, Ming’en Wang, Shuai |
author_facet | Hou, Haihai Zhang, Huajie Shao, Longyi Guo, Shuangqing Zhao, Ming’en Wang, Shuai |
author_sort | Hou, Haihai |
collection | PubMed |
description | [Image: see text] Coal macrolithotypes are closely correlated with coal macerals and pore–fracture structures, which greatly influence the changes in gas content and the coal structure. Traditional macrolithotype identification in coalbed methane (CBM) wells mostly depends on core drilling observation, which is expensive, time-consuming, and difficult for broken core extraction. Geophysical logging is a quick and effective method to address this issue. We obtained coal cores from 75 wells in the deep regions of the Jiaozuo Coalfield, northern China, quantitatively analyzed the logging cutoff number corresponding to various macrolithotypes, and established natural γ (GR), deep lateral resistivity (LLD), and γ–γ log (GGL) response rules for each coal macrolithotype. The formation mechanisms of different coal macrolithotypes are discussed from the perspective of coal facies and pore structures. The results show that GGL decreased but GR and LLD increased from bright coal to dull coal. Most coal macrolithotypes can be distinguished based on the established thresholds of various logging curves. However, excessively high or low ash yields significantly affect the validity of identification. The vertical coal macrolithotypes attributed to the peat marsh environment in Shanxi Formation mostly comprise three to six sublayers; dull or semi-dull coals are predominant close to the 2(1) coal seam, and the bright or semi-bright types usually appear in the middle part. The semi-bright and bright coals are usually vitrinite rich, whereas the semi-dull and dull coals are primarily inertinite rich. For pore structure arguments, the highest average specific surface area (S(BET)) and the total pore volume (V(BJH)) are found in bright coals, followed by dull and semi-bright coals; those of semi-dull coals are the lowest. However, S(BET) and V(BJH) change significantly for different samples, even though the coal macrolithotype is the same. Therefore, the macrolithotype is not the key factor determining the coal parameters of pore structures. Rapid and effective identification of coal macrolithotypes can help determine the CBM enrichment area, the CBM well location, and the exploration horizon. |
format | Online Article Text |
id | pubmed-8717541 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-87175412022-01-03 Coal Macrolithotype Distribution and Its Genetic Analyses in the Deep Jiaozuo Coalfield Using Geophysical Logging Data Hou, Haihai Zhang, Huajie Shao, Longyi Guo, Shuangqing Zhao, Ming’en Wang, Shuai ACS Omega [Image: see text] Coal macrolithotypes are closely correlated with coal macerals and pore–fracture structures, which greatly influence the changes in gas content and the coal structure. Traditional macrolithotype identification in coalbed methane (CBM) wells mostly depends on core drilling observation, which is expensive, time-consuming, and difficult for broken core extraction. Geophysical logging is a quick and effective method to address this issue. We obtained coal cores from 75 wells in the deep regions of the Jiaozuo Coalfield, northern China, quantitatively analyzed the logging cutoff number corresponding to various macrolithotypes, and established natural γ (GR), deep lateral resistivity (LLD), and γ–γ log (GGL) response rules for each coal macrolithotype. The formation mechanisms of different coal macrolithotypes are discussed from the perspective of coal facies and pore structures. The results show that GGL decreased but GR and LLD increased from bright coal to dull coal. Most coal macrolithotypes can be distinguished based on the established thresholds of various logging curves. However, excessively high or low ash yields significantly affect the validity of identification. The vertical coal macrolithotypes attributed to the peat marsh environment in Shanxi Formation mostly comprise three to six sublayers; dull or semi-dull coals are predominant close to the 2(1) coal seam, and the bright or semi-bright types usually appear in the middle part. The semi-bright and bright coals are usually vitrinite rich, whereas the semi-dull and dull coals are primarily inertinite rich. For pore structure arguments, the highest average specific surface area (S(BET)) and the total pore volume (V(BJH)) are found in bright coals, followed by dull and semi-bright coals; those of semi-dull coals are the lowest. However, S(BET) and V(BJH) change significantly for different samples, even though the coal macrolithotype is the same. Therefore, the macrolithotype is not the key factor determining the coal parameters of pore structures. Rapid and effective identification of coal macrolithotypes can help determine the CBM enrichment area, the CBM well location, and the exploration horizon. American Chemical Society 2021-12-13 /pmc/articles/PMC8717541/ /pubmed/34984284 http://dx.doi.org/10.1021/acsomega.1c05012 Text en © 2021 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Hou, Haihai Zhang, Huajie Shao, Longyi Guo, Shuangqing Zhao, Ming’en Wang, Shuai Coal Macrolithotype Distribution and Its Genetic Analyses in the Deep Jiaozuo Coalfield Using Geophysical Logging Data |
title | Coal Macrolithotype Distribution and Its Genetic Analyses
in the Deep Jiaozuo Coalfield Using Geophysical Logging Data |
title_full | Coal Macrolithotype Distribution and Its Genetic Analyses
in the Deep Jiaozuo Coalfield Using Geophysical Logging Data |
title_fullStr | Coal Macrolithotype Distribution and Its Genetic Analyses
in the Deep Jiaozuo Coalfield Using Geophysical Logging Data |
title_full_unstemmed | Coal Macrolithotype Distribution and Its Genetic Analyses
in the Deep Jiaozuo Coalfield Using Geophysical Logging Data |
title_short | Coal Macrolithotype Distribution and Its Genetic Analyses
in the Deep Jiaozuo Coalfield Using Geophysical Logging Data |
title_sort | coal macrolithotype distribution and its genetic analyses
in the deep jiaozuo coalfield using geophysical logging data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8717541/ https://www.ncbi.nlm.nih.gov/pubmed/34984284 http://dx.doi.org/10.1021/acsomega.1c05012 |
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