Identification of tar-rich coal based on the true density and natural gamma ray response of coals

Authors

SHI Qingmin, Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi’an University of Science and Technology, Xi’an 710054, China; College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaFollow
GENG Xuhu, Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi’an University of Science and Technology, Xi’an 710054, China; College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
WANG Shuangming, Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi’an University of Science and Technology, Xi’an 710054, China; College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
CAI Yue, Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi’an University of Science and Technology, Xi’an 710054, China; College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
HAN Bo, China Petroleum Logging Co. Ltd., Xi’an 710054, China
WANG Shengquan, Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi’an University of Science and Technology, Xi’an 710054, China; College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
ZHANG Zhehao, China Petroleum Logging Co. Ltd., Xi’an 710054, China
HE Yufei, China Petroleum Logging Co. Ltd., Xi’an 710054, China

Abstract

Objective Tar yield, derived from the Gray-King assay, serves as the sole metric for evaluating tar-rich coal. However, insufficient exploration data hinder its effective application in the fine-scale evaluation of extensive tar-rich coal. Methods Based on binary classification, this study proposed the optimal thresholds for tar-rich coal identification using true density (ρ) and natural gamma-ray (GR) log. Moreover, this study elucidated the intrinsic mechanisms behind the significant petrophysical parameter responses of tar-rich coal. Results and Conclusions Key findings are as follows: (1) Coals with various metamorphic grades differ in identification threshold. For instance, long-flame tar-rich coal, the optimal identification thresholds consist of raw coal’s true density of < 1.41 g/cm³ and GR responses < 80 API units, corresponding to identification accuracy of up to 81.82%. (2) The geophysical log parameter responses of the aforementioned tar-rich coal are affected by inorganic and organic components. The ash content, correlating negatively with the tar yield, acts as the dominant factor influencing the true density of coals, and the clay mineral content indicated by the ash composition (Al₂O₃+SiO₂) significantly influences the GR responses of coals. Additionally, macerals, determining the tar yield, produce a certain impact on raw coal’s true density. (3) On a molecular scale, a lower true density under a dry ash-free basis (ρ_daf) corresponds to richer aliphatic structures in coals, creating more favorable pyrolysis conditions for tar production. Conversely, a higher true density corresponds to higher aromatic structure content, which is more unfavorable for tar production through pyrolysis. Overall, the coupling effects of multiple factors contribute to a strongly negative correlation between the true density and tar yield of coals. These findings can serve as a theoretical guide for evaluating tar-rich coal based on geophysical log parameters.

Keywords

tar-rich coal, tar yield, log, true density, natural gamma ray, Ordos Basin

DOI

10.12363/issn.1001-1986.23.08.0471

Recommended Citation

SHI Qingmin, GENG Xuhu, WANG Shuangming, et al. (2024) "Identification of tar-rich coal based on the true density and natural gamma ray response of coals," Coal Geology & Exploration: Vol. 52: Iss. 7, Article 10.
DOI: 10.12363/issn.1001-1986.23.08.0471
Available at: https://cge.researchcommons.org/journal/vol52/iss7/10

Reference

[1] 王双明，师庆民，王生全，等. 富油煤的油气资源属性与绿色低碳开发[J]. 煤炭学报，2021，46(5)：1365−1377.

WANG Shuangming，SHI Qingmin，WANG Shengquan，et al. Resource property and exploitation concepts with green and low–carbon of tar–rich coal as coal–based oil and gas[J]. Journal of China Coal Society，2021，46(5)：1365−1377.

[2] 张宁，许云，乔军伟，等. 陕北侏罗纪富油煤有机地球化学特征[J]. 煤田地质与勘探，2021，49(3)：42−49.

ZHANG Ning，XU Yun，QIAO Junwei，et al. Organic geochemistry of the Jurassic tar–rich coal in Northern Shaanxi Province[J]. Coal Geology & Exploration，2021，49(3)：42−49.

[3] 李华兵，姚征，李宁，等. 神府矿区5^–2煤层富油煤赋存特征及资源潜力评价[J]. 煤田地质与勘探，2021，49(3)：26−32.

LI Huabing，YAO Zheng，LI Ning，et al. Occurrence characteristics and resource potential evaluation of tar–rich coal for No.5^–2 coal seam in Shenfu Mining Area[J]. Coal Geology & Exploration，2021，49(3)：26−32.

[4] 东振，张梦媛，陈艳鹏，等. 三塘湖–吐哈盆地富油煤赋存特征与资源潜力分析[J]. 煤炭学报，2023，48(10)：3789−3805.

DONG Zhen，ZHANG Mengyuan，CHEN Yanpeng，et al. Analysis on the occurrence characteristics and resource potential of tar–rich coal in Santanghu and Turpan–Hami Basins[J]. Journal of China Coal Society，2023，48(10)：3789−3805.

[5] 申艳军，王旭，赵春虎，等. 榆神府矿区富油煤多尺度孔隙结构特征[J]. 煤田地质与勘探，2021，49(3)：33−41.

SHEN Yanjun，WANG Xu，ZHAO Chunhu，et al. Experimental study on multi–scale pore structure characteristics of tar–rich coal in Yushenfu Mining Area[J]. Coal Geology & Exploration，2021，49(3)：33−41.

[6] 王旭. 榆神府矿区富油煤组构特性及破碎强度试验研究[D]. 西安：西安科技大学，2021.

WANG Xu. Experimental study on structural characteristics and mechanical behavior of oil-rich coal in Yushenfu Mining Area[D]. Xi’an：Journal of Xi’an University of Science and Technology，2021.

[7] 谢青，李宁，姚征，等. 黄陵矿区富油煤焦油产率特征及主控地质因素分析[J]. 中国煤炭，2020，46(11)：83−90.

XIE Qing，LI Nning，YAO Zheng，et al. Research on the tar yield characteristics and main control factors of tar-rich coal in Huangling mining area[J]. China Coal，2020，46(11)：83−90.

[8] 蔡昕原，申文盛，马丽，等. 表面活性剂对陕北富油煤微生物降解的影响[J]. 西安科技大学学报，2022，42(2)：317−323.

CAI Xinyuan，SHEN Wensheng，MA Li，et al. Effects of surfactants on microbial degradation of tar-rich coals from northern Shaanxi[J]. Journal of Xi’an University of Science and Technology，2022，42(2)：317−323.

[9] 姚征，罗乾周，李宁，等. 陕北石炭–二叠纪富油煤赋存特征及影响因素[J]. 煤田地质与勘探，2021，49(3)：50−61.

YAO Zheng，LUO Qianzhou，LI Ning，et al. Occurrence characteristics of Carboniferous–Permian tar–rich coal and its influencing factors in Northern Shaanxi[J]. Coal Geology & Exploration，2021，49(3)：50−61.

[10] 师庆民，米奕臣，王双明，等. 富油煤热解流体滞留特征及其机制[J]. 煤炭学报，2022，47(3)：1329−1337.

SHI Qingmin，MI Yichen，WANG Shuangming，et al. Trap characteristic and mechanism of volatiles during pyrolysis of tar–rich coal[J]. Journal of China Coal Society，2022，47(3)：1329−1337.

[11] 唐颖，吴晓丹，李乐忠，等. 富油煤原位热解地下加热技术及其高效工艺[J]. 洁净煤技术，2023，29(12)：42−50.

TANG Ying，WU Xiaodan，LI Lezhong，et al. Heating technology of in–situ pyrolysis for tar–rich coal and its high efficiency process[J]. Clean Coal Technology，2023，29(12)：42−50.

[12] 付德亮，段中会，杨甫，等. 富油煤钻井式地下原位热解提取煤基油气资源的几个关键问题[J]. 煤炭学报，2023，48(4)：1759−1772.

FU Deliang，DUAN Zhonghui，YANG Fu，et al. Key problems in in–situ pyrolysis of tar–rich coal drilling for extraction of coal–based oil and gas resources[J]. Journal of China Coal Society，2023，48(4)：1759−1772.

[13] 尚建选，张喻，闵楠，等. 陕西煤业化工集团煤化工产业高质量发展研究[J]. 中国煤炭，2022，48(8)：14−19.

SHANG Jianxuan，ZHANG Yu，MIN Nan，et al. Research on high-quality development of coal chemical industry in Shaanxi Coal and chemical industry group[J]. China Coal，2022，48(8)：14−19.

[14] 马丽，王双明，段中会，等. 陕西省富油煤资源潜力及开发建议[J]. 煤田地质与勘探，2022，50(2)：1−8.

MA Li，WANG Shuangming，DUAN Zhonghui，et al. Potential of oil–rich coal resources in Shaanxi Province and its new development suggestion[J]. Coal Geology & Exploration，2022，50(2)：1−8.

[15] WANG Zihao，CAI Yidong，LIU Dameng，et al. Intelligent classification of coal structure using multinomial logistic regression，random forest and fully connected neural network with multisource geophysical logging data[J]. International Journal of Coal Geology，2023，268：104208.

[16] TENG Juan，YAO Yanbin，LIU Dameng，et al. Evaluation of coal texture distributions in the southern Qinshui Basin，North China：Investigation by a multiple geophysical logging method[J]. International Journal of Coal Geology，2015，140：9−22.

[17] TIWARY A K，GHOSH S，SINGH R，et al. Automated coal petrography using random forest[J]. International Journal of Coal Geology，2020，232：103629.

[18] 毛志强，赵毅，孙伟，等. 利用地球物理测井资料识别我国的煤阶类型[J]. 煤炭学报，2011，36(5)：766−771.

MAO Zhiqiang，ZHAO Yi，SUN Wei，et al. Identification on the type of coal rank by using geophysical well logging data[J]. Journal of China Coal Society，2011，36(5)：766−771.

[19] 师庆民，王双明，王生全，等. 神府南部延安组富油煤多源判识规律[J]. 煤炭学报，2022，47(5)：2057−2066.

SHI Qingmin，WANG Shuangming，WANG Shengquan，et al. Multi–source identification and internal relationship of tar–rich coal of the Yan’an Formation in the south of Shenfu[J]. Journal of China Coal Society，2022，47(5)：2057−2066.

[20] SHI Qingmin，LI Chunhao，WANG Shuangming，et al. Effect of the depositional environment on the formation of tar–rich coal：A case study in the northeastern Ordos Basin，China[J]. Journal of Petroleum Science and Engineering，2022，216：110828.

[21] 徐浩. 鄂尔多斯盆地煤系矿产资源赋存规律的构造控制研究[D]. 北京：中国矿业大学(北京)，2017.

XU Hao. Occurrence characteristics and tectonic controls of coal series mineral resources in Ordos Basin[D]. Beijing：China University of Mining and Technology (Beijing)，2017.

[22] 中国煤田地质总局. 鄂尔多斯盆地聚煤规律及煤炭资源评价[M]. 北京：煤炭工业出版社，1996.

[23] 魏迎春，曹代勇，宁树正，等. 鄂尔多斯盆地煤系矿产资源赋存规律研究进展[J]. 中国煤炭地质，2018，30(6)：14−20.

WEI Yingchun，CAO Daiyong，NING Shuzheng，et al. Research progress of coal measures mineral resource hosting patterns in Ordos Basin[J]. Coal Geology of China，2018，30(6)：14−20.

[24] 李增学，李江涛，韩美莲，等. 鄂尔多斯盆地中生界聚煤规律及对多能源共存富集的贡献[J]. 山东科技大学学报(自然科学版)，2006，25(2)：1−5.

LI Zengxue，LI Jiangtao，HAN Meilian，et al. On the coal accumulating law of Mesozoic and its contribution to the concentration of multiple energy resources in Ordos Basin[J]. Journal of Shandong University of Science and Technology (Natural Science)，2006，25(2)：1−5.

[25] WANG Dongdong，SHAO Longyi，LI Zhixue，et al. Hydrocarbon generation characteristics，reserving performance and preservation conditions of continental coal measure shale gas：A case study of Mid–Jurassic shale gas in the Yan’an Formation，Ordos Basin[J]. Journal of Petroleum Science and Engineering，2016，145：609−628.

[26] JIU Bo，HUANG Wenhui，HAO Ruilin. A method for judging depositional environment of coal reservoir based on coal facies parameters and rare earth element parameters[J]. Journal of Petroleum Science and Engineering，2021，207：109128.

[27] 赵云刚，李美芬，曾凡桂，等. 伊敏褐煤不同化学组分结构特征的红外光谱研究[J]. 煤炭学报，2018，43(2)：546−554.

ZHAO Yungang，LI Meifen，ZENG Fangui，et al. FTIR study of structural characteristics of different chemical components from Yimin Lignite[J]. Journal of China Coal Society，2018，43(2)：546−554.

[28] NORTON M，URYASEV S. Maximization of AUC and Buffered AUC in binary classification[J]. Mathematical Programming，2019，174(1-2)：575−612.

[29] 于营，杨婷婷，杨博雄. 混淆矩阵分类性能评价及Python实现[J]. 现代计算机，2021，20：70−73.

YU Ying，YANG Tingting，YANG Boxiong. Confusion matrix classification performance evaluation and Python implementation[J]. Modern Computer，2021，20：70−73.

[30] 李俊堂，王如江，高彬，等. 新景煤矿保安区煤体结构特征及测井模型构建[J]. 矿业安全与环保，2022，49(2)：72−77.

LI Juntang，WANG Rujiang，GAO Bin，et al. Characteristics of coal structure and construction of well logging model in Bao’an Block of Xinjing Mine[J]. Mining Safety & Environmental Protection，2022，49(2)：72−77.

[31] CHATTERJEE R，PAUL S. Classification of coal seams for coal bed methane exploitation in central part of Jharia Coalfield，India：A statistical approach[J]. Fuel，2013，111：20−29.

[32] GHOSH S，CHATTERJEE R，PAUL S，et al. Designing of plug–in for estimation of coal proximate parameters using statistical analysis and coal seam correlation[J]. Fuel，2014，134：63−73.

[33] 李增学，魏久传，余继峰，等. 煤地质学[M]. 北京：地质出版社，2009.

[34] IHEKWEME G O，SHONDO J N，ORISEKEH K I，et al. Characterization of certain Nigerian clay minerals for water purification and other industrial applications[J]. Heliyon，2020，6(4)：e03783.

[35] XU Yong，CHEN Xuexi，ZHAO Wei，et al. Water vapor sorption properties on coals affected by hydrophilic inorganic minerals and pore fissures[J]. Fuel，2022，324：124659.

[36] YUE Liang，JIAO Yangquan，WU Liqun，et al. Evolution and origins of pyrite in sandstone–type uranium deposits，northern Ordos Basin，north–central China，based on micromorphological and compositional analysis[J]. Ore Geology Reviews，2020，118：103334.

[37] FINKELMAN R B. Trace and minor elements in coal[J]. Organic Geochemistry，1993，28：593−607.

[38] WU Dun，WANG Yuanyuan，WANG Meichen，et al. Basic characteristics of coal gangue in a small–scale mining site and risk assessment of radioactive elements for the surrounding soils[J]. Minerals，2021，11(6)：647.

[39] XU Hao，TANG Dazhen，MATHEWS J P，et al. Evaluation of coal macrolithotypes distribution by geophysical logging data in the Hancheng Block，Eastern Margin，Ordos Basin，China[J]. International Journal of Coal Geology，2016，165：265−277.

[40] 李飞. 煤炭显微组分与密度相关性的试验研究[D]. 太原：太原理工大学，2014.

LI Fei. Experimental research on the correlation of coal maceral and the density[D]. Taiyuan：Taiyuan University of Technology，2014.

[41] SHI Qingmin，LI Chunhao，WANG Shuangming，et al. Variation of molecular structures affecting tar yield：A comprehensive analysis on coal ranks and depositional environments[J]. Fuel，2023，335：127050.

[42] XIN Haihui，WANG Deming，QI Xuyao，et al. Structural characteristics of coal functional groups using quantum chemistry for quantification of infrared spectra[J]. Fuel Processing Technology，2014，118：287−295.

[43] ZHOU He，WU Caifang，PAN Jienan，et al. Research on molecular structure characteristics of vitrinite and inertinite from bituminous coal with FTIR，Micro–Raman，and XRD spectroscopy[J]. Energy & Fuels，2021，35(2)：1322−1335.

[44] MENG Dexi，YUE Chengyan，WANG Tian，et al. Evolution of carbon structure and functional group during Shenmu lump coal pyrolysis[J]. Fuel，2021，287：119538.