Skip to main content

Advertisement

Log in

Coal Body Structure Detection Based on Logging and Seismic Data and Its Impacts on Coalbed Methane Development: A Case Study in the Dahebian Block, Western Guizhou, Southern China

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

Coal mining and coalbed methane (CBM) development in Western Guizhou are hampered by the tectonically deformed coal (TDC). In this article, the support vector machine algorithm was used to train and establish coal body structure detection models based on logging and seismic data, and the coal body structure distribution in the Dahebian block was predicted. The fivefold cross-validation prediction accuracy for identifying coal body structure using logging data is 96.46%. The coefficient of determination of fivefold cross-validation for predicting coal body structure thickness using seismic data is generally greater than 0.99. The coal body structure distributions in the No.1, 4, and 7 coal seams are similar, containing about 2–3 layers, and are dominated by cataclastic coal. The primary undeformed coal is usually found in the inter-fault area, whereas the cataclastic and granulated coals are mostly developed along the fault. The No.11 coal seam generally has more than 5 layers of coal body structures, mainly granulated coal. The primary undeformed coal is primarily distributed along the fault, and the granulated coal is widespread and not restricted to the fault area. The No.11 coal seam contains the most CBM resources, with more than half stored in TDC. The CBM resources in the No.1, 4, and 7 coal seams are mostly stored in cataclastic and granulated coal. The cumulative gas production is adversely associated with the proportion of TDC, and the increase in the gas production rate of wells with a high proportion of TDC is relatively slow. When releasing stress through protective layer mining, the No.11 coal seam is suitable as the protected layer. The utilization of horizontal well cavity completion for stress relief is an appropriate approach for CBM development in the No.11 coal seam dominated by thick granulated coal. This study has significant theoretical guidance and engineering reference significance for coal mining and CBM development in the study area and areas with similar demands.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20

Similar content being viewed by others

References

  • Ahamed, M. A. A., Perera, M. S. A., Dong-yin, L., Ranjith, P. G., & Matthai, S. K. (2019). Proppant damage mechanisms in coal seam reservoirs during the hydraulic fracturing process: A review. Fuel, 253, 615–629.

    Article  Google Scholar 

  • Batista, G. E., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20–29.

    Article  Google Scholar 

  • Bunt, R. J. (2015). The use of seismic attributes for fan and reservoir definition in the Sea Lion Field, North Falkland Basin. Petroleum Geoscience, 21(2–3), 137–149.

    Article  Google Scholar 

  • Cao, L., Yao, Y., Liu, D., Yang, Y., Wang, Y., & Cai, Y. (2020). Application of seismic curvature attributes in the delineation of coal texture and deformation in Zhengzhuang field, southern Qinshui Basin. AAPG Bulletin, 104(5), 1143–1166.

    Article  Google Scholar 

  • Cao, Y., Davis, A., Liu, R., Liu, X., & Zhang, Y. (2003). The influence of tectonic deformation on some geochemical properties of coals—A possible indicator of outburst potential. International Journal of Coal Geology, 53(2), 69–79.

    Article  Google Scholar 

  • Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 1–27.

    Article  Google Scholar 

  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.

    Article  Google Scholar 

  • Chen, Q., Jing, J., Liu, J., Long, J., & Zhang, S. (2019). Productivity evaluation of coalbed methane well with geophysical logging-derived tectonically deformed coal. Energies, 12(18), 3459.

    Article  Google Scholar 

  • Chen, T., Wang, X., & Guan, Y. (2015). Quantitative prediction of tectonic coal seam thickness using support vector regression and seismic attributes. Journal of China Coal Society, 40(5), 1103–1108. (in Chinese with English abstract).

    Google Scholar 

  • Chen, T., Wang, Z., Yang, G., Li, J., & Peng, R. (2014). Analysis of cavitation pressure difference during blowdown in CBM cavity completion. Journal of Natural Gas Science and Engineering, 18, 175–179.

    Article  Google Scholar 

  • Chen, Y., Zhang, X., & Pu, J. (2017). Logging parameters applied to quantitatively identify coal structure in Northern Hancheng Mining Area. Coal Science and Technology, 45(9), 42–46. (in Chinese with English abstract).

    Google Scholar 

  • Cheng, G., Jiang, B., Li, M., Liu, J., & Li, F. (2020). Effects of pore structure on methane adsorption behavior of ductile tectonically deformed coals: An inspiration to coalbed methane exploitation in structurally complex area. Journal of Natural Gas Science and Engineering, 74, 103083.

    Article  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

    Article  Google Scholar 

  • Cui, D., Wang, Y., & Yu, J. (2013). Study on the distribution of deformed coal with AVO attributes. Journal of China Coal Society, 38(10), 1842–1849. (in Chinese with English abstract).

    Google Scholar 

  • Ding, S., Qin, B., & Tan, H. (2011). An overview on theory and algorithm of support vector machines. Journal of University of Electronic Science and Technology of China, 40(1), 2–10. (in Chinese with English abstract).

    Google Scholar 

  • Dou, X., Jiang, B., Qin, Y., Qu, Z., & Li, M. (2012). Tectonic control of coalbed methane reservoirs in Panxian, Western Guizhou. Geological Journal of China Universities, 18(3), 447–452. (In Chinese with English abstract).

    Google Scholar 

  • Fan, J., Ju, Y., Hou, Q., & Wei, M. (2010). Pore structure characteristics of different metamorphic-deformed coal reservoirs and its restriction on recovery of coalbed methane. Earth Science Frontiers, 17(5), 325–335. (in Chinese with English abstract).

    Google Scholar 

  • Feng, Q., Zhang, J., Zhang, X., & Hu, A. (2012). Optimizing well placement in a coalbed methane reservoir using the particle swarm optimization algorithm. International Journal of Coal Geology, 104, 34–45.

    Article  Google Scholar 

  • Fu, X., Qin, Y., Wang, G. G., & Rudolph, V. (2009). Evaluation of coal structure and permeability with the aid of geophysical logging technology. Fuel, 88(11), 2278–2285.

    Article  Google Scholar 

  • Gong, Z. (2019). Logging curve identification technology of coal body and combination in Guxu Mining Area. Science Technology and Engineering, 19(20), 77–84. (in Chinese with English abstract).

    Google Scholar 

  • Goupillaud, P., Grossmann, A., & Morlet, J. (1984). Cycle-octave and related transforms in seismic signal analysis. Geoexploration, 23(1), 85–102.

    Article  Google Scholar 

  • Guo, D., Han, D., & Zhang, J. (2002). Research on the occurrence and distribution of structural coal in Pingdingshan coal district. Journal of China Coal Society, 27(3), 249–253. (in Chinese with English abstract).

    Google Scholar 

  • Guo, J., Du, T., Zhang, Z., Xiao, H., Qin, R., Yu, J., & Can, W. (2021). The coal structure identification method based on support vector machine and geophysical logging data. Geophysical and Geochemical Exploration, 45(3), 768–777. (in Chinese with English abstract).

    Google Scholar 

  • Hackley, P. C., & Martinez, M. (2007). Organic petrology of Paleocene Marcelina Formation coals, Paso Diablo mine, western Venezuela: Tectonic controls on coal type. International Journal of Coal Geology, 71(4), 505–526.

    Article  Google Scholar 

  • Huang, H., Sang, S., Fang, L., Li, G., Xu, H., & Ren, B. (2010). Optimum location of surface wells for remote pressure relief coalbed methane drainage in mining areas. Mining Science and Technology (China), 20(2), 230–237.

    Article  Google Scholar 

  • Ju, Y., & Li, X. (2009). New research progress on the ultrastructure of tectonically deformed coals. Progress in Natural Science, 19(11), 1455–1466.

    Article  Google Scholar 

  • Ju, Y., Wang, G., & Hu, C. (2002). Tectonic deformation and its control over thickness of coal seams in Haizi coal mine. Journal of China University of Mining & Technology, 31(4), 47–52. (in Chinese with English abstract).

    Google Scholar 

  • Kang, J., Fu, X., Shen, J., Liang, S., Chen, H., & Shang, F. (2022). Characterization of coal structure of high-thickness coal reservoir using geophysical logging: A case study in Southern Junggar Basin, Xinjiang, Northwest China. Natural Resources Research, 31(2), 929–951.

    Article  Google Scholar 

  • Kang, Q., Chen, X., Li, S., & Zhou, M. (2016). A noise-filtered under-sampling scheme for imbalanced classification. IEEE Transactions on Cybernetics, 47(12), 4263–4274.

    Article  Google Scholar 

  • Karimi, A. M., Sadeghnejad, S., & Rezghi, M. (2021). Well-to-well correlation and identifying lithological boundaries by principal component analysis of well-logs. Computers & Geosciences, 157, 104942.

    Article  Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942–1948). IEEE.

  • Li, H. (2001). Major and minor structural features of a bedding shear zone along a coal seam and related gas outburst, Pingdingshan coalfield, northern China. International Journal of Coal Geology, 47(2), 101–113.

    Article  Google Scholar 

  • Li, J., Pan, D., Cui, R., Ding, E., Zhang, W., & Hu, M. (2016). Prediction of tectonically deformed coal based on lithologic seismic information. Journal of Geophysics and Engineering, 13(1), 116–122.

    Article  Google Scholar 

  • Li, W., Yao, H., Liu, H., Kang, Z., Song, X., & Feng, Z. (2014). Advanced characterization of three-dimensional pores in coals with different coal-body structure by micro-CT. Journal of China Coal Society, 39(6), 1127–1132. (in Chinese with English abstract).

    Google Scholar 

  • Liu, J., Chang, S., Zhang, S., Li, Y., & Chen, Q. (2022). Integrated seismic–geological prediction of tectonic coal via main controlling factors. Acta Geophysica, 70, 1–18.

    Article  Google Scholar 

  • Lu, J., Wang, Y., & Chen, J. (2018). Detection of tectonically deformed coal using model-based joint inversion of multi-component seismic data. Energies, 11(4), 829.

    Article  Google Scholar 

  • Ma, L., Chen, T., Wang, X., & Ma, G. (2018). Recent progress of quantitative prediction of tectonic coal thickness. Coal Geology & Exploration, 46(5), 66–72. (in Chinese with English abstract).

    Google Scholar 

  • Mandal, M., & Mukhopadhyay, A. (2013). An improved minimum redundancy maximum relevance approach for feature selection in gene expression data. Procedia Technology, 10, 20–27.

    Article  Google Scholar 

  • Meng, Q., Ma, X., & Zhou, Y. (2014). Forecasting of coal seam gas content by using support vector regression based on particle swarm optimization. Journal of Natural Gas Science and Engineering, 21, 71–78.

    Article  Google Scholar 

  • Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238.

    Article  Google Scholar 

  • Peng, L. Y., Cui, R. F., Ren, C., & Cui, D. W. (2013a). Classification of coal body structure using seismic lithology inversion information. Journal of China Coal Society, 38(2), 410–415. (in Chinese with English abstract).

    Google Scholar 

  • Peng, L., Cui, R., Ren, C., & Cui, D. (2013b). Utilizing seismic inversion information in classifying coal structures. Journal of China Coal Society, 39(4), 69–73. (in Chinese with English abstract).

    Google Scholar 

  • Qin, Y., & Gao, D. (2012). Prediction of evaluation of coalbed methane resources potential in Guizhou Province (p. 263). Xuzhou: China University of Mining and Technology Press. (in Chinese).

    Google Scholar 

  • Rodriguez, J. D., Perez, A., & Lozano, J. A. (2009). Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 569–575.

    Article  Google Scholar 

  • Sang, S., Xu, H., Fang, L., Li, G., & Huang, H. (2010). Stress relief coalbed methane drainage by surface vertical wells in China. International Journal of Coal Geology, 82(3–4), 196–203.

    Article  Google Scholar 

  • Sang, S., Zhou, Z., Liu, S., Wang, H., Cao, L., Liu, H., Li, Z., Zhu, S., Liu, C., Huang, H., Xu, H., Wang, R., Jiang, J., Ashutosh, T., & Han, S. (2020). Research advances in theory and technology of the stress release applied extraction of coalbed methane from tectonically deformed coals. Journal of China Coal Society, 45(7), 2531–2543. (in Chinese with English abstract).

    Google Scholar 

  • Shi, J., Zeng, L., Dong, S., Wang, J., & Zhang, Y. (2020). Identification of coal structures using geophysical logging data in Qinshui Basin, China: Investigation by kernel Fisher discriminant analysis. International Journal of Coal Geology, 217, 103314.

    Article  Google Scholar 

  • Shuck, E. L., Davis, T. L., & Benson, R. D. (1996). Multicomponent 3-D characterization of a coalbed methane reservoir. Geophysics, 61(2), 315–330.

    Article  Google Scholar 

  • Skoczylas, N., Dutka, B., & Sobczyk, J. (2014). Mechanical and gaseous properties of coal briquettes in terms of outburst risk. Fuel, 134, 45–52.

    Article  Google Scholar 

  • Song, C., Zhi, S., Feng, G., & Lin, J. (2021). Enhancing potential of hydrofracturing in mylonitic coal by biocementation. Energy Science & Engineering, 9(4), 565–576.

    Article  Google Scholar 

  • Sun, X., Cui, R., Mao, X., & Tian, Z. (2011). Elastic impedance inversion associated with simutaneous inversion in determining the distribution of tectonic coal. Journal of China Coal Society, 36(5), 778–783. (in Chinese with English abstract).

    Google Scholar 

  • Tao, C., Wang, Y., Ni, X., Zhang, C., & Ma, T. (2017). Prediction model of coal-body structure and spatial distibution law based on logging parameters. Coal Science and Technology, 45(2), 173–177. (in Chinese with English abstract).

    Google Scholar 

  • Vaziri, H. H., & Palmer, I. D. (1998). Evaluation of openhole cavity completion technique in coalbed methane reservoirs. International Journal of Rock Mechanics and Mining Sciences, 4(35), 523–524.

    Article  Google Scholar 

  • Wang, H., Li, J., & Yang, F. (2014). Overview of support vector machine analysis and algorithm. Application Research of Computers, 31(5), 1281–1286. (in Chinese with English abstract).

    Google Scholar 

  • Wang, X., Chen, T., & Xu, H. (2020). Thickness distribution prediction for tectonically deformed coal with a deep belief network: A case study. Energies, 13(5), 1169.

    Article  Google Scholar 

  • Wang, X., Li, Y., Chen, T., Yan, Q., & Ma, L. (2017). Quantitative thickness prediction of tectonically deformed coal using Extreme Learning Machine and Principal Component Analysis: A case study. Computers & Geosciences, 101, 38–47.

    Article  Google Scholar 

  • Wang, Y., Liu, D., Cai, Y., Yao, Y., & Zhou, Y. (2018). Evaluation of structured coal evolution and distribution by geophysical logging methods in the Gujiao Block, northwest Qinshui basin, China. Journal of Natural Gas Science and Engineering, 51, 210–222.

    Article  Google Scholar 

  • Xiao, C., Chen, Z., & Jin, X. (2021). Coal structure model and fracturing effect of Yanchuannan coalbed gas field. Coal Science and Technology, 49(11), 38–46. (in Chinese with English abstract).

    Google Scholar 

  • Xiao, Z., Jiang, W., Sun, B., Cao, Y., Jiang, L., Cao, T., Yang, Q., Huang, C., Yang, X., & Huang, X. (2020). Quantitative identification of coal texture using the support vector machine with geophysical logging data: A case study using medium-rank coal from the Panjiang, Guizhou. China. Interpretation, 8(4), 753–762.

    Article  Google Scholar 

  • Xu, H., Sang, S., Fang, L., Huang, H., & Ren, B. (2011). Failure characteristics of surface vertical wells for relieved coal gas and their influencing factors in Huainan mining area. Mining Science and Technology (China), 21(1), 83–88.

    Article  Google Scholar 

  • Xu, H., Sang, S., Yang, J., & Chen, J. (2016a). Status and expectation on coalbed methane exploration and development in Guizhou Province. Coal Science and Technology, 44(2), 1–7. (in Chinese with English abstract).

    Google Scholar 

  • Xu, J., Zhang, X., & Li, Y. (2004). Advances in support vector machines. Control and Decision, 19(5), 481–484. (in Chinese with English abstract).

    Google Scholar 

  • Xu, Q., Huang, W., Yang, Y., Liu, B., Feng, X., & Lu, X. (2016b). Analysis of Identifying Deformed Coal by Logging Curve in Shizhuang North Block, Qinshui Basin. China. Science Technology and Engineering, 16(3), 11–16. (in Chinese with English abstract).

    Google Scholar 

  • Yang, H., Pan, H., Wu, A., Luo, M., Konaté, A. A., & Meng, Q. (2017). Application of well logs integration and wavelet transform to improve fracture zones detection in metamorphic rocks. Journal of Petroleum Science and Engineering, 157, 716–723.

    Article  Google Scholar 

  • Yang, Z., Li, Y., Qin, Y., Sun, H., Zhang, P., Zhang, Z., Wu, C., Li, C., & Chen, C. (2019). Development unit division and favorable area evaluation for joint mining coalbed methane. Petroleum Exploration and Development, 46(3), 583–593.

    Article  Google Scholar 

  • Yang, Z., Qin, Y., Qin, Z., Yi, T., Li, C., & Zhang, Z. (2020). Characteristics of dissolved inorganic carbon in produced water from coalbed methane wells and its geological significance. Petroleum Exploration and Development, 47(5), 1074–1083.

    Article  Google Scholar 

  • Yao, J., Sima, L., & Zhang, Y. (2011). Quantitative identification of deformed coals by geophysical logging. Journal of China Coal Society, S1, 94–98. (in Chinese with English abstract).

    Google Scholar 

  • Yao, Z., Cao, D., Wei, Y., Li, X., Wang, X., & Zhang, X. (2016). Experimental analysis on the effect of tectonically deformed coal types on fines generation characteristics. Journal of Petroleum Science and Engineering, 146, 350–359.

    Article  Google Scholar 

  • Yuan, L. (2011). Theories and techniques of coal bed methane control in China. Journal of Rock Mechanics and Geotechnical Engineering, 3(4), 343–351.

    Article  Google Scholar 

  • Zhang, B., Sun, H., Liang, Y., Wang, K., & Zou, Q. (2020). Characterization and quantification of mining-induced fractures in overlying strata: Implications for coalbed methane drainage. Natural Resources Research, 29(4), 2467–2480.

    Article  Google Scholar 

  • Zhang, X., Du, Z., & Li, P. (2017). Physical characteristics of high-rank coal reservoirs in different coal-body structures and the mechanism of coalbed methane production. Science China Earth Sciences, 60(2), 246–255.

    Article  Google Scholar 

  • Zhang, Z., Qin, Y., Wang, G., Sun, H., You, Z., Jin, J., & Yang, Z. (2021). Evaluation of coal body structures and their distributions by geophysical logging methods: Case study in the Laochang block, eastern Yunnan. China. Natural Resources Research, 30(3), 2225–2239.

    Article  Google Scholar 

  • Zhao, L., Cui, R., & Peng, L. (2013). Delineating deformed coal development zones with lithological inversion methods based on logging curve reconstruction. Safety in Coal Mines, 44(8), 8–10. (in Chinese with English abstract).

    Google Scholar 

  • Zhao, Y., Shi, Z., Hao, S., Liu, J., Yang, Z., & Wang, S. (2014). Well completion technology using screen pipe for horizontally-intersected well in soft coal seam. Procedia Engineering, 73, 311–317.

    Article  Google Scholar 

  • Zhou, Z. H., & Liu, X. Y. (2005). Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 18(1), 63–77.

    Article  Google Scholar 

Download references

Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (No. 41727801), the Geological Exploration Foundation of Guizhou Province (No. 208-9912-JBN-UTSO), and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shuxun Sang.

Ethics declarations

Conflict of Interest

No conflict of interest exists in the submission of this manuscript, and the manuscript has been approved by all authors for publication. The authors declare that the work described is original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 799 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shu, Y., Sang, S., Zhou, X. et al. Coal Body Structure Detection Based on Logging and Seismic Data and Its Impacts on Coalbed Methane Development: A Case Study in the Dahebian Block, Western Guizhou, Southern China. Nat Resour Res 32, 691–716 (2023). https://doi.org/10.1007/s11053-023-10168-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-023-10168-6

Keywords

Navigation