Pore size distribution of high volatile bituminous coal of the southern Junggar Basin: a full-scale characterization applying multiple methods

doi:10.1007/s11707-020-0845-x

Front. Earth Sci.

2021, Vol. 15

Issue (2) : 237-255 https://doi.org/10.1007/s11707-020-0845-x

RESEARCH ARTICLE

Pore size distribution of high volatile bituminous coal of the southern Junggar Basin: a full-scale characterization applying multiple methods

Wanchun ZHAO¹, Xin LI^1,²(

), Tingting WANG^1,³, Xuehai FU^2,⁴

¹. Key Laboratory of Continental Shale Accumulation and Development (Ministry of Education), Northeast Petroleum University, Daqing 163000, China
². School of Geology and Mining Engineering, Xinjiang University, Urumqi 830047, China
³. School of Electrical Engineering and Information, Northeast Petroleum University, Daqing 163000, China
⁴. School of Resources & Earth Science, China University of Mining & Technology, Xuzhou 221008, China

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Abstract

Studying on the pore size distribution of coal is vital for determining reasonable coalbed methane development strategies. The coalbed methane project is in progress in the southern Junggar Basin of northwestern China, where high volatile bituminous coal is reserved. In this study, with the purpose of accurately characterizing the full-scale pore size distribution of the high volatile bituminous coal of the southern Junggar Basin, two grouped coal samples were applied for mercury intrusion porosimetry, low-temperature nitrogen adsorption, low-field nuclear magnetic resonance, rate-controlled mercury penetration, scanning electron microscopy, and nano-CT measurements. A comprehensive pore size distribution was proposed by combining the corrected mercury intrusion porosimetry data and low-temperature nitrogen adsorption data. The relationship between transverse relaxation time (T₂, ms) and the pore diameter was determined by comparing the T₂ spectrum with the comprehensive pore size distribution. The macro-pore and throat size distributions derived from nano-CT and rate-controlled mercury penetration were distinguishingly analyzed. The results showed that: 1) comprehensive pore size distribution analysis can be regarded as an accurate method to characterize the pore size distribution of high volatile bituminous coal; 2) for the high volatile bituminous coal of the southern Junggar Basin, the meso-pore volume was the greatest, followed by the transition pore volume or macro-pore volume, and the micro-pore volume was the lowest; 3) the relationship between T₂ and the pore diameter varied for different samples, even for samples with close maturities; 4) the throat size distribution derived from nano-CT was close to that derived from rate-controlled mercury penetration, while the macro-pore size distributions derived from those two methods were very different. This work can deepen the knowledge of the pore size distribution characterization techniques of coal and provide new insight for accurate pore size distribution characterization of high volatile bituminous coal.

Keywords pore size distribution coalbed methane high volatile bituminous coal low field nuclear magnetic resonance the southern Junggar Basin

Corresponding Author(s): Xin LI

Online First Date: 16 December 2020 Issue Date: 26 October 2021

Cite this article:

Wanchun ZHAO,Xin LI,Tingting WANG, et al. Pore size distribution of high volatile bituminous coal of the southern Junggar Basin: a full-scale characterization applying multiple methods[J]. Front. Earth Sci., 2021, 15(2): 237-255.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-020-0845-x
https://academic.hep.com.cn/fesci/EN/Y2021/V15/I2/237

Fig.1 (a) Sampling sites in the SJR (after Zhou et al. (2016)); (b) Generalized coal-bearing stratigraphic column for the SJR (after Tian and Yang (2011)).

Fig.2 The workflow diagram of this study.

Fig.3 Pictures of all the experimental facilities: (a) Autopore IV 9500 mercury injection apparatus; (b) automatic specific surface and pore analyzer Tristar II3020; (c) LF-NMR device RecCore-2500; (d) CMP device-APSE730; (e) double beam electron microscope system FEI Helio 650; and (f) NanoVoxel-3502 series X-ray three-dimensional microscope.

Tab.1 Results of the proximate analysis and coal composition of the coal samples

Tab.2 Specific experimental condition of each test.

Fig.4 PSDs of the WD sample (a) and KG sample (b) derived from MIP and LTNA.

Fig.5 T₂ spectra of the WD and KG samples.

Tab.3 Parameter information of the S-IV samples applied for CMP experiment

Fig.6 Pore and throat size distributions of the (a) WD sample and (b) KG sample, derived from the CMP data.

Fig.7 Pore-throat diameter ratios and permeability contributions of the throats of the (a) WD sample (b) and KG sample.

Fig.8 SEM map of the WD sample (a–c) and KG sample (d–f).

Fig.9 Micro-CT scanning results of sample WD S-VI: (a) 2D CT slice; (b) detailed description of the 2D slice; (c) 3D structure demonstration; (d) 3D structure of the connected macro-pores; (e) 3D pore-throat skeletal structure; (f) 3D structure demonstration of the connected and unconnected macro-pores; (g) 3D distribution of the isolated macro-pores marked by different colors; and (h) macro-pore and throat size distributions.

Fig.10 Corrected and uncorrected cumulative pore volume curves derived from the MIP data: (a) WD sample; (b) KG sample.

Fig.11 Corrected and uncorrected PSDs derived from the MIP data: (a) WD sample; (b) KG sample.

Fig.12 CPSDs derived from the MIP and LTNA data: (a) WD sample; (b) KG sample.

Fig.13 Cumulative pore volume frequencies derived from the CPSD data and LF-NMR data: (a) WD sample; (b) KG sample.

Fig.14 PSDs derived from the LF-NMR data after transferring T₂ to the pore diameter and the PSDs derived from the CPSD data: (a) WD sample; (b) KG sample.

Fig.15 (a) Throat size distributions of the WD sample derived by nano-CT and CMP metnods; (b) Pore size distributions of the WD sample derived by nano-CT and CMP metnods.

Fig.16 Typical PSDs of HVBC derived by combining the LTNA data and uncorrected MIP data published in the work by (a) Tao et al. (2018) and (b) Lin et al. (2019).

Fig.17 PSDs of HVBC derived by combining the LTNA data and corrected MIP data published in Wang et al. (2017).

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