3D reconstruction of coal pore network and its application in CO<sub>2</sub>-ECBM process simulation at laboratory scale

doi:10.1007/s11707-021-0944-3

Front. Earth Sci.

2022, Vol. 16

Issue (2) : 523-539 https://doi.org/10.1007/s11707-021-0944-3

RESEARCH ARTICLE

3D reconstruction of coal pore network and its application in CO₂-ECBM process simulation at laboratory scale

Huihuang FANG^1,^2,³(

), Hongjie XU^3,^1,²(

), Shuxun SANG^4,⁵, Shiqi Liu^4,⁵, Shuailiang SONG⁶, Huihu LIU^1,²

¹. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China
². School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China
³. Institute of Energy, Hefei Comprehensive National Science Center, Hefei 230000, China
⁴. Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization, China University of Mining and Technology, Xuzhou 221008, China
⁵. Low Carbon Energy Institute, China University of Mining and Technology, Xuzhou 221008, China
⁶. Shandong Provincial Lunan Geo-engineering Exploration Institute, Jining 272100, China

Download: PDF(13327 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Three-dimensional (3D) reconstruction of the equivalent pore network model (PNM) using X-ray computed tomography (CT) data are of significance for studying the CO₂-enhanced coalbed methane recovery (CO₂-ECBM). The docking among X-ray CT technology, MATLAB, with COMSOL software not only can realize the 3D reconstruction of PNM, but also the CO₂-ECBM process simulation. The results show that the Median filtering algorithm enabled the de-noising of the original 2D CT slices, the image segmentation of all slices was realized based on the selected threshold, and the PNM can be constructed based on the Maximum Sphere algorithm. The mathematical model of CO₂-ECBM process fully coupled the expanded Langmuir equation. At the same time for CO₂ injection, CH₄ pressure tends to decrease with the increase of CO₂ pressure, but its difference is not obvious. The CH₄ pressure in the slice center changed a lot, while at the edge it changed a little under different CO₂ pressures. The injected CO₂ was transported to matrix along the macro and micro-fractures with continuous flow. The injected CO₂ first replaced the adsorbed CH₄ by covering the inner surface of macro-pores and meso-pores to form the single molecular layer adsorption of CO₂. Then they migrated to micro-pores by Fick’s diffusion, sliding flow, and surface diffusion. Furthermore, the CO₂ replaced CH₄ adsorbed by volumetric filling in micro-pores, and formed the multi-molecular layer adsorption of CO₂. The gas pressure and migration path between CO₂ and CH₄ are opposite. This study can provide a theoretical basis for studying digital rock physics technology and enrich the development of CO₂-ECBM technology.

Keywords CO₂-ECBM 3D reconstruction numerical simulation X-ray CT COMSOL Qinshui Basin

Corresponding Author(s): Huihuang FANG,Hongjie XU

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Online First Date: 28 September 2021 Issue Date: 29 August 2022

Cite this article:

Huihuang FANG,Hongjie XU,Shuxun SANG, et al. 3D reconstruction of coal pore network and its application in CO₂-ECBM process simulation at laboratory scale[J]. Front. Earth Sci., 2022, 16(2): 523-539.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0944-3
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I2/523

Fig.1 General research idea of numerical simulation for CO₂-ECBM process based on the equivalent pore and fracture network model. (Note: File with the suffix STL plays a bridge between communication MATLAB and COMSOL software).

Fig.2 Geological setting of the Qinshui Basin. (a) Four uplift belts in the Qinshui Basin (Modified from Cai et al., 2011), and (b) Anti-oxidation treatment of coal sample.

Tab.1 Key properties of coal sample used in this study

Fig.3 X-ray CT scanning system. (a) Preparation of sample; (b) imaging system components; (c) typical 2D CT slices of BF coal sample.

Fig.4 Sketch of the Maximum Ball algorithm.

Tab.2 Numerical simulation schemes of CO₂-ECBM process on the laboratory scale

Fig.5 Loading diagram of CH₄ and CO₂ boundary conditions during CO₂-ECBM process. (a) Initial conditions; (b) CO₂-ECBM process.

Tab.3 Numerical simulation parameters of CO₂-ECBM process on the laboratory scale

Fig.6 Construction flow chart of the CO₂-ECBM simulation system based on the COMSOL and MATLAB software.

Fig.7 Comparison of images before and after the Median Filtration. (a) Before Median Filtration; (b) after Median Filtration.

Fig.8 Threshold selection and image segmentation. (a) Typical 2D CT slices; (b) threshold selection and image segmentation (1. greyscale distribution; 2. threshold selection of pore and matrix; 3. threshold selection of organic matter and inorganic mineral; 4. before segmentation; 5. segmentation of pore and matrix; 6. segmentation of organic matter and inorganic mineral); (c) 3D reconstruction of BF sample.

Fig.9 Schematic diagram of REV analysis and extraction of pore and fracture network model. (a) REV analysis (1. original 2D CT slice; 2. REV size; 3. relationship between porosity and REV size); (b) Application of Maximum Ball Algorithm; (c) Extraction of pore and throat in pore and fracture network model.

Fig.10 P retreatment of geological model. (a) Interconnected pore and fracture network model; (b) surface detail repair of pore geometry model (1. editing a triangle with the Translate Vertex tool to improve model quality; 2. editing a triangle with the Contract Edges tool to improve model quality); (c) tetrahedral mesh without error.

Fig.11 3D distribution of gas pressure field during the CO₂-ECBM process. (a) CH₄; (b) CO₂.

Fig.12 2D distribution of gas pressure field during the CO₂-ECBM process.

Fig.13 Relationship curves of gas pressure and time at different position during the CO₂-ECBM process. (a) CO₂ pressure and time curves; (b) CH₄ pressure and time curves.

Fig.14 Effect of CO₂ pressure injected on CO₂ pressure field during the CO₂-ECBM process. (a) 1.2 times CO₂ pressure injected; (b) 2.4 times CO₂ pressure injected; (c) 3.6 times CO₂ pressure injected; (d) 4.8 times CO₂ pressure injected; (e) 6.0 times CO₂ pressure injected.

Fig.15 2D distribution of CO₂ pressure under different CO₂ pressures injected during the CO₂-ECBM process (the 15th slice). (a) 1.2 times CO₂ pressure injected; (b) 2.4 times CO₂ pressure injected; (c) 3.6 times CO₂ pressure injected; (d) 4.8 times CO₂ pressure injected; (e) 6.0 times CO₂ pressure injected.

Fig.16 CO₂ pressure distribution of point B under different CO₂ pressure injected during the CO₂-ECBM process.

Fig.17 Schematic diagram of CO₂-ECBM continuity process.

Fig.18 Schematic diagram of CH₄ and CO₂ occupying adsorption sites. (a) Initial state of CH₄ in reservoir; (b) after CO₂ injection.

Fig.19 Fluid dynamic characteristics of the CO₂-ECBM process (Modified from CERVIK, 1967).

Fig.20 Abstract schematic diagram of the Double Pore media of the coal reservoir (Wang et al., 2018a, 2018b). (a) Actual coal reservoir; (b) abstract coal reservoir; (c) schematic diagram of pore and fracture.

1	Y D Cai, D M Liu, Y B Yao, J G Li, Y K Qiu (2011). Geological controls on prediction of coalbed methane of No. 3 coal seam in Southern Qinshui Basin, North China. Int J Coal Geol, 88(2–3): 101–112 https://doi.org/10.1016/j.coal.2011.08.009
2	T T Cai, Z C Feng, D Zhou (2018). Multi-scale characteristics of coal structure by X-ray computed tomography (X-ray CT), scanning electron microscope (SEM) and mercury intrusion porosimetry (MIP). AIP Adv, 8(2): 025324 https://doi.org/10.1063/1.5021699
3	J Cervik (1967). Behavior of coal-gas reservoirs. In: SPE Eastern Regional Meeting
4	G X Cheng, B Jiang, M Li, F L Li, Y Song (2020). Effects of pore-fracture structure of ductile tectonically deformed coals on their permeability: an experimental study based on raw coal cores. J Petrol Sci Eng, 193: 107371 https://doi.org/10.1016/j.petrol.2020.107371
5	C R Clarkson, R M Bustin (1999). The effect of pore structure and gas pressure upon the transport properties of coal: a laboratory and modeling study. Fuel, 78(11): 1345–1362 https://doi.org/10.1016/S0016-2361(99)00056-3
6	L D Connell, S Mazumder, R Sander, M Camilleri, Z J Pan, D Heryanto (2016). Laboratory characterization of coal matrix shrinkage, cleat compressibility and the geo-mechanical properties determining reservoir permeability. Fuel, 165: 499–512 https://doi.org/10.1016/j.fuel.2015.10.055
7	N Fan, J R Wang, C B Deng, Y P Fan, T T Wang, X Y Guo (2020). Quantitative characterization of coal microstructure and visualization seepage of macropores using CT-based 3D reconstruction. J Nat Gas Sci Eng, 81: 103384 https://doi.org/10.1016/j.jngse.2020.103384
8	H H Fang, S X Sang, J L Wang, S Q Liu, W Ju (2017). Simulation of paleotectonic stress fields and distribution prediction of tectonic fractures at the Hudi Coal Mine, Qinshui Basin. Acta Geol Sin-Engl, 91(6): 2007–2023 https://doi.org/10.1111/1755-6724.13447
9	H H Fang, S X Sang, S Q Liu (2019a). Establishment of dynamic permeability model of coal reservoir and its numerical simulation during the CO2-ECBM process. J Petrol Sci Eng, 179: 885–898 https://doi.org/10.1016/j.petrol.2019.04.095
10	H H Fang, S X Sang, S Q Liu, Y Du (2019b). Methodology of three-dimensional visualization and quantitative characterization of nanopores in coal by using FIB-SEM and its application with anthracite in Qinshui Basin. J Petrol Sci Eng, 182: 106285 https://doi.org/10.1016/j.petrol.2019.106285
11	H H Fang, S X Sang, S Q Liu (2019c). The coupling mechanism of the thermal-hydraulic-mechanical fields in CH4-bearing coal and its application in the CO2-enhanced coalbed methane recovery. J Petrol Sci Eng, 181: 106177 https://doi.org/10.1016/j.petrol.2019.06.041
12	H H Fang, S X Sang, S Q Liu (2019d). Numerical simulation of enhancing coalbed methane recovery by injecting CO2 with heat injection. Petrol Sci, 16(1): 32–43 https://doi.org/10.1007/s12182-018-0291-5
13	H H Fang, S X Sang, S Q Liu, S P Liu (2019e). Experimental simulation of replacing and displacing CH4 by injecting supercritical CO2 and its geological significance. Int J Greenh Gas Control, 81: 115–125 https://doi.org/10.1016/j.ijggc.2018.12.015
14	H H Fang, S X Sang, S Q Liu (2020). Three-dimensional spatial structure of the macro-pores and flow simulation in anthracite coal based on X-ray mu-CT scanning data. Petrol Sci, 17(5): 1221–1236 https://doi.org/10.1007/s12182-020-00485-3
15	A Fick (1855). On liquid diffusion. Philos Mag, 10(63): 30–39 https://doi.org/10.1080/14786445508641925
16	S P Huang, D M Liu, Y D Cai, Q Gan (2019). In situ stress distribution and its impact on CBM reservoir properties in the Zhengzhuang area, southern Qinshui Basin, North China. J Nat Gas Sci Eng, 61: 83–96 https://doi.org/10.1016/j.jngse.2018.11.005
17	J Lei, B Z Pan, L H Zhang (2018). Advance of microscopic flow simulation based on digital cores and pore network. Prog Geophys, 33(2): 653–660 (in Chinese)
18	Y Li, C Zhang, D Tang, Q Gan, X Niu, K Wang, R Shen (2017). Coal pore size distributions controlled by the coalification process: an experimental study of coals from the Junggar, Ordos, and Qinshui basins in China. Fuel, 206: 352–363 https://doi.org/10.1016/j.fuel.2017.06.028
19	Z W Li, G Y Zhang (2019). Fracture segmentation method based on contour evolution and gradient direction consistency in sequence of coal rock CT images. Math Probl Eng, 1: 1–82980747 https://doi.org/10.1155/2019/2980747
20	Y Li, Y Wang, J Wang, Z Pan (2020a). Variation in permeability during CO2-CH4 displacement in coal seams: part I-Experimental insights. Fuel, 263: 116666 https://doi.org/10.1016/j.fuel.2019.116666
21	Y Li, J Yang, Z Pan, W Tong (2020b). Nanoscale pore structure and mechanical property analysis of coal: an insight combining AFM and SEM images. Fuel, 260: 116352 https://doi.org/10.1016/j.fuel.2019.116352
22	S Q Liu, S X Sang, G Wang, J S Ma, T Wang, W Wang, Y Du, T Wang (2017). FIB-SEM and X-ray CT characterization of interconnected pores in high-rank coal formed from regional metamorphism. J Petrol Sci Eng, 148: 21–31 https://doi.org/10.1016/j.petrol.2016.10.006
23	G H Ni, S Li, S Rahman, M Xun, H Wang, Y H Xu, H C Xie (2020a). Effect of nitric acid on the pore structure and fractal characteristics of coal based on the low-temperature nitrogen adsorption method. Powder Technol, 367: 506–516 https://doi.org/10.1016/j.powtec.2020.04.011
24	X M Ni, Z Zhao, Y B Wang, L Wang (2020b). Optimisation and application of well types for ground development of coalbed methane from No. 3 coal seam in shizhuang south block in Qinshui Basin, Shanxi Province, China. J Petrol Sci Eng, 193: 107453 https://doi.org/10.1016/j.petrol.2020.107453
25	B S Nie, J Y Lun, K D Wang, J S Shen (2020). Three-dimensional characterization of open and closed coal nanopores based on a multi-scale analysis including CO2 adsorption, mercury intrusion, low-temperature nitrogen adsorption, and small-angle X-ray scattering. Energy Sci Eng, 8(6): 2086–2099 https://doi.org/10.1002/ese3.649
26	J Q Shi, S Durucan (2005). A model for changes in coalbed permeability during primary and enhanced methane recovery. SPE Reservoir Eval Eng, 8(04): 291–299 https://doi.org/10.2118/87230-PA
27	J Q Shi, S Mazumder, K H Wolf, S Durucan (2008). Competitive methane desorption by supercritical CO2 injection in coal. Transp Porous Media, 75(1): 35–54 https://doi.org/10.1007/s11242-008-9209-9
28	D Silin, T Patzek (2006). Pore space morphology analysis using maximal inscribed spheres. Physica A, 371(2): 336–360 https://doi.org/10.1016/j.physa.2006.04.048
29	H Singh (2017). Representative Elementary Volume (REV) in spatio-temporal domain: a method to find REV for dynamic pores. J Earth Sci, 28(2): 391–403 https://doi.org/10.1007/s12583-017-0726-8
30	Y F Sun, Y X Zhao, L Yuan (2018). CO2-ECBM in coal nanostructure: modelling and simulation. J Nat Gas Sci Eng, 54: 202–215 https://doi.org/10.1016/j.jngse.2018.04.007
31	B Vik, E Bastesen, A Skauge (2013). Evaluation of representative elementary volume for a vuggy carbonate rock-Part: porosity, permeability, and dispersivity. J Petrol Sci Eng, 112(3): 36–47 https://doi.org/10.1016/j.petrol.2013.03.029
32	G Wang, K Wang, Y J Jiang, S Q Wang (2018a). Reservoir permeability evolution during the process of CO2-enhanced coalbed methane recovery. Energies, 11(11): 2996 https://doi.org/10.3390/en11112996
33	G Wang, K Wang, S G Wang, D Elsworth, Y J Jiang (2018b). An improved permeability evolution model and its application in fractured sorbing media. J Nat Gas Sci Eng, 56: 222–232 https://doi.org/10.1016/j.jngse.2018.05.038
34	X M Wang, X M Wang, Z J Pan, X B Yin, P C Chai, S D Pan, Q Yang (2019). Abundance and distribution pattern of rare earth elements and yttrium in vitrain band of high-rank coal from the Qinshui Basin, northern China. Fuel, 248: 93–103 https://doi.org/10.1016/j.fuel.2019.03.054
35	G Wang, D Y Han, C H Jiang, Z Y Zhang (2020a). Seepage characteristics of fracture and dead-end pore structure in coal at micro- and meso-scales. Fuel, 266: 117058 https://doi.org/10.1016/j.fuel.2020.117058
36	M F Wang, J J Wang, S Tao, D Z Tang, C C Wang, J Yi (2020b). Quantitative characterization of void and demineralization effect in coal based on dual-resolution X-ray computed tomography. Fuel, 267: 116836 https://doi.org/10.1016/j.fuel.2019.116836
37	H Wang, Y B Yao, C C Huang, D M Liu, Y D Cai (2021). Fault development characteristics and their effects on current gas content and productivity of No.3 coal seam in the Zhengzhuang Field, southern Qinshui Basin, north China. Energ Fuels, 35(3): 2268–2281 https://doi.org/10.1021/acs.energyfuels.0c04149
38	C Yuan, B Chareyre, F Darve (2016). Pore-scale simulations of drainage in granular materials: finite size effects and the representative elementary volume. Adv Water Resour, 95: 109–124 https://doi.org/10.1016/j.advwatres.2015.11.018
39	Y X Zhang, Q Yuan, D Huang, S F Kong, J Zhang, X F Wang, C Y Lu, Z B Shi, X Y Zhang, Y L Sun, Z F Wang, L Y Shao, J H Zhu, W J Li (2018). Direct observations of fine primary particles from residential coal burning: insights into their morphology, composition, and hygroscopicity. J Geophys Res Atmos, 123(22): 12964–12979 https://doi.org/10.1029/2018JD028988
40	K Zhang, S X Sang, X Z Zhou, C J Liu, M Y Ma, Q H Niu (2021). Influence of supercritical CO2-H2O-caprock interactions on the sealing capability of deep coal seam caprocks related to CO2 geological storage: a case study of the silty mudstone caprock of coal seam No. 3 in the Qinshui Basin, China. Int J Greenh Gas Control, 106: 103282 https://doi.org/10.1016/j.ijggc.2021.103282
41	S J Zheng, Y B Yao, D Elsworth, D M Liu, Y D Cai (2020). Dynamic fluid interactions during CO2-ECBM and CO2 sequestration in coal seams. Part 2: CO2-H2O wettability. Fuel, 279: 118560 https://doi.org/10.1016/j.fuel.2020.118560
42	Q Z Zhu, X L Wang, Y Q Zuo, J N Pan, Y W Ju, X F Su, K Yu (2020). Numerical simulation of matrix swelling and its effects on fracture structure and permeability for a high-rank coal based on X-ray micro-CT image processing techniques. Energ Fuels, 34(9): 10801–10809 https://doi.org/10.1021/acs.energyfuels.0c01903

[1]	Jun LIU, Ye ZHANG, Lijun CHENG, Zhaohui LU, Chunlin ZENG, Peng ZHAO. Numerical modeling of the dynamic variation in multiphase CH₄ during CO₂ enhanced gas recovery from depleted shale reservoirs[J]. Front. Earth Sci., 2021, 15(4): 790-802.
[2]	Pingzhi FANG, Deqian ZHENG, Liang LI, Wenyong MA, Shengming TANG. Numerical and experimental study of the aerodynamic characteristics around two-dimensional terrain with different slope angles[J]. Front. Earth Sci., 2019, 13(4): 705-720.
[3]	Rui XING,Zhiying DING,Sangjie YOU,Haiming XU. Relationship of tropical-cyclone-induced remote precipitation with tropical cyclones and the subtropical high[J]. Front. Earth Sci., 2016, 10(3): 595-606.
[4]	Shou MA,Jianchun GUO,Lianchong LI,Leslie George THAM,Yingjie XIA,Chun’an TANG. Influence of pore pressure on tensile fracture growth in rocks: a new explanation based on numerical testing[J]. Front. Earth Sci., 2015, 9(3): 412-426.
[5]	Xinwen LI,Yongming SHEN. Numerical simulation of the impacts of water level variation on water age in Dahuofang Reservoir[J]. Front. Earth Sci., 2015, 9(2): 209-224.
[6]	Lianchong LI,Shaohua LI,Chun’an TANG. Fracture spacing behavior in layered rocks subjected to different driving forces: a numerical study based on fracture infilling process[J]. Front. Earth Sci., 2014, 8(4): 472-489.
[7]	Da AN, Yonghai JIANG, Beidou XI, Zhifei MA, Yu YANG, Queping YANG, Mingxiao LI, Jinbao ZHANG, Shunguo BAI, Lei JIANG. Analysis for remedial alternatives of unregulated municipal solid waste landfills leachate-contaminated groundwater[J]. Front Earth Sci, 2013, 7(3): 310-319.
[8]	Kyungjeen PARK, X. ZOU, Gang LI. A numerical study on rapid intensification of Hurricane Charley (2004) near landfall[J]. Front. Earth Sci., 2009, 3(4): 457-470.

Viewed

Full text

Abstract

Cited

Shared

Discussed