The spatial complexity of oil shale systems is manifested by microstructure, pore space randomness and extensive heterogeneity. A microwave pyrolysis device developed for this study was used to pyrolyze oil shale, and the microstructure before and after pyrolysis was visually examined and quantified. The internal structure of the rock and the extent of pore and fracture expansion are more accurately determined in this way. The microstructure of oil shale at different temperatures before and after microwave pyrolysis is identified by X-ray microcomputed tomography (μCT) with automatic ultra-high-resolution scanning electron microscopy (SEM), to observe the heterogeneous state of oil shale on 2D and 3D scales and define the distribution of internal pores and fractures by post-processing μCT visualization. The study found that fractures sized from microns to millimeters along with pore fractures were observed at increasing microwave temperatures. The fractures gradually expanded with increasing temperature in the direction of horizontal or vertical laminae and generated a more connected pore network. The kerogen gradually decreased with a rise in temperature. The porosity increased from 0.26% to 13.69% at the initial temperature. This research is essential for the qualitative as well as quantitative analysis of the internal structure of oil shales under microwave radiation.
1. Russell, P. L. World history and resources of oil shale. Energy Explor. Exploit., 1986, 4(4), 301‒320.
https://doi.org/10.1177/014459878600400404
2. Wang, M., Ma, R., Li, J., Lu, S., Li, C., Guo, Z., Li, Z. Occurrence mechanism of lacustrine shale oil in the Paleogene Shahejie Formation of Jiyang depression, Bohai Bay Basin, China. Pet. Explor. Dev., 2019, 46(4), 833‒846.
https://doi.org/10.1016/S1876-3804(19)60242-9
3. Lu, S., Huang, W., Chen, F., Li, J., Wang, M., Xue, H., Wang, W., Cai, X. Classification and evaluation criteria of shale oil and gas resources: Discussion and application. Pet. Explor. Dev., 2012, 39(2), 268‒276.
https://doi.org/10.1016/S1876-3804(12)60042-1
4. Li, S., Yue, C. Study of pyrolysis kinetics of oil shale. Fuel, 2003, 82(3), 337‒342.
https://doi.org/10.1016/S0016-2361(02)00268-5
5. Li, S., Yue, C. Study of different kinetic models for oil shale pyrolysis. Fuel Process. Technol., 2004, 85(1), 51‒61.
https://doi.org/10.1016/S0378-3820(03)00097-3
6. Kök, M. V., Guner, G., Bagci, S. Laboratory steam injection applications for oil shale fields of Turkey. Oil Shale, 2008, 25(1), 37‒46.
https://doi.org/10.3176/oil.2008.1.05
7. Chanaa, M. B., Lallemant, M., Mokhlisse, A. Pyrolysis of Timahdit, Morocco, oil shales under microwave field. Fuel, 1994, 73(10), 1643‒1649.
https://doi.org/10.1016/0016-2361(94)90145-7
8. Lin, L., Lai, D., Guo, E., Zhang, C., Xu, G. Oil shale pyrolysis in indirectly heated fixed bed with metallic plates of heating enhancement. Fuel,2016, 163, 48‒55.
https://doi.org/10.1016/j.fuel.2015.09.024
9. Wang, G., Liu, S., Yang, D., Fu, M. Numerical study on the in-situ pyrolysis process of steeply dipping oil shale deposits by injecting superheated water steam: A case study on Jimsar oil shale in Xinjiang, China. Energy, 2022, 239, 122182.
https://doi.org/10.1016/j.energy.2021.122182
10. Meisels, R., Toifl, M., Hartlieb, P., Kuchar, F., Antretter, T. Microwave propa-gation and absorption and its thermo-mechanical consequences in heterogeneous rocks. Int. J. Miner. Process., 2015, 135, 40‒51.
https://doi.org/10.1016/j.minpro.2015.01.003
11. Kumar, H., Lester, E., Kingman, S., Bourne, R., Avila, C., Jones, A., Robinson, J., Halleck, P. M., Mathews, J. P. Inducing fractures and increasing cleat apertures in a bituminous coal under isotropic stress via application of microwave energy. Int. J. Coal Geol., 2011, 88(1), 75‒82.
https://doi.org/10.1016/j.coal.2011.07.007
12. Bradhurst, D. H., Worner, H. K. Evaluation of oil produced from the microwave retorting of Australian shales. Fuel, 1996, 75(3), 285‒288.
https://doi.org/10.1016/0016-2361(95)00232-4
13. Jesch, R. L., McLaughlin, R. H. Dielectric measurements of oil shale as functions of temperature and frequency. IEEE Trans. Geosci. Remote Sens., 1984, GE-22(2), 99‒105.
https://doi.org/10.1109/TGRS.1984.350600
14. Hascakir, B., Akin, S. Recovery of Turkish oil shales by electromagnetic heating and determination of the dielectric properties of oil shales by an analytical method. Energy Fuels, 2010, 24(1), 503‒509.
https://doi.org/10.1021/ef900868w
15. Zhang, N., He, M., Zhang, B., Qiao, F., Sheng, H., Hu, Q. Pore structure characte-ristics and permeability of deep sedimentary rocks determined by mercury intrusion porosimetry. J. Earth Sci., 2016, 27(4), 670‒676.
https://doi.org/10.1007/s12583-016-0662-z
16. Wang, G., Jiao, G., Liu, H., Bai, J., Li, S. Variation of the pore structure during microwave pyrolysis of oil shale. Oil Shale, 2010, 27(2), 135‒146.
https://doi.org/10.3176/oil.2010.2.04
17. Kang, Z., Zhao, J., Yang, D., Zhao, Y., Hu, Y. Study of the evolution of micron-scale pore structure in oil shale at different temperatures. Oil Shale, 2017, 34(1), 42‒54.
https://doi.org/10.3176/oil.2017.1.03
18. Han, X., Jiang, X., Yu, L., Cui, Z. Change of pore structure of oil shale particles during combustion. Part 1. Evolution mechanism. Energy Fuels, 2006, 20(6), 2408‒2412.
https://doi.org/10.1021/ef0603277
19. Han, X., Jiang, X., Cui, Z. Change of pore structure of oil shale particles during combustion. 2. Pore structure of oil-shale ash. Energy Fuels,2008, 22(2), 972‒975.
https://doi.org/10.1021/ef700645x
20. Xu, S., Sun, Y., Lü, X., Yang, Q., Li, Q., Wang, Z., Guo, M. Effects of composition and pore evolution on thermophysical properties of Huadian oil shale in retorting and oxidizing pyrolysis. Fuel, 2021, 305, 121565.
https://doi.org/10.1016/j.fuel.2021.121565
21. Wu, W., Wang, G., Liu, G., Dong, X., Yu, J., Pan, J. Micron-nano pore structures and microscopic pore distribution of oil shale in the Shahejie Formation of the Dongying Depression, Bohai Bay basin, using the argon-scanning electron microscope method. J. Nanosci. Nanotechnol., 2021, 21(1), 750‒764.
https://doi.org/10.1166/jnn.2021.18723
22. Bai, B., Elgmati, M., Zhang, H., Wei, M. Rock characterization of Fayetteville shale gas plays. Fuel, 2013, 105, 645‒652.
https://doi.org/10.1016/j.fuel.2012.09.043
23. Bian, H., Xia, Y., Lu, C., Qin, X., Meng, Q., Lu, H. Pore structure fractal characterization and permeability simulation of natural gas hydrate reservoir based on CT images. Geofluids, 2020, Article ID 6934691.
https://doi.org/10.1155/2020/6934691
24. Wang, L., Yang, D., Kang, Z. Evolution of permeability and mesostructure of oil shale exposed to high-temperature water vapor. Fuel, 2021, 290, 119786.
https://doi.org/10.1016/j.fuel.2020.119786
25. Cao, G., Lin, M., Ji, L., Jiang, W., Yang, M. Characterization of pore structures and gas transport characteristics of Longmaxi shale. Fuel, 2019, 258, 116146.
https://doi.org/10.1016/j.fuel.2019.116146
26. Zhang, L., Chen, S., Zhang, C., Fang, X., Li, S. The characterization of bituminous coal microstructure and permeability by liquid nitrogen fracturing based on μCT technology. Fuel, 2020, 262, 116635.
https://doi.org/10.1016/j.fuel.2019.116635
27. Chen, L., He, A., Zhao, J., Kang, Q., Li, Z.-Y., Carmeliet. J., Shikazono, N., Tao, W.-Q. Pore-scale modeling of complex transport phenomena in porous media. Prog. Energy Combust. Sci., 2022, 88, 100968.
https://doi.org/10.1016/j.pecs.2021.100968
28. Hsieh, J., Molthen, R. C., Dawson, C. A., Johnson, R. H. An iterative approach to the beam hardening correction in cone beam CT. Med. Phys.,2000, 27(1), 23‒29.
https://doi.org/10.1118/1.598853
29. Karimpouli, S., Tahmasebi, P., Saenger, E. H. Coal cleat/fracture segmentation using convolutional neural networks. Nat. Resour. Res., 2020, 29(3), 1675‒1685.
https://doi.org/10.1007/s11053-019-09536-y
30. Wang, S., Chen, Z., Zhang, M. Pore and microfracture of coal matrix block and their effects on the recovery of methane from coal. Earth Science-Journal of China University of Geosciences, 1995, 20(5), 557‒561 (in Chinese).
31. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern., 1979, 9(1), 62‒66.
https://doi.org/10.1109/TSMC.1979.4310076
32. Rodriguez, F., Maire, E., Courjault-Radé, P., Darrozes, J. The Black Top Hat function applied to a DEM: A tool to estimate recent incision in a mountainous watershed (Estibère Watershed, Central Pyrenees). Geophys. Res. Lett., 2002, 29(6), 9‒1‒9‒4.
https://doi.org/10.1029/2001GL014412
33. Saif, T., Lin, Q., Bijeljic, B., Blunt, M.J. Microstructural imaging and characte- rization of oil shale before and after pyrolysis. Fuel, 2017, 197, 562‒574.
https://doi.org/10.1016/j.fuel.2017.02.030
34. Dong, H., Blunt, M.J. C. Pore-network extraction from micro-computerized-tomo--graphy images. Phys. Rev. E, 2009, 80(3), 036307.
https://doi.org/10.1103/PhysRevE.80.036307
35. Williams, P. T., Ahmad, N. Influence of process conditions on the pyrolysis of Pakistani oil shales. Fuel, 1999, 78(6), 653‒662.
https://doi.org/10.1016/S0016-2361(98)00190-2
36. Liu, Z., Meng, Q., Dong, Q., Zhu, J., Guo, W., Ye, S., Liu, R., Jia, J. Characteristics and resource potential of oil shale in China. Oil Shale,2017, 34(1), 15‒41.
https://doi.org/10.3176/oil.2017.1.02
37. Wang, Q., Sun, B., Hu, A., Bai, J., Li, S. Pyrolysis characteristics of Huadian oil shales. Oil Shale, 2007, 24(2), 147‒157.
https://doi.org/10.3176/oil.2007.2.05
38. Akhondzadeh, H., Keshavarz, A., Al-Yaseri, A. Z., Ali, M., Awan, F. U. R., Wang, X., Yang, Y., Iglauer, S., Lebedev, M. Pore-scale analysis of coal cleat network evolution through liquid nitrogen treatment: A Micro-Computed Tomography investigation. Int. J. Coal Geol., 2020, 219, 103370.
https://doi.org/10.1016/j.coal.2019.103370
39. Shang, H., Yue, Y., Zhang, J., Wang, J., Shi, Q., Zhang, W., Liu, L., Omar, S. Effect of microwave irradiation on the viscosity of crude oil: A view at the molecular level. Fuel Process. Technol., 2018, 170, 44‒52.
https://doi.org/10.1016/j.fuproc.2017.10.021
40. Wu, Y., Lin, C., Ren, L., Yan, W., An, S., Chen, B., Wang, Y., Zhang, X., You, C., Zhang, Y. Reconstruction of 3D porous media using multiple-point statistics based on a 3D training image. J. Nat. Gas Sci. Eng., 2018, 51, 129‒140.
https://doi.org/10.1016/j.jngse.2017.12.032
41. Blunt, M. J., Bijeljic, B., Dong, H., Gharbi, O., Iglauer, S., Mostaghimi, P., Paluszny, A., Pentland, C. Pore-scale imaging and modelling. Adv. Water Resour., 2013, 51, 197‒216.
https://doi.org/10.1016/j.advwatres.2012.03.003
