The multiscale digital core of shale and its application

JI Lili; LIN Mian; JIANG Wenbin; CAO Gaohui

doi:10.6052/1000-0992-24-006

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Ji L L, Lin M, Jiang W B, Cao G H. The multiscale digital core of shale and its application. Advances in Mechanics, 2024, 54(3): 1-23 doi: 10.6052/1000-0992-24-006

Citation:

Ji L L, Lin M, Jiang W B, Cao G H. The multiscale digital core of shale and its application. Advances in Mechanics, 2024, 54(3): 1-23 doi: 10.6052/1000-0992-24-006

Ji L L, Lin M, Jiang W B, Cao G H. The multiscale digital core of shale and its application. Advances in Mechanics, 2024, 54(3): 1-23 doi: 10.6052/1000-0992-24-006

Citation:

Ji L L, Lin M, Jiang W B, Cao G H. The multiscale digital core of shale and its application. Advances in Mechanics, 2024, 54(3): 1-23 doi: 10.6052/1000-0992-24-006

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The multiscale digital core of shale and its application

doi: 10.6052/1000-0992-24-006

JI Lili^{1, 2},
LIN Mian^{1, 2
,
,},
JIANG Wenbin^{1, 2
,
,},
CAO Gaohui¹

1.
Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190
2.
School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049

More Information

Corresponding author: linmian@imech.ac.cn; jiangwenbin@imech.ac.cn
Received Date: 2024-01-10
Accepted Date: 2024-07-26

Available Online: 2024-08-10

Abstract

Abstract

Reconstructing digital core that can fully characterize the multiscale pore (fracture) and matrix structure of the rock is one of the most advancing front issue in the field of unconventional oil and gas research, and is also an important foundation for shale oil and gas exploration and development. This article comprehensively analyzes the research progress in characterizing organic pore clusters, multiscale pore (fracture) structures, and representative element volumes (REV) of shale. Based on the analysis of the structural characteristics of marine shale in the Sichuan Basin, a new method for fully characterizing its multiscsal pore (fracture) structure has been proposed. On this basis, the digital cores are applied to the impact of multiscale pore (fracture) structures on acoustic properties and the gas content evaluation, and provides new technical methods for shale reservoir evaluation and sweet spots prediction.
- multiscale and multicomponent digital core,
- oREV,
- lREV,
- digital-experimental core,
- shale reservoir evaluation

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References(60)

References

[1]	范尚炯, 姚爱华. 1990. 地面孔隙度压缩校正方法研究. 石油勘探与开发, 4: 69-77 (Fan S J, Yao A H. 1990. A study of the method of correction from surface laboratory porosity to subsurface porosity of the reservoir rocks. Petroleum Exploration and Development, 4: 69-77). Fan S J, Yao A H. 1990. A study of the method of correction from surface laboratory porosity to subsurface porosity of the reservoir rocks. Petroleum Exploration and Development, 4: 69-77
[2]	吉利明, 邱军利, 宋之光, 夏燕青. 2014. 黏土岩孔隙内表面积对甲烷吸附能力的影响. 地球化学, 43(03): 238-244 (Ji L M, Qiu J L, Song Z G, Xia Y Q. 2014. Impact of internal surface area of pores in clay rockson their adsorption capacity of methane. GeoChimica, 43(03): 238-244). Ji L M, Qiu J L, Song Z G, Xia Y Q. 2014. Impact of internal surface area of pores in clay rockson their adsorption capacity of methane. GeoChimica, 43(03): 238-244
[3]	简世凯, 符力耘, 王志伟, 韩同城, 刘建林. 2020. 龙马溪组页岩数字岩心动态法弹性等效数值建模. 地球物理学报, 63 (7): 2786-2799 (Jian S K, Fu L Y, Wang Z W, Han T C, Liu J L. 2020. Elastic equivalent numerical modeling based on the dynamic method of Longmaxi formation shale digital core. Chinese J. Geophysics, 63 (7): 2786-2799). Jian S K, Fu L Y, Wang Z W, Han T C, Liu J L. 2020. Elastic equivalent numerical modeling based on the dynamic method of Longmaxi formation shale digital core. Chinese J. Geophysics, 63(7): 2786-2799.
[4]	刘振武, 撒利明, 杨晓, 李向阳. 2011. 页岩气勘探开发对地球物理技术的需求. 石油地球物理勘探, 46 (05): 810-819 (Liu Z W, Sa L M, Yang X, Li X M. 2011. Needs of geophysical technologies for shale gas exploration. Oil Geophysical Prospecting, 46 (05): 810-819). Liu Z W, Sa L M, Yang X, Li X M. 2011. Needs of geophysical technologies for shale gas exploration. Oil Geophysical Prospecting, 46(05): 810-819.
[5]	王先武, 张挺, 吉欣, 杜奕. 2021. 基于带梯度惩罚深度卷积生成对抗网络的页岩三维数字岩心重构方法. 计算机应用, 41 (6): 1805-1811 (Wang X W, Zhang T, Ji X, Du Y. 2021. 3D shale digital core reconstruction method based on deep convolutional generative adversarial network with gradient penalty. Journal of Computer Applications, 41 (6): 1805-1811). Wang X W, Zhang T, Ji X, Du Y. 2021. 3D shale digital core reconstruction method based on deep convolutional generative adversarial network with gradient penalty. Journal of Computer Applications, 41(6): 1805-1811.
[6]	徐中华, 郑马嘉, 刘忠华, 邓继新, 李熙喆, 郭伟, 李晶, 王楠, 张晓伟, 郭晓龙. 2020. 四川盆地南部地区龙马溪组深层页岩岩石物理特征. 石油勘探与开发, 47(06): 1100-1110 (Xu Z H, Zheng M J, Liu Z H, Deng J X, Li X J, Guo W, Li J, Wang N, Zhang X W, Guo X L. 2020. Petrophysical properties of deep Longmaxi formation shales in the southern Sichuan basin, SW China. Petroleum Exploration and Development, 47(06): 1100-1110). doi: 10.11698/PED.2020.06.04 Xu Z H, Zheng M J, Liu Z H, Deng J X, Li X J, Guo W, Li J, Wang N, Zhang X W, Guo X L. 2020. Petrophysical properties of deep Longmaxi formation shales in the southern Sichuan basin, SW China. Petroleum Exploration and Development, 47(06): 1100-1110 doi: 10.11698/PED.2020.06.04
[7]	杨胜来, 涂中, 张友彩. 2007. 异常高压气藏储层孔隙度应力敏感性及其对容积法储量计算精度的影响. 天然气地球科学, 18(1): 137-140 (Yang S L, Tu Z, Zhang Y C. 2007. Changes of porosity value in abnormally high pressure gas reservoirs and its effects on the precision of reserve calculation. Natural Gas Geoscience, 18(1): 137-140). doi: 10.3969/j.issn.1672-1926.2007.01.027 Yang S L, Tu Z, Zhang Y C. 2007. Changes of porosity value in abnormally high pressure gas reservoirs and its effects on the precision of reserve calculation. Natural Gas Geoscience, 18(1): 137-140 doi: 10.3969/j.issn.1672-1926.2007.01.027
[8]	杨永飞, 刘夫贵, 姚军, 宋华军, 王民. 2021. 基于生成对抗网络的页岩三维数字岩芯构建. 西南石油大学学报, 43 (5): 73-83 (Yang Y F, Liu F G, Yao J, Song H J, Wang M. 2021. Reconstruction of 3D shale digital rock based on generative adversarial network. Journal of Southwest Petroleum University, 43 (5): 73-83). Yang Y F, Liu F G, Yao J, Song H J, Wang M. 2021. Reconstruction of 3D shale digital rock based on generative adversarial network. Journal of Southwest Petroleum University, 43(5): 73-83.
[9]	赵群, 王红岩, 杨慎, 刘洪林, 刘德勋, 董雷. 2013. 一种计算页岩岩心解吸测试中损失气量的新方法. 天然气工业, 33 (5): 30-34 (Zhao Q, Wang H Y, Yang S, Liu H L, Liu D X, Dong L. 2013. A new method of calculating the lost gas volume during the shale core desorption test. Nature Gas Industry, 33 (5): 30-34). Zhao Q, Wang H Y, Yang S, Liu H L, Liu D X, Dong L. 2013. A new method of calculating the lost gas volume during the shale core desorption test. Nature Gas Industry, 33(5): 30-34.
[10]	Ambrose R J, Hartman R C, Diaz-Campos M, Akkutlu I Y, Sondergeld C H. 2012. Shale gas-in-place calculations part I: New pore-scale considerations. SPE J, 17: 219-229. doi: 10.2118/131772-PA
[11]	Bai B, Elgmati M, Zhang H, Wei M Z. 2013. Rock characterization of Fayetteville shale gas plays. Fuel, 105: 645-652. doi: 10.1016/j.fuel.2012.09.043
[12]	Bear J. Dynamics of fluids in porous media. American Elsevier Pub. Co, 1972.
[13]	Cao G H, Lin M, Jiang W B, Zhao W L, Ji L L, Li C X, Lei D. 2018. A statistical-coupled model for organic-rich shale gas transport. Journal of Petroleum Science and Engineering, 69: 167-183.
[14]	Chalmers G R L, Bustin R M. 2007. The organic matter distribution and methane capacity of the lower cretaceous strata of northeastern British Columbia, Canada. Int J Coal Geol, 70: 223-239. doi: 10.1016/j.coal.2006.05.001
[15]	Chen C, Hu D, Westacott D, Loveless D. 2013. Nanometer-scale characterization of microscopic pores in shale kerogen by image analysis and pore-scale modeling. Geochem. Geophysics. Geosystems, 14: 4066-4075. doi: 10.1002/ggge.20254
[16]	Chen L, Kang Q, Dai Z, Viswanathan H S, Tao W. 2015. Permeability prediction of shale matrix reconstructed using the elementary building block model. Fuel, 160: 346-356. doi: 10.1016/j.fuel.2015.07.070
[17]	Cudjoe S, Fu Q W, Tsau J S, Barati R, Goldstein R, Nicoud B, Baldwin A, Zaghloul J, Mohrbacher D. A reconstructed core-scale model of the lower eagle ford shale through FIB-SEM, SEM-EDS, and microscopy for gas Huff-n-Puff simulation[C]. The SPE Improved Oil Recovery Conference, Virtual, August 2020.
[18]	Dan Y, Seidle J P, Hanson W B. Gas sorption on coal and measurement of gas content. Instituto Fernando el Católico. IFC, 1993: 166-170.
[19]	Dand W, Zhang J, Tang X, et al. 2018. Investigation of gas content of organic-rich shale: A case study from lower Permian shale in southern North China Basin, central China. Geoscience Frontiers, 9(2): 559-575. doi: 10.1016/j.gsf.2017.05.009
[20]	Diamond W P, LaScola J C, Hyman D M. Results of direct-method determination of the gas content of US coalbeds. Information Circular/1986. United States: N. p., 1986. Web.
[21]	Gao K, Guo G J, Zhang M M, et al. 2021. Nanopore surfaces control the shale gas adsorption via roughness and layer accumulated adsorption potential: A molecular dynamics study. Energy & Fuel, 35: 4893.
[22]	Gao M L, He X H, Teng Q Z, Zuo C, Chen D. 2015. Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling. Physical Review E, 91: 013308. doi: 10.1103/PhysRevE.91.013308
[23]	Guo Z, Li X Y. 2015. Rock physics model-based prediction of shear wave velocity in Barnett Shale formation. Journal of Geophysics & Engineering, 12(3): 527-534.
[24]	Jiang W B, Lin M. 2018. Molecular dynamics investigation of conversion methods for excess adsorption amount of shale gas. Journal of Natural Gas Science and Engineering, 49: 241-249. doi: 10.1016/j.jngse.2017.11.006
[25]	Ji L L, Lin M, Jiang W B, Wu C J. 2018. An improved method for reconstructing the digital core model of heterogeneous porous media. Transport in porous media, 121: 389-406. doi: 10.1007/s11242-017-0970-5
[26]	Ji L L, Lin M, Cao G H, Jiang W B. 2019a. A multiscale reconstructing method for shale based on SEM image and experiment data. Journal of Petroleum Science and Engineering, 179: 586-599. doi: 10.1016/j.petrol.2019.04.067
[27]	Ji L L, Lin M, Jiang W B, Cao G H. 2019b. A core-scale reconstructing method for shale. Scientific reports, 9: 4364. doi: 10.1038/s41598-019-39442-5
[28]	Ji L L, Lin M, Jiang W B, Cao G H, Luo C. 2019c. Investigation into the apparent permeability and gas-bearing property in typical organic pores in shale rocks. Marine and Petroleum Geology, 110: 871-885. doi: 10.1016/j.marpetgeo.2019.08.030
[29]	Kelly S, El-Sobky H, Torres-Verdin C, Balhoff M T. 2016. Assessing the utility of FIB-SEM images for shale digital rock physics. Advances in Water Resources, 95: 302-316. doi: 10.1016/j.advwatres.2015.06.010
[30]	Li G Z, Qin Y, Li G R, Wu M, Liu H. 2022. Influence of reservoir properties on gas occurrence and fractal features of transitional shale from the Linxing area, Ordos Basin, China. Arabian Journal of Geosciences, 15: 250. doi: 10.1007/s12517-021-09387-z
[31]	Li Z Z, Min T, Chen L, Kangd Q J, He Y L, Tao W Q. 2016. Investigation of methane adsorption and its effect on gas transport in shale matrix through microscale and mesoscale simulations. Int. J. Heat. Mass Tran, 98: 675-686. doi: 10.1016/j.ijheatmasstransfer.2016.03.039
[32]	Liu Y, Zhu Y M, Li W, Xiang J H, Wang Y, Li J H, Zeng F G. 2016. Molecular simulation of methane adsorption in shale based on grand canonical Monte Carlo method and pore size distribution. J. Nat. Gas. Sci. Eng, 30: 119-126. doi: 10.1016/j.jngse.2016.01.046
[33]	Mansi M, Almobarak M, Lagat C, Xie Q. 2022. Effect of reservoir pressure and total organic content on adsorbed gas production in shale reservoirs: A numerical modelling study. Arabian Journal of Geosciences, 15: 134. doi: 10.1007/s12517-021-09416-x
[34]	Mehmani A, Prodanovic M, Javadpour F. 2013. Multiscale, multiphysics network modeling of shale matrix gas flows. Transport in Porous Media, 99: 377-390. doi: 10.1007/s11242-013-0191-5
[35]	Mehmani A, Prodanović M. 2014. The application of sorption hysteresis in nano-petrophysics using multiscale multiphysics network models. Int. J. Coal Geol, 128: 96-108.
[36]	Mosher K, He J J, Liu Y Y, Rupp E, Wilcox J. 2013. Molecular simulation of methane adsorption in micro- and mesoporous carbons with applications to coal and gas shale systems. Int. J. Coal Geol, 109-110 : 36-44.
[37]	Nie X, Zou C C, Li Z, Meng X H, Jia S, Wan Y. 2016. Numerical simulation of the electrical properties of shale gas reservoir rock based on digital core. Journal of Geophysics and Engineering, 13: 481-490.
[38]	Pillalamarry M, Harpalani S, Liu S. 2011. Gas diffusion behavior of coal and its impact on production from coalbed methane reservoirs. International Journal of Coal Geology, 86(4): 342-348. doi: 10.1016/j.coal.2011.03.007
[39]	Rao Y, Fu L, Wang Z, et al. 2021. Multiscale reconstructions, effective elastic properties, and ultrasonic responses of kerogen matter based on digital organic shales. IEEE access, 9: 43785-43798. doi: 10.1109/ACCESS.2021.3058944
[40]	Ross D J K, Bustin R M. 2007. Shale gas potential of the Lower Jurassic Gardondale member, northeastern British Columbia, Canada. B Can Petrol Geol, 55: 51-75. doi: 10.2113/gscpgbull.55.1.51
[41]	Saraji S, Piri M. 2015. The representative sample size in shale oil rocks and nano-scale characterization of transport properties. International Journal of Coal Geology, 146: 42-54. doi: 10.1016/j.coal.2015.04.005
[42]	Smith D M, Williams F L. 1984. Diffusion models for gas production from coals: Application to methane content determination. Fuel, 63(2): 251-255. doi: 10.1016/0016-2361(84)90046-2
[43]	Sui H G, Yao J, Zhang L. 2015. Molecular simulation of shale gas adsorption and diffusion in clay nanopores. Computation, 3: 687-700. doi: 10.3390/computation3040687
[44]	Tahmasebi P, Javadpour F, Sahimi M. 2015. Three-dimensional stochastic characterization of shale SEM images. Transport in Porous Media, 110: 521-531. doi: 10.1007/s11242-015-0570-1
[45]	Tahmasebi P, Javadpour F, Sahimi M. 2016. Stochastic shale permeability matching: Three-dimensional characterization and modeling. International Journal of Coal Geology, 165: 231-242. doi: 10.1016/j.coal.2016.08.024
[46]	Tahmasebi P, Javadpour F, Sahimi M, Piri M. 2016. Multiscale study for stochastic characterization of shale samples. Advances in Water Resources, 89: 91-103. doi: 10.1016/j.advwatres.2016.01.008
[47]	Wang H, Chen L, Qu Z, Yin Y, Kang Q, Yu B, Tao W-Q. 2020. Modeling of multi-scale transport phenomena in shale gas production — A critical review. Applied Energy, 262: 114575. doi: 10.1016/j.apenergy.2020.114575
[48]	Wang T Y, Tian S C, Liu Q L, Li G S, Sheng M, Ren W X, Zhang P P. 2021. Pore structure characterization and its effect on methane adsorption in shale kerogen. Petroleum Science, 18: 565-578. doi: 10.1007/s12182-020-00528-9
[49]	Wang Y Z, Yuan Y D, Rahman S S, Arns C. 2018. Semi-quantitative multiscale modelling and flow simulation in a nanoscale porous system of shale. Fuel, 234: 1181-1192. doi: 10.1016/j.fuel.2018.08.007
[50]	Wu K J, VanDijke M I, Couples G D, Jiang Z Y, Ma J S, Sorbie K S, Crawford J, Young I, Zhang X X. 2006. 3D stochastic modelling of heterogeneous porous media e applications to reservoir rocks. Transport in Porous Media, 65(3): 443-467. doi: 10.1007/s11242-006-0006-z
[51]	Wu Q Y, Tahmasebi P, Lin C Y, Dong C M. 2020. Process-based and dynamic 2D modeling of shale samples: Considering the geology and pore-system evolution. International Journal of Coal Geology, 218: 103368. doi: 10.1016/j.coal.2019.103368
[52]	Wu Q Y, Tahmasebi P, Yu H, Lin C Y, Wu H A, Dong C M. 2020. Pore scale 3D dynamic modeling and characterization of shale samples: Considering the effects of thermal maturation. J. Geophys. Res.: Solid Earth, 125(1): 2019JB018309. doi: 10.1029/2019JB018309
[53]	Wu Y Q, Lin C, Yan W, Liu Q, Zhao P, Ren L. 2020. Pore-scale simulations of electrical and elastic properties of shale samples based on multicomponent and multiscale digital rocks. Marine and Petroleum Geology, 104369.
[54]	Yang Y, Yao J, Wang C, Ying G, Song W. 2015. New pore space characterization method of shale matrix formation by considering organic and inorganic pores. J Nat Gas Sci Eng, 27: 496-503. doi: 10.1016/j.jngse.2015.08.017
[55]	Yao S S, Wang X Z, Yuan Q W, Zeng F H. 2018. Estimation of shale intrinsic permeability with process-based pore network modeling approach. Transport in Porous Media, 125: 127-148. doi: 10.1007/s11242-018-1091-5
[56]	Yuan W, Pan Z, Li X, et al. 2014. Experimental study and modelling of methane adsorption and diffusion in shale. Fuel, 117: 509-519. doi: 10.1016/j.fuel.2013.09.046
[57]	Zhang P W, Hu L M, Meegoda J N, Gao S Y. 2015. Micro/nano-pore network analysis of gas flow in shale matrix. Scientific Reports, 5: 13501. doi: 10.1038/srep13501
[58]	Zhang W H, Fu L Y, Zhang Y, et al. 2016. Computation of elastic properties of 3D digital cores from the Longmaxi shale. Applied Geophysics, 13(2): 364-374. doi: 10.1007/s11770-016-0542-4
[59]	Zhao L X, Xuan Q, Han D H, et al. 2016. Roch-physics modeling for the elastic properties of organic shale at different maturity stage. Geophysics, 81(5): D527-D541. doi: 10.1190/geo2015-0713.1
[60]	Zhou J, Jiang W B, Lin M, Ji L L, et al. 2020. Impact of water on methane adsorption in nanopores: A hybrid GCMC-MD simulation study. V. V. Krzhizhanovskaya et al. (Eds.): ICCS 2020, LNCS12138: 1-138, pp. 1–13.