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页岩多尺度数字岩心及其应用

姬莉莉 林缅 江文滨 曹高辉

姬莉莉, 林缅, 江文滨, 曹高辉. 页岩多尺度数字岩心及其应用. 力学进展, 2024, 54(3): 1-23 doi: 10.6052/1000-0992-24-006
引用本文: 姬莉莉, 林缅, 江文滨, 曹高辉. 页岩多尺度数字岩心及其应用. 力学进展, 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

页岩多尺度数字岩心及其应用

doi: 10.6052/1000-0992-24-006
基金项目: 本文工作由国家自然科学基金重点项目 (编号: 42030808); 国家自然科学基金面上项目(编号: 41872163); 中国科学院战略性先导科技专项(A类)子课题(编号: XDA14010304)资助.
详细信息
    作者简介:

    林缅, 中国科学院力学研究所研究员, 博士生导师, 主要从事非常规油气勘探开发中的跨尺度输运、页岩油气甜点预测新方法、致密油成藏中的关键力学问题、岩石压裂缝网的力学机制、二氧化碳储存预测等方面的研究. 在《Fuel》《Advances in Water Resources》《Journal of Petroleum Science and Engineering》等国际知名期刊发表论文五十余篇, 相关授权发明专利三十余项, 制定行业标准1个

    通讯作者:

    linmian@imech.ac.cn

    jiangwenbin@imech.ac.cn

  • 中图分类号: TE122

The multiscale digital core of shale and its application

More Information
  • 摘要: 构建能完备表征岩石多尺度孔隙 (缝) 及基质结构的数字岩心是非常规油气研究领域的科技前沿问题, 也是页岩油气勘探开发的重要基础. 文章综合分析了国内外在表征页岩有机孔隙簇、多尺度孔 (缝) 结构和代表性单元体 (REV) 三个方面的研究进展, 在分析四川盆地海相页岩结构特征的基础之上, 提出了可完备表征其孔隙 (缝) 和基质结构的新方法. 最后, 将页岩数字岩心应用到多尺度孔 (缝) 结构对声学特性的影响和原位地层含气量评估方面, 为页岩储层评价和甜点预测提供了新的技术方法.

     

  • 图  1  有机质内孔隙簇主要表征方法分类图

    图  2  叠加法分类图

    图  3  数字-实验岩心重构流程图

    图  4  超声波穿过岩心RC和岩心SC的波形差异 (a)总的波形差异, (b)孔隙结构差异引起的波形差异, (c)矿物差异引起的波形差异

    图  5  岩心RC和岩心SC的声速计算结果与实测值误差对比

    图  6  声速随孔隙度、渗透率的变化率图 (a)声速随孔隙度改变率 (b)声速随渗透率改变率

    图  7  页岩含气量计算方法流程图

    图  8  四川盆地五种典型有机孔分布特征图

    图  9  四川盆地五种典型有机孔结构在不同地层压力系数和地温梯度时的吸附气量和游离气量

    图  10  四川盆地五种典型有机孔结构由地层压力系数和地温梯度引起的吸附气量和游离气量差异图. (a)吸附气量相对于地层压力系数为1.3时变化率; (b)吸附气量相对于地温梯度为1.7时变化率; (c) 游离气量相对于地层压力系数为1.3时的变化率; (d)游离气量相对于地温梯度为1.7时的变化率

    图  11  含气量判识图版. 其中黑色原点为页岩样品. (a)单位质量有机质吸附气量 (b)单位质量游离气量

    图  12  四川盆地某口井含气量计算结果和实测数据的对比

    表  1  对比数字-实验岩心、CT图像和FIB-SEM图像的三维结构和物性参数

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  • [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.
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  • 收稿日期:  2024-01-10
  • 录用日期:  2024-07-26
  • 网络出版日期:  2024-08-10

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