Specific surface area versus porosity from digital images: High-porosity granular samples

被引:11
作者
Hussaini, Syed Rizwanullah [1 ,2 ]
Dvorkin, Jack [1 ,2 ]
机构
[1] King Fahd Univ Petr & Minerals, Dhahran, Saudi Arabia
[2] Coll Petr Engn & Geosci, Dhahran, Saudi Arabia
关键词
Porosity; Specific surface area; Granular mixtures; Digital rock physics;
D O I
10.1016/j.petrol.2021.108961
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
We micro-CT-scanned several granular packs, including glass beads of two different particle sizes and their mixtures, a natural dune sand, as well as two sieved samples of this sand and their mixtures. After segmenting these digital volumes into grains and pores, we computed their porosity (9) and specific surface area (S). Moreover, we subsampled these segmented volumes and computed the respective 9 and S of the subvolumes as well. The resulting data pairs exhibited fairly tight S versus 9 trends. These trends, as observed among different samples, present a somewhat inconsistent picture. In some granular digital samples, S increases with increasing 9, while in others the opposite behavior is observed. To explain these behaviors, we analytically model the evolution of the pore-space geometry as that of a binary mixture of large and small particles. Where the small particles gradually fill the pores of an undisturbed large-particle frame, S becomes larger as 9 reduces. The opposite behavior takes place where the disparate large particles are embedded into the continuum of the small particles. The smaller the number of the large particles the higher the porosity and the larger the specific surface area. Where the particles in a pack are of uniform size, S reduces with increasing 9. This theory quantitatively supports the observed experimental relations. The observed consistency between the experiment and theory means that the behaviors observed on microscopic samples are applicable at a much larger spatial scale.
引用
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页数:11
相关论文
共 28 条
  • [11] Hussaini S.R, 2020, J PETROL SCI ENG, V196, P1
  • [12] Karimpouli S, 2020, COMPUT GEOSCI, V135, P118
  • [13] Segmentation of digital rock images using deep convolutional autoencoder networks
    Karimpouli, Sadegh
    Tahmasebi, Pejman
    [J]. COMPUTERS & GEOSCIENCES, 2019, 126 : 142 - 150
  • [14] Conditional reconstruction: An alternative strategy in digital rock physics
    Karimpouli, Sadegh
    Tahmasebi, Pejman
    [J]. GEOPHYSICS, 2016, 81 (04) : D465 - D477
  • [15] On the analysis of spatial binary images
    Lang, C
    Ohser, J
    Hilfer, R
    [J]. JOURNAL OF MICROSCOPY-OXFORD, 2001, 203 : 303 - 313
  • [16] Legland David, 2007, Image Analysis & Stereology, V26, P83, DOI 10.5566/ias.v26.p83-92
  • [17] Permeability-porosity relations from single image of natural rock: Subsampling approach
    Li, Jun
    Hussaini, Syed Rizwanullah
    Dvorkin, Jack
    [J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2020, 194
  • [18] 3D digital rock modeling of the fractal properties of pore structures
    Luo, Miao
    Glover, Paul W. J.
    Zhao, Peiqiang
    Li, Dong
    [J]. MARINE AND PETROLEUM GEOLOGY, 2020, 122
  • [19] Marion D. P., 1990, PhD thesis
  • [20] Mavko G., 2020, ROCK PHYS HDB, DOI DOI 10.1017/9781108333016.011