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SCIENCE CHINA Technological Sciences, Volume 63 , Issue 5 : 755-767(2020) https://doi.org/10.1007/s11431-019-1449-4

Fracture characterization and permeability prediction by pore scale variables extracted from X-ray CT images of porous geomaterials

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  • ReceivedJun 22, 2019
  • AcceptedSep 17, 2019
  • PublishedJan 14, 2020

Abstract

Pore scale variables (e.g., porosity, grain size) are important indexes to predict the hydraulic properties of porous geomaterials. X-ray images from ten types of intact sandstones and another type of sandstone samples subjected to triaxial compression are used to investigate the permeability and fracture characteristics. A novel double threshold segmentation algorithm is proposed to segment cracks, pores and grains, and pore scale variables are defined and extracted from these X-ray CT images to study the geometric characteristics of microstructures of porous geomaterials. Moreover, novel relations among these pore scale variables for permeability prediction are established, and the evolution process of cracks is investigated. The results indicate that the pore-scale permeability is prominently improved by cracks. In addition, excellent agreements are found between the measured and the estimated pore scale variables and permeability. The established correlations can be employed to effectively identify the hydraulic properties of porous geomaterials.


Funded by

the National Natural Science Foundation of China(Grant,Nos.,51839009,51679017)

and the Graduate Research and Innovation Foundation of Chongqing

China(Grant,No.,CYB18037)


Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant Nos. 51839009 and 51679017), and the Graduate Research and Innovation Foundation of Chongqing, China (Grant No. CYB18037).


References

[1] Xiao D, Lu S, Yang J, et al. Classifying multiscale pores and investigating their relationship with porosity and permeability in tight sandstone gas reservoirs. Energy Fuels, 2017, 31: 9188-9200 CrossRef Google Scholar

[2] Ojha S P, Misra S, Tinni A, et al. Relative permeability estimates for Wolfcamp and Eagle Ford shale samples from oil, gas and condensate windows using adsorption-desorption measurements. Fuel, 2017, 208: 52-64 CrossRef Google Scholar

[3] Zhao Y, Yu Q. CO2 breakthrough pressure and permeability for unsaturated low-permeability sandstone of the Ordos Basin. J Hydrol, 2017, 550: 331-342 CrossRef ADS Google Scholar

[4] Liu Z, Cheng Y, Wang L, et al. Analysis of coal permeability rebound and recovery during methane extraction: Implications for carbon dioxide storage capability assessment. Fuel, 2018, 230: 298-307 CrossRef Google Scholar

[5] Ameli A A, Amvrosiadi N, Grabs T, et al. Hillslope permeability architecture controls on subsurface transit time distribution and flow paths. J Hydrol, 2016, 543: 17-30 CrossRef ADS Google Scholar

[6] Elger J, Berndt C, Rüpke L, et al. Submarine slope failures due to pipe structure formation. Nat Commun, 2018, 9: 715 CrossRef PubMed ADS Google Scholar

[7] Zhao Z, Zhou X P. Digital energy grade-based approach for crack path prediction based on 2D X-ray CT images of geomaterials. Fatigue Fract Eng Mater Struct, 2019, 42: 1292-1307 CrossRef Google Scholar

[8] Honarpour M, Mahmood S M. Relative-permeability measurements: An overview. J Pet Tech, 1988, 40: 963-966 CrossRef Google Scholar

[9] Gomez C T, Dvorkin J, Vanorio T. Laboratory measurements of porosity, permeability, resistivity, and velocity on fontainebleau sandstones. Geophysics, 2010, 75: E191-E204 CrossRef ADS Google Scholar

[10] Chen J, Yang X, Duan Q, et al. Integrated measurements of permeability, effective porosity, and specific storage of core samples using water as the pore fluid. Int J Rock Mech Min Sci, 2015, 79: 55-62 CrossRef Google Scholar

[11] Qajar J, Arns C H. Characterization of reactive flow-induced evolution of carbonate rocks using digital core analysis-part 1: Assessment of pore-scale mineral dissolution and deposition. J Contam Hydrol, 2016, 192: 60-86 CrossRef PubMed ADS Google Scholar

[12] Tahmasebi P, Hezarkhani A. A fast and independent architecture of artificial neural network for permeability prediction. J Pet Sci Eng, 2012, 86-87: 118-126 CrossRef Google Scholar

[13] Mohamed M T. Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci, 2011, 48: 845-851 CrossRef Google Scholar

[14] Saemi M, Ahmadi M, Varjani A Y. Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng, 2007, 59: 97-105 CrossRef Google Scholar

[15] Okabe H, Blunt M J. Pore space reconstruction using multiple-point statistics. J Pet Sci Eng, 2005, 46: 121-137 CrossRef Google Scholar

[16] Al-Kharusi A S, Blunt M J. Network extraction from sandstone and carbonate pore space images. J Pet Sci Eng, 2007, 56: 219-231 CrossRef Google Scholar

[17] Ranaee E, Porta G M, Riva M, et al. Prediction of three-phase oil relative permeability through a sigmoid-based model. J Pet Sci Eng, 2015, 126: 190-200 CrossRef Google Scholar

[18] Shams M, Raeini A Q, Blunt M J, et al. A numerical model of two-phase flow at the micro-scale using the volume-of-fluid method. J Comput Phys, 2018, 357: 159-182 CrossRef ADS Google Scholar

[19] Haeri H, Marji M F, Shahriar K, et al. On the HDD analysis of micro crack initiation, propagation, and coalescence in brittle materials. Arab J Geosci, 2015, 8: 2841-2852 CrossRef Google Scholar

[20] Haeri H, Shahriar K, Marji M F, et al. Modeling the propagation mechanism of two random micro cracks in rock samples under uniform tensile loading. In: Proceedings of the 13th International Conference on Fracture. Beijing, 2013. Google Scholar

[21] Haeri H, Sarfarazi V, Marji M F, et al. Experimental and numerical study of shear fracture in brittle materials with interference of initial double cracks. Acta Mech Solid Sin, 2016, 29: 555-566 CrossRef Google Scholar

[22] Sarfarazi V, Haeri H. A review of experimental and numerical investigations about crack propagation. Comput Concrete, 2016, 18: 235-266 CrossRef Google Scholar

[23] Behrang A, Mohammadmoradi P, Taheri S, et al. A theoretical study on the permeability of tight media; effects of slippage and condensation. Fuel, 2016, 181: 610-617 CrossRef Google Scholar

[24] Becker I, Wüstefeld P, Koehrer B, et al. Porosity and permeability variations in a tight gas sandstone reservoir analogue, westphalian d, lower saxony basin, nw germany: influence of depositional setting and diagenesis. J PetGeol, 2017, 40: 363-389 CrossRef ADS Google Scholar

[25] Sun W C, Wong T F. Prediction of permeability and formation factor of sandstone with hybrid lattice Boltzmann/finite element simulation on micro tomographic images. Int J Rock Mech Min Sci, 2018, 106: 269–277. Google Scholar

[26] Weerakone W B, Wong R C K, Kantzas A. Morphological characterization of induced fracture in sandstone using X-ray computed tomography scanning. Geotech Testing J, 2012, 35: 460–468. Google Scholar

[27] Alhammadi A M, AlRatrout A, Bijeljic B, et al. Pore-scale imaging and characterization of hydrocarbon reservoir rock wettability at subsurface conditions using X-ray microtomography. J Vis Exp, 2018, 140: e57915. Google Scholar

[28] Jobmann M, Billaux D. Fractal model for permeability calculation from porosity and pore radius information and application to excavation damaged zones surrounding waste emplacement boreholes in opalinus clay. Int J Rock Mech Min Sci, 2010, 47: 583–589. Google Scholar

[29] Zheng J, Zheng L, Liu H H, et al. Relationships between permeability, porosity and effective stress for low-permeability sedimentary rock. Int J Rock Mech Min Sci, 2015, 78: 304–318. Google Scholar

[30] Ingraham M D, Bauer S J, Issen K A, et al. Evolution of permeability and Biot coefficient at high mean stresses in high porosity sandstone. Int J Rock Mech Min Sci, 2017, 96: 1-10 CrossRef Google Scholar

[31] Alyafei N, Blunt M J. Estimation of relative permeability and capillary pressure from mass imbibition experiments. Adv Water Resources, 2018, 115: 88-94 CrossRef ADS Google Scholar

[32] Sufian A, Russell A R. Microstructural pore changes and energy dissipation in Gosford sandstone during pre-failure loading using X-ray CT. Int J Rock Mech Min Sci, 2013, 57: 119–131. Google Scholar

[33] Oliveira T D S, Blunt M J, Bijeljic B. Modelling of multispecies reactive transport on pore-space images. Adv Water Resources, 2019, 127: 192-208 CrossRef ADS Google Scholar

[34] Latief F D E, Fauzi U, Bijaksana S, et al. Pore structure characterization of 3D random pigeon hole rock models. Int J Rock Mech Min Sci, 2010, 47: 523–531. Google Scholar

[35] Du S, Li M J, Ren Q, et al. Pore-scale numerical simulation of fully coupled heat transfer process in porous volumetric solar receiver. Energy, 2017, 140: 1267-1275 CrossRef Google Scholar

[36] Sun W C, Wong T F. Prediction of permeability and formation factor of sandstone with hybrid lattice Boltzmann/finite element simulation on micro tomographic images. Int J Rock Mech Min Sci, 2018, 106: 269–277. Google Scholar

[37] Zhao Z, Zhou X P. An integrated method for 3D reconstruction model of porous geomaterials through 2D CT images. Comput Geosci, 2019, 123: 83–94. Google Scholar

[38] Yang L, Ai L, Xue K, et al. Analyzing the effects of inhomogeneity on the permeability of porous media containing methane hydrates through pore network models combined with CT observation. Energy, 2018, 163: 27-37 CrossRef Google Scholar

[39] Dong H, Blunt M J. Pore-network extraction from micro-computerized-tomography images. Phys Rev E, 2009, 80: 036307 CrossRef PubMed ADS Google Scholar

[40] Kachanov L M. Rupture time under creep conditions. Int J Fracture, 1999, 97: 11–18. Google Scholar

[41] Rabotnov Y N. Paper 68: On the equation of state of creep. In: Proceedings of the Institution of Mechanical Engineers, Conference Proceedings. London: SAGE Publications, 1963. Google Scholar

[42] Murakami S. Continuum damage mechanics: A continuum mechanics approach to the analysis of damage and fracture. Springer Ebooks, 2012. 4731–4755. Google Scholar

[43] Pape H, Tillich J E, Holz M. Pore geometry of sandstone derived from pulsed field gradient NMR. J Appl Geophys, 2006, 58: 232-252 CrossRef ADS Google Scholar

[44] Jeong N. Advanced study about the permeability for micro-porous structures using the lattice boltzmann method. Transp Porous Media, 2010, 83: 271-288 CrossRef Google Scholar

[45] Xie C, Raeini A Q, Wang Y, et al. An improved pore-network model including viscous coupling effects using direct simulation by the lattice Boltzmann method. Adv Water Resources, 2017, 100: 26-34 CrossRef ADS Google Scholar

[46] Henderson N, Brêttas J C, Sacco W F. A three-parameter Kozeny-Carman generalized equation for fractal porous media. Chem Eng Sci, 2010, 65: 4432-4442 CrossRef Google Scholar

[47] Xu P, Yu B. Developing a new form of permeability and Kozeny-Carman constant for homogeneous porous media by means of fractal geometry. Adv Water Resources, 2008, 31: 74-81 CrossRef ADS Google Scholar

[48] Li B, Wong R C K, Heidari S. A modified Kozeny-Carman model for estimating anisotropic permeability of soft mudrocks. Mar PetGeol, 2018, 98: 356-368 CrossRef Google Scholar

[49] Mohammadmoradi P, Kantzas A. Pore-scale permeability calculation using CFD and DSMC techniques. J Pet Sci Eng, 2016, 146: 515-525 CrossRef Google Scholar

[50] Peyman M, Apostolos K. Modelling two-phase flow in multi-mineral porous media using coupled level set-volume of fluid (CLSVOF) method. In: Proceedings of the GeoConvention 2016: Optimizing Resources. Calgary, 2016. Google Scholar

[51] Kozeny J. Ueber kapillare leitung des wassers im boden. Sitzungsber Akad Wiss Wien, 1927, 136: 271–306. Google Scholar

[52] Carman P C. Fluid flow through granular beds. Trans Inst Chem Eng, 1937, 15: 150–66. Google Scholar

[53] McCabe W L. Unit Operations of Chemical Engineering. 7th ed. New York: McGraw-Hill, 2005. Google Scholar

[54] Tiab D, Donaldson E C. Petrophysics: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties. Houston: Gulf Professional Publishing, 2011. Google Scholar

  • Figure 1

    Pore and crack segmentation demonstration using double threshold algorithm.

  • Figure 2

    Microstructures of sandstone samples S4, S9 and S10 for (a)–(c) original images; (d)–(f) binary images (pores presented by black and grains presented by white); (g)–(i) color images (pores with sizes presented by different colors and grains presented by black), respectively.

  • Figure 3

    3D Sketch maps of (a) pores with different color bulks and (b) grains with different color bulks from sandstone specimen S8.

  • Figure 4

    Sketch maps of converting unit cubic pixel grains to spheres.

  • Figure 5

    Porosity versus specific surface of (a) S11, (b) S3 and (c) S8 for fine, medium and coarse pore sandstones.

  • Figure 6

    Real 3D pore models of sandstone samples S1–S10 (a)–(j) and fracture model of sandstone sample S11 (k).

  • Figure 7

    Sketch maps of constructing 3D MPNP model.

  • Figure 8

    Fracture evolution process of sandstone S11 for (a) damage ratio with crack images at each stage and (b) specific surface of crack with crack images at each stage.

  • Figure 9

    Porosity (a) and specific surface (b) distribution curves with schematic pore-crack images of sandstone S3 and sandstone S11.

  • Figure 10

    Relative content of specific surface and porosity for different types of sandstone samples.

  • Figure 11

    Relative content distributions of equivalent pore and grain size for different types of sandstone samples.

  • Figure 12

    Estimated results of sandstone samples for (a) comprehensive factor and (b) specific surface versus grain size under different porosity.

  • Figure 13

    Relative content of porosity and specific surface from sandstone S11 for (a) intact samples and (b) damaged samples.

  • Figure 14

    Comparison of the laboratory measured value and estimated value for (a) specific surface (R2=0.98432) and (b) permeability for sandstone samples S1–S11.

  • Table 1   Parameters of selected sandstone specimens

    Types

    S1

    S2

    S3

    S4

    S5

    S6

    S7

    S8

    S9

    S10

    S11

    Resolution (μm/pixel)

    5.35

    8.68

    4.96

    9.1

    8.96

    4

    5.1

    4.8

    4.89

    3.4

    8.01

    Pixel-scale

    3002

    3002

    3002

    3002

    3002

    3002

    3002

    3002

    3002

    3002

    3002

    Slices

    300

    300

    300

    300

    300

    300

    300

    300

    300

    300

    100

    G (kN/m3)

    23.25

    23.28

    22.97

    23.35

    22.88

    23.12

    23.15

    22.89

    23.11

    23.15

    22.89

    φ

    0.188

    0.141

    0.238

    0.152

    0.158

    0.218

    0.238

    0.245

    0.351

    0.223

    0.163

    κ (mD)

    279

    332

    550

    223

    251

    517

    594

    767

    706

    364

    172

    G-unit weight, κ-permeability

  • Table 2   Results analysis of the 11 types of sandstone samples

    Rock types

    S1

    S2

    S3

    S4

    S5

    S6

    S7

    S8

    S9

    S10

    S11

    Sample numbers

    26

    25

    28

    12

    9

    14

    29

    15

    15

    14

    14

    φ

    0.191

    0.142

    0.241

    0.153

    0.162

    0.212

    0.244

    0.253

    0.345

    0.227

    0.168

    ξM (1/mm)

    83.96

    43.94

    54.74

    53.38

    60.03

    53.32

    31.04

    65.69

    52.05

    63.55

    70.86

    ξC (1/mm)

    80.23

    40.92

    54.29

    50.42

    56.73

    50.62

    27.44

    61.84

    49.63

    60.41

    68.70

    γ

    5.95

    7.20

    5.89

    7.33

    7.12

    5.71

    5.83

    5.66

    5.69

    5.72

    7.35

    ξM and ξC are respectively the experimental and estimated specific surface.

  • Table 3   Pore and grain sizes of 11 types of sandstone samples

    Rock types

    Average pore size (μm)

    Standard errors (μm)

    Maxima

    Minima

    Average

    Maxima

    Minima

    Average

    S1

    34.41

    27.82

    30.16

    17.46

    12.87

    14.90

    S2

    63.67

    43.99

    53.64

    37.72

    19.60

    28.47

    S3

    42.94

    26.78

    35.29

    24.92

    11.86

    17.19

    S4

    46.49

    35.06

    39.35

    21.01

    14.55

    17.49

    S5

    41.38

    35.35

    37.84

    20.64

    13.55

    16.64

    S6

    55.67

    29.71

    40.54

    38.22

    16.41

    25.26

    S7

    66.31

    42.98

    52.51

    57.99

    23.11

    36.22

    S8

    49.71

    32.94

    40.77

    34.16

    15.93

    22.69

    S9

    51.10

    35.68

    41.98

    30.11

    17.23

    21.78

    S10

    38.38

    24.96

    31.89

    28.15

    12.05

    20.07

    S11

    38.22

    26.80

    29.25

    18.17

    10.50

    12.16

    Rock types

    Average grain size (μm)

    Standard errors (μm)

    Maxima

    Minima

    Average

    Maxima

    Minima

    Average

    S1

    99.46

    72.62

    82.93

    55.05

    28.86

    41.26

    S2

    185.34

    142.63

    161.37

    104.53

    57.81

    76.1

    S3

    82.85

    60.25

    72.21

    41.73

    25.40

    31.42

    S4

    133.25

    98.77

    116.37

    72.30

    47.83

    59.16

    S5

    122.84

    99.96

    109.47

    63.15

    39.88

    51.12

    S6

    117.84

    69.87

    92.49

    65.62

    38.56

    50.91

    S7

    150.82

    77.69

    106.13

    106.19

    51.1

    77.02

    S8

    89.57

    68.30

    78.13

    60.80

    36.80

    48.28

    S9

    71.42

    55.09

    63.40

    43.83

    28.03

    34.08

    S10

    86.62

    59.37

    70.57

    47.67

    26.13

    35.91

    S11

    107.96

    53.35

    90.53

    57.65

    23.9

    43.67

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