Regular Article
A relative permeability model for CBM reservoir
^{1}
SINOPEC Petroleum Exploration and Production Research Institute, 100083 Beijing, PR China
^{2}
SINOPEC Key Laboratory of Shale Oil/Gas Exploration & Production, 100083 Beijing, PR China
^{3}
Key Laboratory for Petroleum Engineering of the Ministry of Education, China University of Petroleum, 102249 Beijing, PR China
^{*} Corresponding author: pengzy0328@sina.com
Received:
15
October
2018
Accepted:
11
December
2019
Relative permeability is an effective tool for studying multiphase fluid flow in porous media. For conventional reservoirs, a relatively reliable relative permeability curve can be obtained by laboratory core test. But because of the coalbed gas reservoir permeability is low, the stable steady state method will take a very long time, and the operation is relatively complex. For the nonsteady state method, the coalbed gas reservoirs are rich in micro nano pore, which causes the strong heterogeneity and gas is easy to break in through the cracks, it makes nonsteady displacement experiment very difficult. Also, the experimental results are greatly affected by human factors and computational methods. Therefore, based on the ideal pore structure and the consideration of different displacement mechanisms, the analytical method not only helps to understand the mechanism of gas water twophase flow, but also is a convenient and practical method. Coalbed methane reservoirs are rich of nano pores, and the percolation process is more complicated due to the water. Consider of the nano pore of the coal, the capillary force’s effect will be more important. The different pressure will cause different flow channel, which will change the permeability. In this paper, the relative permeability model of coalbed methane reservoir has been built which considers the gas diffusion and slippage effect, pore throat structure parameter, water saturation distribution, and gas water interface pressure drop. It can describe the difference flow channel between different pressure.
© Z. Peng et al., published by IFP Energies nouvelles, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1 Preface
There is the huge amount of resources of CBM, the global CBM resources are about 256.1 × 10^{12} m^{3}, mainly in the former Soviet Union, North America, and the Asia Pacific region. In 2011, the global coalbed methane production exceeded 700 × 10^{8} m^{3} (Birol, 2010). However, due to the characteristics of low porosity, low permeability, complicated pore structure, high capillary pressure, and high initial water content, the coalbed gas flow is very complicated (Marle, 1981). Therefore, the study of gas water permeability curve of coalbed gas reservoir is the basis of gas well productivity and water production evaluation and prediction, and it is of great significance to the efficient and economic development of coalbed methane reservoir (Yang and Wei, 2015).
Relative permeability is an effective tool for studying multiphase fluid flow in porous media. For conventional reservoirs, a relatively reliable relative permeability curve can be obtained by laboratory core test (steady state method or nonsteady state method) (Gong et al., 2014). But because of the coalbed gas reservoir permeability is low, the stable steady state method will take a very long time, and the operation is relatively complex. For the nonsteady state method, the coalbed gas reservoirs are rich in micro nano pore, which causes the strong heterogeneity and gas is easy to break in through the cracks, it makes nonsteady displacement experiment very difficult. Also, the experimental results are greatly affected by human factors and computational methods (Chima and Geiger, 2012; Yang and Wei, 2015). The lattice Boltzmann simulation at the pore scale is another feasible method to obtain the phase permeability curve. However, this method needs a lot of calculation resources and time, which leads to some limitations in practical application (Li et al., 2014; Wu et al., 2015a). Therefore, based on the ideal pore structure and the consideration of different displacement mechanisms, the analytical method not only helps to understand the mechanism of gas water twophase flow, but also is a convenient and practical method (Wu et al., 2015a, b).
According to the different Knudsen number, which can be calculated by average porous diameter (d) and the mean free path of particle (λ), the formula is K _{ n } = λ/d. The transmission mechanism of the gas can be divided into continuous flow (K _{ n } < 10^{−3}), slip flow (10^{−3} < K _{ n } < 10^{−1}), transition flow (10^{−1} < K _{ n } < 10) and free molecular flow (K _{ n } > 10) (Sampath and Keighim, 1982; Wu et al., 2016a). The Knudsen number of typical coalbed gas reservoir pressure and temperature conditions is near 10^{−1}, therefore, the transmission mechanism comprises a continuous flow and slip flow, and transition flow may be included (Klinkenberg, 1941). Knudsen (1934) defined the ratio of the mean free range of gas molecules to the porous medium scale as the Knudsen number for characterizing the rarefied effects of gases. Then, based on the Knudsen number, Schaaf and Chambré (1961) divided the gas phase nano hole transmission mechanism into four basic types, which is continuous flow, slip flow, transition flow, and Knudsen diffusion. After years of research, the continuous flow, slip flow, and Knudsen diffusion, has formed a relatively complete theoretical system (Fig. 1).
Fig. 1 Knudsen number and gas flow regimes Ertekin et al. (1986). 
With further research, Ertekin et al. (1986) draw lessons from weight factor, interconnection Fick diffusion and Knudsen diffusion. But because of the weight factor will not change with the Knudsen number, the results are quite different from the result from molecular simulation and experimental. Liu et al. (2002) given the weight factor’s specific expression, while is limited the situation which the Knudsen number must less than 1. The model built by Beskok and Karniadakis (1999) is able to describe all the transport mechanisms known for gas phases in nanoscale pore. But the model has multiple experience factor and cannot degenerate to Knudsen flow model when the Knudsen number is large. Javadpour et al. (2007) and Javadpour (2009) linear addition slip flow and Knudsen diffusion based on Maxwell theory to get the transmission model in all Knudsen number. Subsequently, Darabi et al. (2012) and Singh et al. (2014) revise the model by consider real gas effect, pore wall roughness and so on. Recently, Wu et al. (2014) have established the weighting coefficients of diffusion and slippage flow according to the molecular collision theory. The deduced gas flow model can be well fitted with the molecular simulation results. Then further correct the model on the basis of pore shape, pore wall roughness, real gas effect and gas rarefaction effect (Wu et al., 2015c–e, 2016b).
In this paper, the two order slip model and the weight coefficient proposed by Wu et al. (2016b) are used to correct the gas free Hagen–Poiseuille equation. Then, established the gas water two phase relative permeability model based on the fractal theory of porous media and capillary bundle model, which considered gas slippage effect, pore roar structure parameter and water saturation distribution. Finally, the correctness of the model is verified by experimental data in public.
2 Model building
Based on the Hagen–Poiseuille continuous equation, two order slip model or no slip condition, introduced the weight coefficients, and the transport model of gas and water in micron scale pore is established by using boundary as coupling condition. This model can take into account the influence of pressure and temperature on fluid transport, and reasonably describe the gas flow behavior in the pore roar of coalbed methane reservoir.
2.1 Single pipe flow model
If the flow of gas and water in the micron pores is laminar, the velocity distribution of the fluid along the pipe diameter still satisfies the continuity equation, if the boundary conditions are properly treated (Cao et al., 2009; Wu, 2008).
Continuity equation of gas:
(1)Continuity equation of water:
(2)where v is flow velocity in pore (m/s), r is radial distance from the middle of capillary to any position (m), r _{0} is capillary radius (m), μ is aqueous viscosity (Pa · s), p is pressure (Pa), z is distance along the flow equation (m). Subscript w means aqueous phase, while subscript g means gas phase.
Owing to the kinetic energy transfer in the gas–solid near wall is affected by the molecules interaction between gases and gases, or gases and walls. The gas molecules on the wall do not completely adhere to the wall, which is called the gas slip. When the number of Knudsen gas increased to 10^{−3}, its influence on the gas transmission cannot be ignored (Sampath and Keighim, 1982; Wu et al., 2016b). When the Knudsen number is less than 10^{−1}, the wall slip velocity can be characterized by the two order slip model (Tocci et al., 2016):
(3)where C _{1} and C _{2} are first order slip factor and second slip factor, which often valued as 4/3 and 1/4 (Tocci et al., 2016; Wu et al., 2017). λ is mean free path of gas molecules, which can be calculated by the follows:
(4)where k _{ b } is Boltzmann’s constant, T is absolute temperature, d is molecular diameter of methane, P _{ave} is average pressure of capillary inlet and outlet.
Due to the smaller pore size in the actual coalbed methane reservoir, the transition flow of 10^{−1} < K _{ n } < 10 cannot be neglected. Therefore, based on the weight coefficient of Wu et al. (2015d), superposition the continuous flow and Knudsen diffusion of gas, correct the flow of gas in the pores:
(5)where J _{ T–tube} is total mass flow (kg/m^{2} · s), J _{slip–tube} is mass flow at slip flow (kg/m^{2} · s), is mass flow at Knudsen diffusion (kg/m^{2} · s), f _{slip–tube} and are weight coefficient of slip flow and Knudsen diffusion, which can be calculated by the follows (Wu et al., 2015a, b):
(7)where ω _{ m–m } is collision frequency between molecules and molecules, which depends on pore volume or crosssectional area. And ω _{ m–w } is collision frequency between molecules and walls, which depends on pore surface area or perimeter.
For the boundary conditions of singlephase water flow, because of the strong force of water molecules and solid molecules, the boundary effect of singlephase flow is usually less than 1 nm (Katz and Thompson, 1985; Yu and Liu, 2004), which can be ignored. Therefore, it can be considered that the velocity of water molecules at the wall surface is 0, and the wall effect has no effect on the water transmission, which means singlephase water flow can be characterized by the nonslip boundary conditions. So, the boundary conditions of singlephase gas and singlephase water flow can be expressed as follows (Fig. 2):
Fig. 2 Velocity distribution along the circular microtube of single gas/water flow. 
Integral equations (1) and (2), the flow equation of gas phase and aqueous phase can be obtained as follows:
(13)equation (11) is flow equation with boundary slip, while equation (12) is the classical Hagen–Poiseuille equation with no slip.
2.2 Relative permeability model
2.2.1 Fractal theory
From the macroscopic scale to the microscopic scale, a large number of porous media in nature exhibit fractal and self similar characteristics (Majumdar and Bhushan, 1990; Yu and Li, 2011). According to the fractal theory, the cumulative pore size distribution can be expressed as follows (Yu and Cheng, 2002):
(14)where N is number of pores, ε is length scale (m), r and r _{max} are aperture and maximum aperture (m), D _{ f } is fractal dimension of pore size distribution, the ranges of 2D and 3D holes are 0 < D _{ f } < 2, and it can be calculated as follows (Bonnet et al., 2001):
(15)where d is Euclidean space, equals 2 or 3, in this work, d = 2 has been used because the capillary can be regarded as 2D, r _{min} is the minimum aperture (m), φ is porosity.
Total pore number between the maximum aperture and the minimum aperture can be calculated as follows:
The distribution probability density function of pores with different sizes can be calculated as follows (Xu and Yu, 2008):
Considering the actual pore in the matrix is tortuous, continuous characterization unit of porous media (REV) with characteristic length L _{0}, the actual length of the capillary will be L, and the L can be calculated as follows (Xu et al., 2013):
(18)where D _{ T } is the tortuosity fractal dimension, the range of 2D and 3D are 1 < D _{ T } < 2 and 1 < D _{ T } < 3. D _{ T } = 1 means a straight capillary, D _{ T } = 2 (in 2D) or 3 (in 3D) means the capillary is infinite tortuosity and can fill the whole space (Burdine, 1953). D _{ T } can be calculated as follows:
(19)where the parameter and can be calculated as follows:
Use the porosity φ, and the result of equation (15), the necessary parameter of equation (19) can be calculated by equations (20) and (21), which means the D _{ T } can be calculated by equation (19). Then, the relationship between characteristic length L _{0} and actual length of the capillary L is shown by equation (18).
2.2.2 Relative permeability model
In order to simplify the model, the complicated pore structure in coalbed methane reservoir is simplified as the capillary bundle model, which consists of some unequal tortuous capillary, which are vertical to REV. The fluid flows only from the capillary bundle, and the circumferential aspect of REV has no fluid flow, which is shown by Figure 3. The pore size distribution and flow tortuosity of capillary bundle model is satisfied with the fractal theory which is shown by equations (14)–(21) (Kim et al., 2012).
Fig. 3 Physical conceptual model of gas/water flow pattern in tight sandstone sample. 
A large amount of adsorbed gas and free gas in the coal reservoir are stored in nanometer pores, while a lot of research in coal geology shows that the nanometer pores in coal reservoir are mainly gas pore, which is formed by gas production during coalification. And the other nano pores are not the main pores because of their small number or poor connectivity (Zhang, 2016). On the other hand, it is difficult for water molecules to enter the nano pores of coal because of the surface hydrophobic effect of organic matter (Firouzi et al., 2014). So suppose there is a critical capillary radius r _{ c }, the capillary which the aperture less than r _{ c } is full of singlephase gas, while the capillary which the aperture larger than r _{ c } may be full of singlephase gas or singlephase water. And the capillary which the aperture less than r _{ c } are randomly connected.
Based on the above hypothesis, the capillary connection can be divided into two categories, the gas capillary which the aperture less than r _{ c } connected with the gas capillary which the aperture larger than r _{ c }, and with the water capillary which the aperture larger than r _{ c }. The proportion of the gas capillary connect gas capillary to total capillary is α _{1}, while the proportion of the gas capillary connect water capillary to total capillary is α _{2}:
where S _{ w } is water saturation in matrix. Consider the influence of tortuosity and pressure, single tube flow formula of gas and water (12) and (13) can be reduced as follows:
(23)where L(r) is the actual length of the pore path, ∇p _{ g }/L and ∇p _{ w }/L are pressure gradient of gas and water.
Integral equation (22), for the gas phase flow into the water phase, only the capillary force is less than pressure gradient can be migrated. Gas volume flow of REV can be shown as follows:
(24)where the r _{ p } is the minimum capillary radius that can be displaced at present pressure, which can be calculated as follows:
Similarly, integral equation (23), water volume flow of REV can be shown as follows:
On the other hand, the flow of gas and water can also be expressed by Darcy’s expansion formula:
(27)where K _{ g } and K _{ w } are effective permeability of gas and water.
Solve the equations (24)–(27), the effective permeability of gas and water can be shown as follows:
Bring S _{ w } = 1 into equation (29), the absolute permeability of coal can be shown as follows:
(30)So, relative permeability of gas and water can be shown as follows:
3 Model verification and discussion
3.1 Model verification
This paper proving the reliability of the model by using two sets of different test data of gas water relative permeability in published papers (Zhang et al., 2017). Two test coal’s porosity in the literature is 9.1% and 8.6%, while the maximum and minimum aperture are r _{max} = 100 μm, r _{min} = 0.001 μm. Fractal dimension of pore size distribution D _{ f } and the tortuosity fractal dimension D _{ T } are calculated by equations (14), (19), and (20). The pressure is 2 MPa and 6 MPa.
A conclusion from the comparison results, the model established in this paper by equations (31) and (32) can be better fitted with the experimental data as Figure 4. For the high pressure reservoir, the model is more accurate than other model. Because of the effect of slippage and capillary force have been considered in this model, which is shown by Figure 5.
Fig. 4 Different model and experimental data in 2 MPa. 
Fig. 5 Different model and experimental data in 6 MPa. 
The model’s deviation can be calculated by the following. The result of both effect is shown (Tabs. 1 and 2):
(33)From the calculation results, it can be seen that the model has high fitting accuracy for both high pressure and low pressure.
Different model’s deviation in 2 MPa.
Different model’s deviation in 6 MPa.
3.2 Discussion
Using the new model to calculate the gas relative permeability in different pressure by the same properties in Section 3.1, the result is shown as Figure 6.
Fig. 6 Gas relative permeability in different pressure. 
With the increase of pressure, the gas relative permeability increase fast in lower pressure, while changes a little when the pressure is larger than 5 MPa. This is because with the increase of pressure, the more nano pore can be flown by fluid, but when the pressure is larger than 5 MPa, the pressure is larger than most pores capillary pressure, the effect of pressure will become very small.
But because of the model has been using fractal theory to describe the pore characteristic, which means the pores distribution of different sizes is very regular, which cause the relative permeability in different pressure is very similar, that will cause some error. If there were some more accurate method to describe pore distribution, this method will have higher precision.
4 Conclusion
Based on the Hagen–Poiseuille continuous equation with two order slip model, introduced the weight coefficients and fractal theory, considered the distribution of gas and water, established a gaswater relative permeability model of coalbed methane reservoir, and the transport model of gas and water in micron scale pore is established by using boundary as coupling condition. This model can take into account the influence of pressure and temperature on fluid transport, and reasonably describe the gas flow behavior in the pore roar of coalbed methane reservoir which has been validated by published experimental data of gas water relative permeability, and can be used to describe the fluid flow behavior in micro scale pores of coalbed methane reservoirs.
References
 Beskok A., Karniadakis G.E. (1999) Report: A model for flows in channels, pipes, and ducts at micro and nano scales, Nanoscale Microscale Therm. Eng. 3, 1, 43–77. doi: 10.1080/108939599199864. [CrossRef] [Google Scholar]
 Birol F. (2010) World energy outlook 2010, International Energy Agency, Washington, DC. [Google Scholar]
 Bonnet E., Bour O., Odling N.E., Davy P., Main I., Cowie P., Berkowitz B. (2001) Scaling of fracture systems in geological media, Rev. Geophys. 39, 3, 347–383. doi: 10.1029/1999rg000074. [Google Scholar]
 Burdine N.T. (1953) Relative permeability calculations from pore size distribution data, J. Pet. Technol. 5, 3, 71–78. doi: 10.2118/225g. [CrossRef] [Google Scholar]
 Cao B.Y., Sun J., Chen M., Zeng Y. (2009) Molecular momentum transport at fluidsolid interfaces in MEMS/NEMS: A review, Int. J. Mol. Sci. 10, 11, 4638. doi: 10.3390/ijms10114638. [Google Scholar]
 Chima A., Geiger S. (2012) An analytical equation to predict gas/water relative permeability curves in fractures, in: The Annual Meeting for the SPE Latin America and Caribbean Petroleum Engineering Conference, 16–18 April, Mexico City. [Google Scholar]
 Darabi H., Ettehad A., Javadpour F., Sepehrnoorl K. (2012) Gas flow in ultratight shale strata, J. Fluid Mech. 710, 12, 641–658. doi: 10.1017/jfm.2012.424. [Google Scholar]
 Ertekin T., King G.R., Schwerer F.C. (1986) Dynamic gas slippage: A unique dualmechanism approach to the flow of gas in tight formations, SPE Form. Eval. 1, 1, 43–52. doi: 10.2118/12045pa. [CrossRef] [Google Scholar]
 Firouzi M., Rupp E.C., Liu C.W., Wilcox J. (2014) Molecular simulation and experimental characterization of the nanoporous structures of coal and gas shale, Int. J. Coal Geol. 121, 1, 123–128. doi: 10.1016/j.coal.2013.11.003. [Google Scholar]
 Gong B., Liu X., Qin G. (2014) A Lattice Boltzmann model for multicomponent vaporliquid two phase flow, Petrol. Explor. Dev. 41, 5, 695–702. doi: 10.11698/PED.2014.05.17. [CrossRef] [Google Scholar]
 Javadpour F. (2009) Nanopores and apparent permeability of gas flow in mudrocks (Shales and Siltstone), J. Can. Pet. Technol. 48, 8, 16–21. doi: 10.2118/090816da. [CrossRef] [Google Scholar]
 Javadpour F., Fisher D., Unsworth M. (2007) Nanoscale gas flow in shale gas sediments, J. Can. Pet. Technol. 46, 10, 55–61. doi: 10.2118/071006. [Google Scholar]
 Katz A.J., Thompson A.H. (1985) Fractal sandstone pores: Implications for conductivity and pore formation, Phys. Rev. Lett. 54, 12, 1325–1328. doi: 10.1103/physrevlett.54.1325. [CrossRef] [PubMed] [Google Scholar]
 Kim T.W., Tokunaga T.K., Shuman D.B., Stephen R.S., Matt N., Lanzirotti A. (2012) Thickness measurements of nanoscale brine films on silica surfaces under geologic CO_{2} sequestration conditions using synchrotron Xray fluorescence, Water Resour. Res. 48, 9. doi: 10.1029/2012wr012200. [Google Scholar]
 Klinkenberg L.J. (1941) The permeability of porous media to liquids and gases, SOCAR Proc. 2, 2, 200–213. doi: 10.5510/ogp20120200114. [Google Scholar]
 Knudsen M. (1934) Die Gesetze der Molekularströmung und der inneren Reibungsströmung der Gase durch Röhren, Ann. Phys. 333, 1, 75–130. doi: 10.1002/andp.19093330106. [Google Scholar]
 Li Y., Li X., Teng S., Xu D. (2014) Improved models to predict gas–water relative permeability in fractures and porous media, J. Nat. Gas Sci. Eng. 19, 7, 190–201. doi: 10.1016/j.jngse.2014.05.006. [Google Scholar]
 Liu Q., Shen P., Yang P. (2002) Pore scale network modelling of gas slippage in tight porous media, Contemp. Math. 295, 367–375. doi: 10.1090/conm/295/05027. [CrossRef] [Google Scholar]
 Majumdar A.A., Bhushan B. (1990) Role of fractal geometry in roughness characterization and contact mechanics of surfaces, ASME J. Tribol. 112, 2, 205–216. doi: 10.1115/1.2920243. [Google Scholar]
 Marle C. (1981) Multiphase flow in porous media, Éditions Technip, France. [Google Scholar]
 Sampath K., Keighim C.W. (1982) Factors affecting gas slippage in tight sandstones of cretaceous age in the Uinta Basin, J. Pet. Technol. 34, 11, 2715–2720. doi: 10.2118/9872pa. [CrossRef] [Google Scholar]
 Schaaf S.A., Chambré P.L. (1961) Flow of rarefied gases, Princeton University Press, Princeton. [Google Scholar]
 Singh H., Javadpour F., Ettehadtavakkol A., Darabi H. (2014) Nonempirical Apparent Permeability of Shale, SPE Reserv. Evalu. Eng. 17, 3, 414–424. doi: 10.2118/170243pa. [CrossRef] [Google Scholar]
 Tocci G., Joly L., Michaelides A. (2016) Friction of water on graphene and hexagonal boron nitride from ab initio methods: Very different slippage despite very similar interface structures, Nano Lett. 14, 12, 6872–6877. doi: 10.1021/nl502837d. [Google Scholar]
 Wu L. (2008) A slip model for rarefied gas flows at arbitrary Knudsen number, Appl. Phys. Lett. 93, 25, 253103. doi: 10.1063/1.3052923. [Google Scholar]
 Wu K., Li X., Wang C., Yu W., Guo C., Ji D., Ren G., Chen Z. (2014) Apparent permeability for gas flow in shale reservoirs coupling effects of gas diffusion and desorption, in: The Annual Meeting for the Unconventional Resources Technology Conference, 25–27 August, Denver. [Google Scholar]
 Wu K., Li X., Chen Z. (2015a) The mechanism and mathematical model for the adsorbed gas surface diffusion in nanopores of shale gas reservoirs, Sci. Sin. Technol. 45, 5, 525–540. doi: 10.1360/n09201400263. [CrossRef] [Google Scholar]
 Wu K., Li X., Chen Z. (2015b) A model for gas transport through nanopores of shale gas reservoirs, Acta Petrol. Sin. 36, 7, 837–848. [Google Scholar]
 Wu K., Chen Z., Li X. (2015c) Real gas transport through nanopores of varying crosssection type and shape in shale gas reservoirs, Chem. Eng. J. 281, 813–825. doi: 10.2118/175453ms. [Google Scholar]
 Wu K., Li X., Wang C., Chen Z., Yu W. (2015d) A model for gas transport in microfractures of shale and tight gas reservoirs, AIChE J. 61, 6, 2079–2088. doi: 10.1002/aic.14791. [Google Scholar]
 Wu K., Li X., Wang C., Yu W., Chen Z. (2015e) Model for surface diffusion of adsorbed gas in nanopores of shale gas reservoirs, Ind. Eng. Chem. Res. 54, 12, 3225–3236. doi: 10.4043/25662ms. [Google Scholar]
 Wu K., Li X., Chen Z. (2016a) Real gas transport through nanopores of shale gas reservoirs, Sci. Sin. Technol. 46, 1, 68–78. doi: 10.2118/180086ms. [CrossRef] [Google Scholar]
 Wu K., Chen Z., Li X., Guo C., Wei M. (2016b) A model for multiple transport mechanisms through nanopores of shale gas reservoirs with real gas effect–adsorptionmechanic coupling, Int. J. Heat Mass Transfer 93, 408–426. doi: 10.2118/173201ms. [CrossRef] [Google Scholar]
 Wu K., Chen Z., Li J., Li X., Xu J., Dong X. (2017) Wettability effect on nanoconfined water flow, Proc. Nat. Acad. Sci. USA 114, 13, 3358–3363. doi: 10.1016/j.nantod.2017.05.001. [CrossRef] [Google Scholar]
 Xu P., Yu B. (2008) Developing a new form of permeability and KozenyCarman constant for homogeneous porous media by means of fractal geometry, Adv. Water Res. 31, 1, 74–81. doi: 10.1016/j.advwatres.2007.06.003. [CrossRef] [Google Scholar]
 Xu P., Qiu S., Yu B. (2013) Prediction of relative permeability in unsaturated porous media with a fractal approach, Int. J. Heat Mass Transfer 64, 3, 829–837. doi: 10.1021/ef3013322. [CrossRef] [Google Scholar]
 Yang S., Wei J. (2015) Reservoir physics, Petroleum Industry Press, Beijing. [Google Scholar]
 Yu B., Cheng P. (2002) A fractal permeability model for bidispersed porous media, Int. J. Heat Mass Transfer 45, 14, 2983–2993. doi: 10.1016/s00179310(02)000145. [CrossRef] [Google Scholar]
 Yu B., Li J. (2011) Some fractal characters of porous media, Fractals 9, 3, 365–372. doi: 10.1142/s0218348x01000804. [Google Scholar]
 Yu B., Liu W. (2004) Fractal analysis of permeabilities for porous media, AIChE J. 50, 1, 46–57. doi: 10.1002/aic.10004. [Google Scholar]
 Zhang H. (2016) A scanning electron microscopic study of unconventional oil and gas reservoir, Geology Publishing House, Beijing. [Google Scholar]
 Zhang X., Wu C., Liu S. (2017) Characteristic analysis and fractal model of the gaswater relative permeability of coal under different confining pressures, J. Pet. Sci. Eng. 159, 488–496. doi: 10.1016/j.petrol.2017.09.057. [Google Scholar]
All Tables
All Figures
Fig. 1 Knudsen number and gas flow regimes Ertekin et al. (1986). 

In the text 
Fig. 2 Velocity distribution along the circular microtube of single gas/water flow. 

In the text 
Fig. 3 Physical conceptual model of gas/water flow pattern in tight sandstone sample. 

In the text 
Fig. 4 Different model and experimental data in 2 MPa. 

In the text 
Fig. 5 Different model and experimental data in 6 MPa. 

In the text 
Fig. 6 Gas relative permeability in different pressure. 

In the text 