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Modern approaches to pore space scale digital modeling of core structure and multiphase flow

K.M. Gerke, D.V. Korost, M.V. Karsanina, S.R. Korost, R.V. Vasiliev, E.V. Lavrukhin, D.R. Gafurova

Review article



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In current review, we consider the Russian and, mainly, international experience of the “digital core» technology, namely – the possibility of creating a numerical models of internal structure of the cores and multiphase flow at  pore space scale. Moreover, our paper try to gives an answer on a key question for the industry: if digital core technology really allows effective to solve the problems of the oil and gas field, then why does it still not do this despite the abundance of scientific work in this area? In particular, the analysis presented in the review allows us to clarify the generally skeptical attitude to technology, as well as errors in R&D work that led to such an opinion within the oil and gas companies. In conclusion, we give a brief assessment of the development of technology in the near future.

petrophysics, pore space structure, multiphase filtration, computed tomography, physical and mathematical modeling


  • Adler P.M., Jacquin C.G., Thovert J.F. (1992). The formation factor of reconstructed porous-media. Water resources research, 28, pp. 1571–1576.
  • Al-Gharbi Mohammed S., Blunt Martin J. (2005). Dynamic network modeling of two-phase drainage in porous media. Phys. Rev. E, 71, 016308.
  • 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 Journal, 17(1), pp. 219–229.
  • Balashov V.A., A.A. Zlotnik, E.B. Savenkov (2017). Numerical algorithm for simulation of three-dimensional two-phase flows with surface effects within domains with voxel geometry. Keldysh Institute Preprints, 091, 28 p. (In Russ.)
  • Baveye P.C., Laba M., Otten W., et al. (2010). Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data. Geoderma, 157(1-2), pp. 51–63.
  • Bilger C., Aboukhedr M., Vogiatzaki K., Cant R.S. (2017). Evaluation of two-phase flow solvers using Level Set and Volume of Fluid methods. Journal of Computational Physics, 345, pp. 665–686.
  • Biswal B., Manwart C., Hilfer R., Bakke S., Oren P.E. (1999). Quantitative analysis of experimental and synthetic microstructures for sedimentary rock. Physica A, 273(3-4), pp. 452–475.
  • Čapek P., Hejtmánek V., Brabec I., Zikanová A., Kocirik M. (2009). Stochastic Reconstruction of Particulate Media Using Simulated Annealing: Improving Pore Connectivity. Transport in Porous Media, 76, pp. 179–198.
  • Čapek P., Hejtmánek V., Kolafa J., Brabec I. (2011). Transport properties of stochastically reconstructed porous media with improved pore connectivity. Transport in Porous Media, 88, pp. 87–106.
  • Chauhan S., Rühaak W., Anbergen H., Kabdenov A. at al. (2016b). Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study. Solid Earth, 7(4), pp. 1125–1139.
  • Chauhan S., Rühaak W., Khan F., Enzmann F., at al. (2016a). Processing of rock core microtomography images: Using seven different machine learning algorithms. Computers & Geosciences, 86, pp. 120–128.
  • Cnudde V., Boone M.N. (2013). High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Reviews, 123, pp. 1–17.
  • Cnudde V., Masschaele B., Dierick M., Vlassenbroeck J., Van Hoorebeke L. Hoorebeke, Jacobs P. (2006). Recent progress in X-ray CT as a geosciences tool. Applied Geochemistry, 21(5), pp. 826–832.
  • Darman N.H., Pickup G.E., Sorbie K.S. (2002). A comparison of two-phase dynamic upscaling methods based on fluid potentials. Computational Geosciences, 6(1), pp. 5–27.
  • Demianov A., Dinariev O., Evseev N. (2011). Density functional modelling in multiphase compositional hydrodynamics. The Canadian Journal of Chemical Engineering, 89(2), pp. 206–226.
  • Deniz C.M., Xiang S., Hallyburton S., Welbeck A. at al. (2018). Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks. Scientific Reports, 8(1), 16485.
  • Dewers T.A., Heath J., Ewy R., Duranti L. (2012). Three-dimensional pore networks and transport properties of a shale gas formation determined from focused ion beam serial imaging. International journal of oil gas and coal technology, 5, pp. 229–248.
  • Diamond S. (2000). Mercury porosimetry: an inappropriate method for the measurement of pore size distributions in cement-based materials. Cem. Concr. Res., 30, pp. 1517–1525.
  • Dikinya O., Hinz C., Aylmore G. (2008). Decrease in hydraulic conductivity and particle release associated with self-filtration in saturated soil columns. Geoderma, 146, pp. 192–200.
  • Dinariev O.Y., Evseev N.V. (2010). Modeling of surface phenomena in the presence of surface-active agents on the basis of the density-functional theory. Fluid dynamics, 45, pp. 85–95.
  • Dong H., Blunt M.J. (2009). Pore-network extraction from micro-computerized-tomography images. Physical Review E, 80, 036307.
  • Eichheimer P., Thielmann M., Popov A., Golabek G.J., Fujita W., Kottwitz M. O., and Kaus B.J.P. (2019): Pore-scale permeability prediction for Newtonian and non-Newtonian fluids, Solid Earth, 10, pp. 1717–1731,
  • Fatt I. (1956a). The network model of porous media I. Capillary pressure characteristics. Petrol. Trans. AIME, 207, pp. 144–159.
  • Fatt I. (1956b). The network model of porous media II. Dynamic properties of a single size tube network. Petrol. Trans. AIME, 207, pp. 160–163.
  • Fatt I. (1956c). The network model of porous media III. Dynamic properties of networks with tube radius distribution. Petrol. Trans. AIME,  207, pp. 164–181.
  • Gerke K., Karsanina M., Khomyak A., Darmaev B. and Korost D. (2018). Permeability Obtained from Pore-Scale Simulations as a Proxy to Core Orientation in Non-Aligned Rock Material. SPE Russian Petroleum Technology Conference, DOI: 10.2118/191661-18RPTC-MS
  • Gerke K., Karsanina M., Sizonenko T. (2017). Multi-Scale Image Fusion of X-Ray Microtomography and SEM Data to Model Flow and Transport Properties for Complex Rocks on Pore-Level. SPE Russian Petroleum Technology Conference.
  • Gerke K.M., Karsanina M. V. (2021). How pore structure non stationarity compromises flow properties representativity (REV) for soil samples: Pore scale modelling and stationarity analysis. European Journal of Soil Science, 72(2), pp. 527–545.
  • Gerke K.M., Karsanina M.V. (2015). Improving stochastic reconstructions by weighting correlation functions in an objective function. Europhysics Lett.,  111, 56002.
  • Gerke K.M., Karsanina M.V., Mallants D. (2015b). Universal stochastic multi-scale image fusion: An example application for shale rock. Scientific Reports, 5, 15880.
  • Gerke K.M., Karsanina M.V., Sizonenko T.O., Miao X., Gafurova D.R., Korost D.V. (2013). Multi-scale image fusion of X-ray microtomography and SEM data to model flow and transport properties for complex rocks on pore-level. SPE Russian Petroleum Technology Conference. Moscow.
  • Gerke K.M., Karsanina M.V., Sizonenko T.O., Miao X., Gafurova D.R., Korost D.V. (2012). Multi-scale image fusion of X-ray microtomography and SEM data to model flow and transport properties for complex rocks on pore-level. SPE Russian Petroleum Technology Conference. Moscow.
  • Gerke K.M., Karsanina M.V., Vasilyev R.V., Mallants D. (2014). Improving pattern reconstruction using directional correlation functions. Europhysics Lett., 106, 66002.
  • Gerke K.M., Korostilev E.V., Romanenko K.A., Karsanina M.V. (2021). Going submicron in the precise analysis of soil structure: A FIB-SEM imaging study at nanoscale. Geoderma, 383, 114739.
  • Gerke K.M., Sizonenko T.O., Karsanina M.V., Katsman R., Korost D.V. (2019). Influence of boundary conditions on the permeability tensor. Proc. Int. Geological and Geophysical Conf. and Exhib. “GeoEurasia 2019”. Tver: PoliPRESS, pp. 474–477. (In Russ.)
  • Gerke K.M., Vasilyev R.V., Khirevich S., Karsanina M.V., at al. (2018b). Finite-difference method Stokes solver (FDMSS) for 3D pore geometries: Software development, validation and case studies. Computers & Geosciences, 114, pp. 41–58.
  • Giffin S., Littke R., J Klaver. et al (2013). Application of BIB-SEM technology to characterize macropore morphology in coal. International journal of coal geology, 114, pp. 85–95.
  • Gostick J., Aghighi M., Hinebaugh J., Tranter T., at al. (2016). OpenPNM: a pore network modeling package. Computing in Science & Engineering, 18(4), pp. 60–74.
  • Gostick J.T. (2017). Versatile and efficient pore network extraction method using marker-based watershed segmentation. Physical Review E, 96(2), 023307.
  • Hannaoui R., Horgue P., Larachi F., Haroun Y., Augier F., Quintard M., Prat M. (2015). Pore-network modeling of trickle bed reactors: Pressure drop analysis. Chemical Engineering Journal, 262, pp. 334–343.
  • Hashemi M.A., Khaddour G., François B., Massart T.J., Salager S. (2014). A tomographic imagery segmentation methodology for three-phase geomaterials based on simultaneous region growing. Acta Geotechnica, 9(5), pp. 831–846.
  • Heiba A.A., Jerauld G.R., Davis H.T., Scriven L.E. (1986). Mechanism-based simulation of oil recovery processes. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
  • Holmes D.W., Williams J.R., Tilke P., Leonardi C.R. (2016). Characterizing flow in oil reservoir rock using SPH : Absolute permeability. Comput. Part. Mech., 3, pp. 141–154.
  • Hu D., Ronhovde P., Nussinov Z. (2012). Replica inference approach to unsupervised multiscale image segmentation. Physical Review E, 85(1), 016101.
  • Iassonov P., Gebrenegus T., Tuller M. (2009). Segmentation of X-ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures. Water Resources Research, 45(9).
  • Iglovikov V., Mushinskiy S., & Osin V. (2017). Satellite imagery feature detection using deep convolutional neural network: A Kaggle competition. arXiv preprint: 1706.06169.
  • Jang J., Narsilio G.A., Santamarina J.C. (2011). Hydraulic conductivity in spatially varying media–a pore-scale investigation. Geophysical journal international, 184(3), pp. 1167–1179.
  • Jiang Z., Van Dijke M.I.J., Wu K., Couples G.D., Sorbie K.S., Ma J. (2012). Stochastic pore network generation from 3D rock images. Transport in porous media, 94(2), pp. 571–593.
  • Jiang Z., Wu K., Couples G., Van Dijke M., Sorbie K. and Ma J. (2007). Efficient extraction of networks from three dimensional porous media. Water Resources Research, 43(12), W12S03.
  • Jiao Y., Chawla N. (2014). Modeling and characterizing anisotropic inclusion orientation in heterogeneous material via directional cluster functions and stochastic microstructure reconstruction. Appl. Phys., 115, 093511.
  • Jiao Y., Stillinger F.H., Torquato S. (2009). A superior descriptor of random textures and its predictive capacity. Proceedings of National Academy of Science, 106, 17634.
  • Jiao Y., Stillinger F.H., Torquato S.. (2008). Modeling heterogeneous materials via two-point correlation functions. II. Algorithmic details and applications. Physical Review E, 77, 031135.
  • Jivkov A., Hollis C., Etiese F., McDonald S., Withers P., (2013). A novel architecture for pore network modelling with applications to permeability of porous media. Journal of Hydrology, 486, pp. 246–258.
  • Joos J., Carraro Th., Weber A., Ivers-Tiffee E. (2011). Reconstruction of porous electrodes by FIB/SEM for detailed microstructure modeling. Journal of Power Sources, 196, pp. 7302–7307.
  • Karimpouli S., Tahmasebi P. (2019). Segmentation of digital rock images using deep convolutional autoencoder networks. Computers & geosciences, 126, pp. 142–150.
  • Karsanina M.V., Gerke K.M. (2018). Hierarchical Optimization: Fast and Robust Multiscale Stochastic Reconstructions with Rescaled Correlation Functions. Physical Review Letters, 121(26).
  • Karsanina M.V., Gerke K.M., Skvortsova E.B., Ivanov A.L., Mallants D. (2018). Enhancing image resolution of soils by stochastic multiscale image fusion. Geoderma, 314, pp. 138–145.
  • Karsanina M.V., Gerke K.M., Skvortsova E.B., Mallants D. (2015). Universal spatial correlation functions for describing and reconstructing soil microstructure. PloS ONE, 10(5), e0126515.
  • Khan F., Enzmann F., Kersten M. (2016). Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples. Solid Earth, 7(2), pp. 481–492.
  • Khirevich S., Daneyko A., Höltzel A., Seidel-Morgenstern A., Tallarek U. (2010). Statistical analysis of packed beds, the origin of short-range disorder, and its impact on eddy dispersion. Journal of Chromatography A, 1217, pp. 4713–4722.
  • Khirevich S., Ginzburg I., Tallarek U. (2015). Coarse-and fine-grid numerical behavior of MRT/TRT lattice-Boltzmann schemes in regular and random sphere packings. Comput. Phys., 281, pp. 708–742.
  • Khirevich S., Höltzel A., Seidel-Morgenstern A., Tallarek U. (2012). Geometrical and topological measures for hydrodynamic dispersion in confined sphere packings at low column-to-particle diameter ratios. Journal of Chromatography A, 1262, pp. 77–91.
  • Khirevich S., Petzek T. (2018). Behavior of numerical error in pore-scale lattice Boltzmann simulations with simple bounce-back rule: Analysis and highly accurate extrapolation. Physics of Fluids, 30(9): 093604.
  • Korost D.V., Gerke K.M. (2012). Computation of reservoir properties based on 3D structure of porous media. SPE Russian Oil and Gas Exploration and Production Technical Conference and Exhibition.
  • Lavrukhin E.V., Gerke K.M., Sizonenko T.O., Karsanina M.V., Korost D.V., Tarasenko S.S. (2021). Segmentation and classification of porous media X-ray tomography images using convolutional neural networks. Advances in Water Resources (article accepted for consideration).
  • Lavrukhin E.V., Karsanina M.V., Izmailov A.F., Gerke K.M. (2019). Increasing the volume of numerical modeling at the scale of pores: the method of dividing into subcubes for the selection of porous network models. Delovoy zhurnal Neftegaz, 7, pp. 70–75. (In Russ.)
  • Lemmens L., Rogiers B., Jacques D., Huysmans M., Swennen R., Urai J.L. et al. (2019). Nested multiresolution hierarchical simulated annealing algorithm for porous media reconstruction. Physical Review E, 100(5), 053316.
  • Li H., Chawla N., Jiao Y. (2014). Reconstruction of heterogeneous materials via stochastic optimization of limited-angle X-ray tomographic projections. Scripta Materialia, 86, pp. 48–51.
  • Li H., Chen P.E., Jiao Y. (2017). Accurate Reconstruction of Porous Materials via Stochastic Fusion of Limited Bimodal Microstructural Data. Transport in Porous Media, pp. 1–18.
  • Lindquist W. B., Lee S. M., Coker D. A., Jones K. W., Spanne P. (1996). Medial axis analysis of void structure in three dimensional tomographic images of porous media. Journal of Geophysical Research: Solid Earth, 101(B4), pp. 8297–8310.
  • Loucks R.G., Reed R.M., Ruppel S.C. et al. (2012). Spectrum of pore types and networks in mudrocks and a descriptive classification for matrix-related mudrock pores. AAPG Bulletin, 96, pp. 1071–1098.
  • Manwart C., Hilfer R. (1999). Reconstruction of random media using Monte-Carlo methods. Physical Review E, 59, pp. 5596–5599.
  • Mason G., Morrow N.R. (1991). Capillary Behavior of a Perfectly Wetting Liquid in Irregular Triangular Tubes. Journal of Colloid and Interface Science, 141, pp. 262–274.
  • Mehmani A., Prodanovic M., Javadpour F. (2013). Multiscale, Multiphysics Network Modeling of Shale Matrix Gas Flows. Transport in porous media, 99, pp. 377–390.
  • Miao X., Gerke K.M., Sizonenko T.O. (2017). A new way to parameterize hydraulic conductances of pore elements: A step forward to create pore-networks without pore shape simplifications. Adv. Water Resour, 105, pp. 162–172.
  • Nesterova I.S., Gerke K.M. (2021). Simulations of nanoscale gas flow with Knudsen diffusion and slip flow. Matem. Mod., 33(3), pp. 85–97.
  • Oh W., Lindquist B. (1999). Image thresholding by indicator kriging. IEEE Trans. Pattern Anal. Mach. Intell., 21, pp. 590–602.
  • Oh W., Lindquist W.B. (1999). Image thresholding by indicator kriging. IEEE Transactions On Pattern Analysis And Machine Intelligence, 21, pp. 590–602.
  • Okabe H., Blunt M.J. (2007). Pore space reconstruction of vuggy carbonates using microtomography and multiple-point statistics. Water Resources Research, 43, pp. 0043–1397.
  • Øren P.E., Bakke S. (2002). Process based reconstruction of sandstones and prediction of transport properties. Transport in Porous Media, 46, pp. 311–314.
  • Øren P.E., Bakke S., Arntzen O.J. (1998). Extending predictive capabilities to network models. SPE Journal, 3, pp. 324–336.
  • Otsu N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), pp. 62–66.
  • Pesaresi M., Benediktsson J.A. (2001). A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE transactions on Geoscience and Remote Sensing, 39(2), pp. 309–320.
  • Piasecki R. (2011). Microstructure reconstruction using entropy descriptors. Proceedings of the Royal Society: A, 467, pp. 806–821.
  • Piri M., Blunt M.J. (2005). Three-dimensional mixed-wet random pore-scale network modeling of two- and three-phase flow in porous media. I. Model description. Physical Review E, 71, 026301.
  • Raeini A.Q., Blunt M.J., Bijeljic B. (2012). Modelling two-phase flow in porous media at the pore scale using the volume-of-fluid method. Journal of Computational Physics, 231, pp. 5653–5668.
  • Raoof A., Hassanizadeh S.M. (2010). A new formulation for pore network modeling of two phase flow. Water Resources Research, 48(1).
  • Renard P., Genty A., Stauffer F. (2001). Laboratory determination of the full permeability tensor. Geophys. Res. Solid Earth, 106, pp. 26443–26452.
  • Roberts A.P., Teubner M. (1995). Transport-Properties of Heterogeneous Materials Derived From Gaussian Random-Fields – Bounds And Simulation. Physical Review E, 51, pp. 4141–4154.
  • Rokhforouz M. R., Akhlaghi Amiri H.A. (2017). Phase-field simulation of counter-current spontaneous imbibition in a fractured heterogeneous porous medium. Physics of Fluids, 29(6), 062104.
  • Ryazanov A., van Dijke M.I.J. and Sorbie K.S. (2009). Two-phase pore-network modelling: Existence of oil layers during water invasion. Transport in Porous Media, 80(1), pp. 79–99.
  • Saucier A., Richer J., Muller J. (2002). Assessing the scope of the multifractal approach to textural characterization with statistical reconstructions of images. Physica A, 311, pp. 231–259.
  • Schlüter S., Vogel H., Vanderborght J. (2013). Combined Impact of Soil Heterogeneity and Vegetation Type on the Annual Water Balance at the Field Scale. Vadose Zone Journal, 12(4).
  • Schlüter S., Weller U., Vogel H.J. (2010). Segmentation of X-ray microtomography images of soil using gradient masks. Comput. Geosci., 36, pp. 1246–1251.
  • Sedaghat M.H., & Azizmohammadi S. (2019). Representative-elementary-volume analysis of two-phase flow in layered rocks. SPE Reservoir Evaluation & Engineering, 22(03), 1–075.
  • Sedaghat M.H., Gerke K., Azizmohammadi S., & Matthai S.K. (2016). Simulation-based determination of relative permeability in laminated rocks. Energy Procedia, 97, 433–439.
  • Sezgin M., & Sankur B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic imaging, 13(1), 146–165.
  • Shabro V., Torres-Verdín C., Javadpour F., & Sepehrnoori K. (2012). Finite-difference approximation for fluid-flow simulation and calculation of permeability in porous media. Transport in porous media, 94(3), 775–793.
  • Sheng Q., & Thompson K. (2013). Dynamic coupling of pore-scale and reservoir scale models for multiphase flow. Water Resources Research, 49(9), 5973–5988.
  • Sheppard A.P., Sok R.M., Averdunk H. (2004). Techniques for image enhancement and segmentation of tomographic images of porous materials. Physica A, 339(1-2), pp. 145–151.
  • Sheppard A.P., Sok R.M., Averdunk H. (2005, August). Improved pore network extraction methods. International Symposium of the Society of Core Analysts, 2125, pp. 1–11.
  • Shulakova V., Pervukhina M., Mueller T.M. et al. (2013). Computational elastic up-scaling of sandstone on the basis of X-ray micro-tomographic images. Geophysical Prospecting, 61, pp. 287–301.
  • Silin D., & Patzek T. (2006). Pore space morphology analysis using maximal inscribed spheres. Physica A, 371(2), pp. 336–360.
  • Tahmasebi P., Hezarkhani A., & Sahimi M. (2012). Multiple-point geostatistical modeling based on the cross-correlation functions. Computational Geosciences, 16(3), pp. 779–797.
  • Tahmasebi P., Sahimi M. (2013). Cross-correlation function for accurate reconstruction of heterogeneous media. Physical review letters, 110(7), 078002.
  • Thovert J.-F., Adler P. M. (2011). Grain reconstruction of porous media: Application to a Bentheim sandstone. Physical Review E, 83, 056116.
  • Torquato S. (1991). Random heterogeneous media: Microstructure and improved bounds on effective properties. Appl. Mech. Rev., 44, pp. 37–76.
  • Torquato S. (2002). Random Heterogeneous Materials: Microstructure and Macroscopic Properties. Springer Verlag. New York, 701 p.
  • Valvatne P.H., Blunt M.J. (2004). Predictive pore scale modeling of two phase flow in mixed wet media. Water resources research, 40(7).
  • Varfolomeev I., Yakimchuk I., Safonov I. (2019). An Application of Deep Neural Networks for Segmentation of Microtomographic Images of Rock Samples. Computers, 8(4), pp. 72.
  • Wen X.H., Gómez-Hernández J.J. (1996). Upscaling hydraulic conductivities in heterogeneous media: An overview. Journal of Hydrology, 183(1–2), ix-xxxii.
  • Wildenschild D., Sheppard A.P. (2013). X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems. Advances in Water Resources, 51, pp. 217–246.
  • Wu K.J., Nunan N., Crawford J.W., Young I.M., Ritz K. (2004). An efficient Markov chain model for the simulation of heterogeneous soil structure. Soil Sci.Soc.Am.J., 68(2), pp. 346–351.
  • Yang Y.S., Liu K.Y., Mayo S., Tulloh A., Clennell M.B., Xiao T.Q. (2013). A data-constrained modelling approach to sandstone microstructure characterisation. Journal of Petroleum Science and Engineering, 105, pp. 76–83.
  • Yeong C.L.Y., Torquato S. (1998a). Reconstructing random media. Physical review E, 57(1), pp. 495–506.
  • Yeong C.L.Y., Torquato S. (1998b). Reconstructing random media. II. Three-dimensional media from two-dimensional cuts. Phys. Rev. E, 58, pp. 224–233.
  • Zakirov T., Galeev A. (2019). Absolute permeability calculations in micro-computed tomography models of sandstones by Navier-Stokes and lattice Boltzmann equations. International Journal of Heat and Mass Transfer, 129, pp. 415–426.
  • Zeinijahromi, A., Farajzadeh, R., Bruining, J. H., & Bedrikovetsky, P. (2016). Effect of fines migration on oil–water relative permeability during two-phase flow in porous media. Fuel, 176, pp. 222–236.

Kirill M. Gerke
Sсhmidt Institute of Physics of the Earth of the RAS
10, build.1, B. Gruzinskaya str., Moscow, 123242, Russian Federation

Dmitry V. Korost
Lomonosov Moscow State University
1, Leninskie gory, Moscow, 119234, Russian Federation

Marina V. Karsanina
Sсhmidt Institute of Physics of the Earth of the RAS
10, build.1, B. Gruzinskaya str., Moscow, 123242, Russian Federation

Svetlana R. Korost
Lomonosov Moscow State University
1, Leninskie gory, Moscow, 119234, Russian Federation

Roman V. Vasiliev
Sсhmidt Institute of Physics of the Earth of the RAS
10, build.1, B. Gruzinskaya str., Moscow, 123242, Russian Federation

Efim V. Lavrukhin
Lomonosov Moscow State University
1, Leninskie gory, Moscow, 119234, Russian Federation

Dina R. Gafurova
Lomonosov Moscow State University
1, Leninskie gory, Moscow, 119234, Russian Federation


For citation:

Gerke K.M., Korost D.V., Karsanina M.V., Korost S.R., Vasiliev R.V., Lavrukhin E.V., Gafurova D.R. (2021). Modern approaches to pore space scale digital modeling of core structure and multiphase flow. Georesursy = Georesources, 23(2), pp. 197–213. DOI: