[1]
Sahimi M. Flow phenomena in rocks: from continuum models to fractals, percolation, cellular automata, and simulated annealing. Reviews of modern physics. 1993;65(4):1393., https://doi.org/10.1103/RevModPhys.65.1393.
[2]
Trogadas P, Ramani V, Strasser P, Fuller TF, Coppens MO. Hierarchically structured nanomaterials for electrochemical energy conversion. Angewandte Chemie International Edition. 2016;55(1):122-48., https://doi.org/10.1002/anie.201506394.
[3]
Jackson EA, Hillmyer MA. Nanoporous membranes derived from block copolymers: from drug delivery to water filtration. ACS nano. 2010;4(7):3548-53., https://doi.org/10.1021/nn1014006.
[4]
De Jong J, Lammertink RG, Wessling M. Membranes and microfluidics: a review. Lab on a Chip. 2006;6(9):1125-39.,
https://doi.org/10.1039/B603275C.
[5]
Velev OD, Lenhoff AM. Colloidal crystals as templates for porous materials. Current opinion in colloid & interface science. 2000;5(1-2):56-63., https://doi.org/10.1016/S1359-0294(00)00039-X.
[6]
Wildenschild D, Sheppard AP. X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems. Advances in Water resources. 2013;51:217-46., https://doi.org/10.1016/j.advwatres.2012.07.018.
[7]
Andisheh-Tadbir M, Orfino FP, Kjeang E. Three-dimensional phase segregation of micro-porous layers for fuel cells by nano-scale X-ray computed tomography. Journal of Power Sources. 2016;310:61-9., https://doi.org/10.1016/j.jpowsour.2016.02.001.
[8]
Andrä H, Combaret N, Dvorkin J, Glatt E, Han J, Kabel M, et al. Digital rock physics benchmarks—Part I: Imaging and segmentation. Computers & Geosciences. 2013;50:25-32., https://doi.org/10.1016/j.cageo.2012.09.005.
[9]
Blunt MJ, Bijeljic B, Dong H, Gharbi O, Iglauer S, Mostaghimi P, et al. Pore-scale imaging and modelling. Advances in Water resources. 2013;51:197-216., https://doi.org/10.1016/j.advwatres.2012.03.003.
[10]
King MJS, P.; Blunt, M.J. Digital rock physics: Current state and future directions. Annual Review of Fluid Mechanics. 2019;51:501-32., http://10.1146/annurev-fluid-010518-040437.
[11]
Fu H, Wang X, Zhang L, Gao R, Li Z, Xu T, et al. Investigation of the factors that control the development of pore structure in lacustrine shale: A case study of block X in the Ordos Basin, China. Journal of Natural Gas Science and Engineering. 2015;26:1422-32., https://doi.org/10.1016/j.jngse.2015.07.025.
[12]
Cnudde V, Boone MN. High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Reviews. 2013;123:1-17., https://doi.org/10.1016/j.earscirev.2013.04.003.
[13]
Mitra R, Jun, Y., & Shah, S. N. Fracture network characterization in unconventional reservoirs using digital rock physics. Journal of Petroleum Science and Engineering. 2020;184:106609.
[14]
Javanmardi SL, M.; Zhang, X.; Huang, H. Integration of digital rock analysis with petrophysical and fluid flow modeling: A review. Journal of Petroleum Science and Engineering. 2020:187.
[15]
Li S, Jiang, L., Jia, Y., Yao, J., Cai, J., & Li, J. Application of digital rock technology in unconventional oil and gas development. Journal of Natural Gas Science and Engineering. 2018:51, 4-67.
[16]
Abass HY, M. A.; Ali, I. M. The applications of digital rock physics in petroleum reservoirs: A review. Journal of Natural Gas Science and Engineering. 2019;70:102994.
[17]
Chatzis I, Dullien FA. Modelling pore structure by 2-D and 3-D networks with applicationto sandstones. Journal of Canadian Petroleum Technology. 1977;16(01)., https://doi.org/10.2118/77-01-09.
[18]
Mohanty KK. Fluids in porous media: two-phase distribution and flow: University of Minnesota; 1981.
[19]
Celia MA, Reeves PC, Ferrand LA. Recent advances in pore scale models for multiphase flow in porous media. Reviews of Geophysics. 1995;33(S2):1049-57., https://doi.org/10.1029/95RG00248.
[20]
Blunt MJ. Flow in porous media—pore-network models and multiphase flow. Current opinion in colloid & interface science. 2001;6(3):197-207., https://doi.org/10.1016/S1359-0294(01)00084-X.
[21]
Primkulov BK, Talman S, Khaleghi K, Shokri AR, Chalaturnyk R, Zhao B, et al. Quasistatic fluid-fluid displacement in porous media: Invasion-percolation through a wetting transition. Physical Review Fluids. 2018;3(10):104001., https://doi.org/10.1103/PhysRevFluids.3.104001.
[22]
Dong H. Micro-CT Imaging and Pore Network Extraction, Imperial College, London: PhD dissertation; 2007.
[23]
Sahimi M. Flow and transport in porous media and fractured rock: from classical methods to modern approaches: John Wiley & Sons; 2011.
[24]
Kohanpur AH, Rahromostaqim M, Valocchi AJ, Sahimi M. Two-phase flow of CO2-brine in a heterogeneous sandstone: Characterization of the rock and comparison of the lattice-Boltzmann, pore-network, and direct numerical simulation methods. Advances in Water Resources. 2020;135:103469., https://doi.org/10.1016/j.advwatres.2019.103469.
[25]
Fathiganjehlou A, Eghbalmanesh A, Peters E, Baltussen MW, Buist KA, Kuipers J, editors. Numerical Study of Pressure Drop inside a Spherical Packed Bed: A Comparison of the Pore Network Model and a Immersed Boundary Method. 15th International Conference on Gas-Liquid & Gas-Liquid-Solid Reactor Engineering GLS 2022; 2022: American Institute of Chemical Engineers (AIChE)., https://scholar.google.com/scholar?oi=bibs&cluster=11033657923031815714&btnI=1&hl=en
[26]
Khan ZA, Salaberri PAG, Heenan TM, Jervis R, Shearing PR, Brett D, et al. Probing the structure-performance relationship of lithium-ion battery cathodes using pore-networks extracted from three-phase tomograms. Journal of The Electrochemical Society. 2020;167(4):040528., https://iopscience.iop.org/article/10.1149/1945-7111/ab7bd8/meta#:~:text=10.1149/1945%2D7111/ab7bd8.
[27]
Pavuluri S. Direct numerical simulations of spontaneous imbibition at the pore-scale: impact of parasitic currents and dynamic capillary barriers: Heriot-Watt University; 2019.
[28]
Sun H, Belhaj H, Tao G, Vega S, Liu L. Rock properties evaluation for carbonate reservoir characterization with multi-scale digital rock images. Journal of Petroleum Science and Engineering. 2019;175:654-64., https://doi.org/10.1016/j.petrol.2018.12.075.
[29]
Da Wang Y, Armstrong RT, Mostaghimi P. Enhancing resolution of digital rock images with super resolution convolutional neural networks. Journal of Petroleum Science and Engineering. 2019;182:106261., https://doi.org/10.1016/j.petrol.2019.106261.
[30]
Pringle J, Westerman A, Clark J, Drinkwater N, Gardiner A. 3D high-resolution digital models of outcrop analogue study sites to constrain reservoir model uncertainty: an example from Alport Castles, Derbyshire, UK. Petroleum Geoscience. 2004;10(4):343-52., https://doi.org/10.1144/1354-079303-617.
[31]
Yang Y, Liu Z, Yao J, Zhang L, Ma J, Hejazi SH, et al. Flow simulation of artificially induced microfractures using digital rock and lattice Boltzmann methods. Energies. 2018;11(8):2145., https://doi.org/10.3390/en11082145.
[32]
Nimmagadda SL, Dreher H. On new emerging concepts of petroleum digital ecosystem. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2012;2(6):457-75., https://doi.org/10.1002/widm.1070.
[33]
Priest T. Extraction not creation: The history of offshore petroleum in the Gulf of Mexico. Enterprise & Society. 2007;8(2):227-67., https://doi.org/10.1093/es/khm027.
[34]
Takahashi KI, Gautier DL. A brief history of oil and gas exploration in the southern San Joaquin Valley of California. US Geological Survey; 2007. Report No.: 2330-7102., https://doi.org/10.3133/pp17133.
[35]
Yang Y-F, Wang K, Lv Q-F, Askari R, Mei Q-Y, Yao J, et al. Flow simulation considering adsorption boundary layer based on digital rock and finite element method. Petroleum Science. 2021;18:183-94., https://doi.org/10.1007/s12182-020-00476-4.
[36]
Koroteev D, Tekic Z. Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Energy and AI. 2021;3:100041., https://doi.org/10.1016/j.egyai.2020.100041.
[37]
D’Almeida AL, Bergiante NCR, de Souza Ferreira G, Leta FR, de Campos Lima CB, Lima GBA. Digital transformation: a review on artificial intelligence techniques in drilling and production applications. The International Journal of Advanced Manufacturing Technology. 2022;119(9-10):5553-82.
[38]
Li X, Li B, Liu F, Li T, Nie X. Advances in the application of deep learning methods to digital rock technology. Advances in Geo-Energy Research. 2023;8(1):5-18.
[39]
Siddig OM, Al-Afnan SF, Elkatatny SM, Abdulraheem A. Drilling data-based approach to build a continuous static elastic moduli profile utilizing artificial intelligence techniques. Journal of Energy Resources Technology. 2022;144(2)., https://doi.org/10.1115/1.4050960.
[40]
Kuang L, He L, Yili R, Kai L, Mingyu S, Jian S, et al. Application and development trend of artificial intelligence in petroleum exploration and development. Petroleum Exploration and Development. 2021;48(1):1-14., https://doi.org/10.1016/S1876-3804(21)60001-0.
[41]
LIU X-f, ZHANG W-w, SUN J-m. Methods of constructing 3-D digital cores: A review. Progress in Geophysics. 2013;28(6):3066-72., https://doi.org/10.6038/pg20130630.
[42]
Rassenfoss S. Digital rocks out to become a core technology. Journal of Petroleum Technology. 2011;63(05):36-41., https://doi.org/10.2118/0511-0036-JPT.
[43]
Lucas-Oliveira E, Araujo-Ferreira AG, Trevizan WA, dos Santos BCC, Bonagamba TJ. Sandstone surface relaxivity determined by NMR T2 distribution and digital rock simulation for permeability evaluation. Journal of Petroleum Science and Engineering. 2020;193:107400., https://doi.org/10.1016/j.petrol.2020.107400.
[44]
Verri I, Della Torre A, Montenegro G, Onorati A, Duca S, Mora C, et al. Development of a digital rock physics workflow for the analysis of sandstones and tight rocks. Journal of Petroleum Science and Engineering. 2017;156:790-800., https://doi.org/10.1016/j.petrol.2017.06.053.
[45]
Sun L, Zhang C, Wang G, Huang Q, Shi Q. Research on the evolution of pore and fracture structures during spontaneous combustion of coal based on CT 3D reconstruction. Energy. 2022;260:125033., https://doi.org/10.1016/j.energy.2022.125033.
[46]
Xiong F, Jiang Q, Xu C. Fast equivalent micro-scale pipe network representation of rock fractures obtained by computed tomography for fluid flow simulations. Rock Mechanics and Rock Engineering. 2021;54:937-53., https://doi.org/10.1007/s00603-020-02284-z.
[47]
ZHAO J, PAN J, HU Y, LI J, YAN B, LI C, et al. Digital rock physics-based studies on effect of pore types on elastic properties of carbonate reservoir Part 1: Imaging processing and elastic modelling. Chinese Journal of Geophysics. 2021;64(2):656-69. https://doi.org/10.6038/cjg2021O0228.
[48]
Ishutov S, Jobe TD, Zhang S, Gonzalez M, Agar SM, Hasiuk FJ, et al. Three-dimensional printing for geoscience: Fundamental research, education, and applications for the petroleum industry. AAPG Bulletin. 2018;102(1):1-26., https://doi.org/10.1306/0329171621117056.
[49]
Du Plessis A, Le Roux SG, Guelpa A. The CT Scanner Facility at Stellenbosch University: An open access X-ray computed tomography laboratory. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 2016;384:42-9., https://doi.org/10.1016/j.nimb.2016.08.005.
[50]
Zandomeneghi D, Voltolini M, Mancini L, Brun F, Dreossi D, Polacci M. Quantitative analysis of X-ray microtomography images of geomaterials: Application to volcanic rocks. Geosphere. 2010;6(6):793-804., https://doi.org/10.1130/GES00561.1.
[51]
Luo M, Glover PW, Zhao P, Li D. 3D digital rock modeling of the fractal properties of pore structures. Marine and Petroleum Geology. 2020;122:104706., https://doi.org/10.1016/j.marpetgeo.2020.104706.
[52]
Li X, Wei W, Wang L, Ding P, Zhu L, Cai J. A new method for evaluating the pore structure complexity of digital rocks based on the relative value of fractal dimension. Marine and Petroleum Geology. 2022;141:105694., https://doi.org/10.1016/j.marpetgeo.2022.105694.
[53]
Fu X, Ding H, Sheng Q, Zhang Z, Yin D, Chen F. Fractal Analysis of Particle Distribution and Scale Effect in a Soil–Rock Mixture. Fractal and Fractional. 2022;6(2):120.,
https://doi.org/10.3390/fractalfract6020120.
[54]
Li Y, He X, Zhu W, AlSinan M, Kwak H, Hoteit H, editors. Digital Rock Reconstruction Using Wasserstein GANs with Gradient Penalty. International Petroleum Technology Conference; 2022: OnePetro., https://doi.org/10.2523/IPTC-21884-MS.
[55]
Cao D, Ji S, Cui R, Liu Q. Multi-task learning for digital rock segmentation and characteristic parameters computation. Journal of Petroleum Science and Engineering. 2022;208:109202., https://doi.org/10.1016/j.petrol.2021.109202.
[56]
Marques Jr A, Horota RK, De Souza EM, Kupssinskü L, Rossa P, Aires AS, et al. Virtual and digital outcrops in the petroleum industry: A systematic review. Earth-Science Reviews. 2020;208:103260., https://doi.org/10.1016/j.earscirev.2020.103260.
[57]
Ramandi HL, Pirzada MA, Saydam S, Arns C, Roshan H. Digital and experimental rock analysis of proppant injection into naturally fractured coal. Fuel. 2021;286:119368., https://doi.org/10.1016/j.fuel.2020.119368.
[58]
Tan M, Su M, Liu W, Song X, Wang S. Digital core construction of fractured carbonate rocks and pore-scale analysis of acoustic properties. Journal of Petroleum Science and Engineering. 2021;196:107771., https://doi.org/10.1016/j.petrol.2020.107771.
[59]
Madonna C, Almqvist BS, Saenger EH. Digital rock physics: Numerical prediction of pressure-dependent ultrasonic velocities using micro-CT imaging. Geophysical Journal International. 2012;189(3):1475-82., https://doi.org/10.1111/j.1365-246X.2012.05437.x.
[60]
Yan W, Sun J, Zhang J, Yuan W, Zhang L, Cui L, et al. Studies of electrical properties of low-resistivity sandstones based on digital rock technology. Journal of Geophysics and Engineering. 2018;15(1):153-63., https://doi.org/10.1088/1742-2140/aa8715.
[61]
Wei J, Li J, Yang Y, Zhang A, Wang A, Zhou X, et al. Digital-Rock Construction of Shale Oil Reservoir and Microscopic Flow Behavior Characterization. Processes. 2023;11(3):697., https://doi.org/10.3390/pr11030697.
[62]
Cao Y, Tang M, Zhang Q, Tang J, Lu S. Dynamic capillary pressure analysis of tight sandstone based on digital rock model. Capillarity. 2020;3(2):28-35., https://doi.org/10.46690/capi.2020.02.02.
[63]
Kameda A. Permeability evolution in sandstone: Digital rock approach: Stanford University; 2005.
[64]
Shikhov I, Arns CH. Evaluation of capillary pressure methods via digital rock simulations. Transport in Porous Media. 2015;107(2):623-40., https://doi.org/10.1007/s11242-015-0459-z.
[65]
Goral J, Panja P, Deo M, Andrew M, Linden S, Schwarz J-O, et al. Confinement effect on porosity and permeability of shales. Scientific reports. 2020;10(1):49., https://doi.org/10.1038/s41598-019-56885-y.
[66]
Tariq Z, Mahmoud M, Alade O, Abdulraheem A, Mustafa A, Mokheimer EM, et al. Productivity Enhancement in Multilayered Unconventional Rocks Using Thermochemicals. Journal of Energy Resources Technology. 2021;143(3):033001.
[67]
Deshenenkov I, MacPherson K, Gorani A, Golab A, editors. Digital Rock Physics for Operational Support of Petroleum Exploration in Saudi Aramco. 77th EAGE Conference and Exhibition 2015; 2015: EAGE Publications BV., https://doi.org/10.3997/2214-4609.201412640.
[68]
Yang B, Wang H, Wang B, Shen Z, Zheng Y, Jia Z, et al. Digital quantification of fracture in full-scale rock using micro-CT images: A fracturing experiment with N2 and CO2. Journal of Petroleum Science and Engineering. 2021;196:107682., https://doi.org/10.1016/j.petrol.2020.107682.
[69]
Clementz DM, editor Alteration of rock properties by adsorption of petroleum heavy ends: implications for enhanced oil recovery. SPE Enhanced Oil Recovery Symposium; 1982: OnePetro., https://doi.org/10.2118/10683-MS.
[70]
Chen S, Yin D, Jiang N, Wang F, Zhao Z. Mechanical properties of oil shale-coal composite samples. International Journal of Rock Mechanics and Mining Sciences. 2019;123:104120.,
https://doi.org/10.1016/j.ijrmms.2019.104120.
[71]
ZHU W, ZHAO L, WANG Y. Digital rock-based broadband dynamic stress-strain simulation method and its applications for characterization of dispersion and attenuation signatures of tight cracked rock. Chinese Journal of Geophysics. 2021;64(6):2086-96., https://doi.org/10.6038/cjg2021O0302.
[72]
ZHU W, SHAN R, NIE X, CHEN W, HAO L. Progress of effective elastic parameter simulation on digital rock. Progress in Geophysics. 2022;37(2):756-65., https://doi.org/10.6038/pg2022FF0182.
[73]
Temizel C, Odi U, Balaji K, Aydin H, Santos JE. Classifying Facies in 3D Digital Rock Images Using Supervised and Unsupervised Approaches. Energies. 2022;15(20):7660., https://doi.org/10.3390/en15207660.
[74]
Anderson RN, editor 'Petroleum Analytics Learning Machine'for optimizing the Internet of Things of today's digital oil field-to-refinery petroleum system. 2017 IEEE International Conference on Big Data (Big Data); 2017: IEEE.
[75]
Saad B, Negara A, Syed Ali S, editors. Digital rock physics combined with machine learning for rock mechanical properties characterization. Abu Dhabi International Petroleum Exhibition & Conference; 2018: OnePetro., https://doi.org/10.2118/193269-MS.
[76]
Petrakov D, Jafarpour H, Qajar J, Aghaei H, Hajiabadi H. Introduction of a workflow for tomographic analysis of formation stimulation using novel nano-based encapsulated acid systems. Journal of Applied Engineering Science. 2021;19(2):327-33., https://doi.org/10.5937/jaes0-29694.
[77]
Agrawal P, Mascini A, Bultreys T, Aslannejad H, Wolthers M, Cnudde V, et al. The impact of pore-throat shape evolution during dissolution on carbonate rock permeability: Pore network modeling and experiments. Advances in Water Resources. 2021;155:103991., https://doi.org/10.1016/j.advwatres.2021.103991.
[78]
Sudakov O, Burnaev E, Koroteev D. Driving digital rock towards machine learning: Predicting permeability with gradient boosting and deep neural networks. Computers & geosciences. 2019;127:91-8., https://doi.org/10.1016/j.cageo.2019.02.002.
[79]
Tahmasebi P, Javadpour F, Enayati SF. Digital rock techniques to study shale permeability: A mini-review. Energy & Fuels. 2020;34(12):15672-85., https://doi.org/10.1021/acs.energyfuels.0c03397.
[80]
Blunt M, Bijeljic B, Dong H, Gharbi O, Iglauer S, Mostaghimi P, et al. " Pore-scale imaging and modelling", Advances in Water Resources. 2013., https://doi.org/10.1016/j.advwatres.2012.03.003.
[81]
De Jonge MD, Vogt S. Hard X-ray fluorescence tomography—an emerging tool for structural visualization. Current opinion in structural biology. 2010;20(5):606-14., https://doi.org/10.1016/j.sbi.2010.09.002.
[82]
Kalam MZ. Digital rock physics for fast and accurate special core analysis in carbonates. New technologies in the oil and gas industry. 2012;2012:201-26., https://doi.org/10.1016/j.sbi.2010.09.002.
[83]
Al-Marzouqi H. Digital rock physics: Using CT scans to compute rock properties. IEEE Signal Processing Magazine. 2018;35(2):121-31., https://doi.org/10.1109/MSP.2017.2784459.
[84]
Baumeister W. Electron tomography: towards visualizing the molecular organization of the cytoplasm. Current opinion in structural biology. 2002;12(5):679-84., https://doi.org/10.1016/S0959-440X(02)00378-0.
[85]
Ma W, Yang Y, Yang W, Lv C, Yang J, Song W, et al. Digital Rock Mechanical Properties by Simulation of True Triaxial Test: Impact of Microscale Factors. Geotechnics. 2023;3(1):3-20., https://doi.org/10.3390/geotechnics3010002.
[86]
Ganzer L, Qi M, Schatzmann S, Sattler C, Wegner J. Evaluation of digital rock methodology to complement rock laboratory experiments. Oil Gas Euro Mag. 2013;39:43-7.
[87]
Knackstedt MA, Latham S, Madadi M, Sheppard A, Varslot T, Arns C. Digital rock physics: 3D imaging of core material and correlations to acoustic and flow properties. The Leading Edge. 2009;28(1):28-33., https://doi.org/10.1190/1.3064143.
[88]
Yousef AA, Al-Saleh S, Al-Kaabi A, Al-Jawfi M. Laboratory investigation of the impact of injection-water salinity and ionic content on oil recovery from carbonate reservoirs. SPE Reservoir Evaluation & Engineering. 2011;14(05):578-93., https://doi.org/10.2118/137634-PA.
[89]
Sun H, Vega S, Tao G. Analysis of heterogeneity and permeability anisotropy in carbonate rock samples using digital rock physics. Journal of petroleum science and engineering. 2017;156:419-29., https://doi.org/10.1016/j.petrol.2017.06.002.
[90]
Bera A, Shah S. A review on modern imaging techniques for characterization of nanoporous unconventional reservoirs: Challenges and prospects. Marine and Petroleum Geology. 2021;133:105287., https://doi.org/10.1016/j.marpetgeo.2021.105287.
[91]
Song Z, Zhou QY. Micro-scale granite permeability estimation based on digital image analysis. Journal of Petroleum Science and Engineering. 2019;180:176-85., https://doi.org/10.1016/j.petrol.2019.05.037.
[92]
Nachev VA, Kazak AV, Turuntaev SB, editors. 3D digital mineral-mechanical modeling of complex reservoirs rocks for understanding fracture propagation at microscale. SPE Russian Petroleum Technology Conference; 2020: OnePetro., https://doi.org/10.2118/201979-MS.
[93]
Liu S, Sang S, Wang G, Ma J, Wang X, Wang W, et al. FIB-SEM and X-ray CT characterization of interconnected pores in high-rank coal formed from regional metamorphism. Journal of Petroleum Science and Engineering. 2017;148:21-31., https://doi.org/10.1016/j.petrol.2016.10.006.
[94]
Goral J, Miskovic I, Gelb J, Andrew M, editors. Correlative XRM and FIB-SEM for (non) organic pore network modeling in Woodford shale rock matrix. International Petroleum Technology Conference; 2015: OnePetro., https://doi.org/10.2523/IPTC-18477-MS.
[95]
Jin X, Yu C, Wang X, Liu X, Li J, Jiao H, et al., editors. Multi-scale digital rock quantitative evaluation technology on complex reservoirs. SPE Asia Pacific Oil and Gas Conference and Exhibition; 2018: OnePetro., https://doi.org/10.2118/191878-18APOG-MS.
[96]
Walls JD, Diaz E, Cavanaugh T, editors. Shale reservoir properties from digital rock physics. SPE/EAGE European Unconventional Resources Conference and Exhibition; 2012: OnePetro., https://doi.org/10.2118/152752-MS.
[97]
Jackson SJ, Niu Y, Manoorkar S, Mostaghimi P, Armstrong RT. Deep learning of multi-resolution X-ray micro-CT images for multi-scale modelling. arXiv preprint arXiv:211101270. 2021., https://doi.org/10.48550/arXiv.2111.01270.
[98]
Dong H, Blunt MJ. Pore-network extraction from micro-computerized-tomography images. Physical review E. 2009;80(3):036307., https://doi.org/10.1103/PhysRevE.80.036307.
[99]
Silin D, Tomutsa L, Benson SM, Patzek TW. Microtomography and pore-scale modeling of two-phase fluid distribution. Transport in porous media. 2011;86(2):495-515., https://doi.org/10.1007/s11242-010-9636-2.
[100]
Hinebaugh J, Fishman Z, Bazylak A. Unstructured pore network modeling with heterogeneous PEMFC GDL porosity distributions. Journal of The Electrochemical Society. 2010;157(11):B1651., https://doi.org/ 10.1149/1.3486095.
[101]
Bryant SL, Mellor DW, Cade CA. Physically representative network models of transport in porous media. AIChE Journal. 1993;39(3):387-96., https://doi.org/10.1002/aic.690390303.
[102]
Thiedmann R, Manke I, Lehnert W, Schmidt V. Random geometric graphs for modelling the pore space of fibre-based materials. Journal of materials science. 2011;46(24):7745-59., https://doi.org/10.1007/s10853-011-5754-7.
[103]
Ioannidis MA, Chatzis I. Network modelling of pore structure and transport properties of porous media. Chemical Engineering Science. 1993;48(5):951-72., https://doi.org/10.1016/0009-2509(93)80333-L.
[104]
Hunt A. Basic transport properties in natural porous media. Complexity. 2005;10(1):22-37., https://doi.org/ 10.1002/cplx.20067.
[105]
Rebai M, Prat M. Scale effect and two-phase flow in a thin hydrophobic porous layer. Application to water transport in gas diffusion layers of proton exchange membrane fuel cells. Journal of Power Sources. 2009;192(2):534-43., https://doi.org/10.1016/j.jpowsour.2009.02.090.
[106]
Blunt MJ, Jackson MD, Piri M, Valvatne PH. Detailed physics, predictive capabilities and macroscopic consequences for pore-network models of multiphase flow. Advances in Water Resources. 2002;25(8-12):1069-89., https://doi.org/10.1016/S0309-1708(02)00049-0.
[107]
Gostick JT, Ioannidis MA, Fowler MW, Pritzker MD. Pore network modeling of fibrous gas diffusion layers for polymer electrolyte membrane fuel cells. Journal of Power Sources. 2007;173(1):277-90., https://doi.org/10.1016/j.jpowsour.2007.04.059.
[108]
Reeves PC, Celia MA. A functional relationship between capillary pressure, saturation, and interfacial area as revealed by a pore‐scale network model. Water resources research. 1996;32(8):2345-58., https://doi.org/10.1029/96WR01105.
[109]
Joekar-Niasar V, Hassanizadeh S. Analysis of fundamentals of two-phase flow in porous media using dynamic pore-network models: A review. Critical reviews in environmental science and technology. 2012;42(18):1895-976., https://doi.org/10.1080/10643389.2011.574101.
[110]
Tartakovsky AM, Meakin P, Scheibe TD, Wood BD. A smoothed particle hydrodynamics model for reactive transport and mineral precipitation in porous and fractured porous media. Water resources research. 2007;43(5)., https://doi.org/10.1029/2005WR004770.
[111]
Sadeghi MA, Agnaou M, Barralet J, Gostick J. Dispersion modeling in pore networks: A comparison of common pore-scale models and alternative approaches. Journal of contaminant hydrology. 2020;228:103578., https://doi.org/10.1016/j.jconhyd.2019.103578.
[112]
Frank F, Liu C, Alpak FO, Berg S, Riviere B. Direct numerical simulation of flow on pore-scale images using the phase-field method. SPE Journal. 2018;23(05):1833-50., https://doi.org/10.2118/182607-PA.
[113]
Bultreys T, Van Hoorebeke L, Cnudde V. Multi-scale, micro-computed tomography-based pore network models to simulate drainage in heterogeneous rocks. Advances in Water resources. 2015;78:36-49., https://doi.org/10.1016/j.advwatres.2015.02.003.
[114]
Chen S, Doolen GD. Lattice Boltzmann method for fluid flows. Annual review of fluid mechanics. 1998;30(1):329-64., https://doi.org/10.1146/annurev.fluid.30.1.329.
[115]
Monaghan JJ. Smoothed particle hydrodynamics. Annual review of astronomy and astrophysics. 1992;30(1):543-74., https://doi.org/10.1146/annurev.aa.30.090192.002551.
[116]
Abdulle A, Budáč O. A reduced basis finite element heterogeneous multiscale method for Stokes flow in porous media. Computer Methods in Applied Mechanics and Engineering. 2016;307:1-31.
[117]
Sandström C, Larsson F, Runesson K, Johansson H. A two-scale finite element formulation of Stokes flow in porous media. Computer Methods in Applied Mechanics and Engineering. 2013;261:96-104., https://doi.org/10.1016/j.cma.2013.03.025.
[118]
Ramstad T, Idowu N, Nardi C, Øren P-E. Relative permeability calculations from two-phase flow simulations directly on digital images of porous rocks. Transport in Porous Media. 2012;94(2):487-504., https://doi.org/10.1007/s11242-011-9877-8.
[119]
Boek ES, Venturoli M. Lattice-Boltzmann studies of fluid flow in porous media with realistic rock geometries. Computers & Mathematics with Applications. 2010;59(7):2305-14., https://doi.org/10.1016/j.camwa.2009.08.063.
[120]
Prodanović M, Lindquist W, Seright R. 3D image-based characterization of fluid displacement in a Berea core. Advances in Water Resources. 2007;30(2):214-26., https://doi.org/10.1016/j.advwatres.2005.05.015.
[121]
Xu L, Yu X, Regenauer-Lieb K. An immersed boundary-lattice Boltzmann method for gaseous slip flow. Physics of Fluids. 2020;32(1):012002.,
https://doi.org/10.1063/1.5126392.
[122]
Yu X, Xu L, Regenauer-Lieb K, Jing Y, Tian F-B. Modeling the effects of gas slippage, cleat network topology and scale dependence of gas transport in coal seam gas reservoirs. Fuel. 2020;264:116715., https://doi.org/10.1016/j.fuel.2019.116715.
[123]
Liu M, Mostaghimi P. High-resolution pore-scale simulation of dissolution in porous media. Chemical Engineering Science. 2017;161:360-9., https://doi.org/10.1016/j.ces.2016.12.064.
[124]
Wang G, Jiang C, Shen J, Han D, Qin X. Deformation and water transport behaviors study of heterogenous coal using CT-based 3D simulation. International Journal of Coal Geology. 2019;211:103204., https://doi.org/10.1016/j.coal.2019.05.011.
[125]
Mahdiabad OA. Primary drainage in static pore network modelling: a comparative study: University of Leoben; 2020.
[126]
Chung T, Wang YD, Armstrong RT, Mostaghimi P. Approximating permeability of microcomputed-tomography images using elliptic flow equations. SPE Journal. 2019;24(03):1154-63., https://doi.org/10.2118/191379-PA.
[127]
Golparvar A, Zhou Y, Wu K, Ma J, Yu Z. A comprehensive review of pore scale modeling methodologies for multiphase flow in porous media. Advances in Geo-Energy Research. 2018;2(4):418-40., https://doi.org/10.26804/ager.2018.04.07.
[128]
Clemens T, Tsikouris K, Buchgraber M, Castanier L, Kovscek A. Pore-Scale Evaluation of Polymers Displacing Viscous Oil—Computational-Fluid-Dynamics Simulation of Micromodel Experiments. SPE Reservoir Evaluation & Engineering. 2013;16(02):144-54., https://doi.org/10.2118/154169-PA.
[129]
Yang X, Mehmani Y, Perkins WA, Pasquali A, Schönherr M, Kim K, et al. Intercomparison of 3D pore-scale flow and solute transport simulation methods. Advances in water resources. 2016;95:176-89., https://doi.org/10.1016/j.advwatres.2015.09.015.
[130]
Bultreys T, De Boever W, Cnudde V. Imaging and image-based fluid transport modeling at the pore scale in geological materials: A practical introduction to the current state-of-the-art. Earth-Science Reviews. 2016;155:93-128., https://doi.org/10.1016/j.earscirev.2016.02.001.
[131]
Pan C, Luo L-S, Miller CT. An evaluation of lattice Boltzmann schemes for porous medium flow simulation. Computers & fluids. 2006;35(8-9):898-909., https://doi.org/10.1016/j.compfluid.2005.03.008.
[132]
Zhu Y, Fox PJ, Morris JP. A pore‐scale numerical model for flow through porous media. International journal for numerical and analytical methods in geomechanics. 1999;23(9):881-904., https://doi.org/10.1002/(SICI)1096-9853(19990810)23:9%3C881::AID-NAG996%3E3.0.CO;2-K.
[133]
Zheng X, Junfeng S, Gang C, Nengyu Y, Mingyue C, Deli J, et al. Progress and prospects of oil and gas production engineering technology in China. Petroleum Exploration and Development. 2022;49(3):644-59., https://doi.org/10.1016/S1876-3804(22)60054-5.
[134]
Li Y, Zhang Y, Fu H, Yan Q. Detailed characterization of micronano pore structure of tight sandstone reservoir space in three dimensional space: a case study of the Gao 3 and Gao 4 members of Gaotaizi reservoir in the Qijia area of the Songliao basin. Arabian Journal of Geosciences. 2020;13:1-10., https://doi.org/10.1007/s12517-020-5096-3.
[135]
Mahmoud A, Gajbhiye R, Li J, Dvorkin J, Hussaini SR, AlMukainah HS. Digital rock physics (DRP) workflow to assess reservoir flow characteristics. Arabian Journal of Geosciences. 2023;16(4):1-15., https://doi.org/10.1007/s12517-023-11314-3.
[136]
Benson SM, Surles T. Carbon dioxide capture and storage: An overview with emphasis on capture and storage in deep geological formations. Proceedings of the IEEE. 2006;94(10):1795-805., https://doi.org/10.1109/JPROC.2006.883718.
[137]
Sanna A, Dri M, Wang XL, Hall MR, Maroto-Valer M, editors. Micro-silica for high-end application from carbon capture and storage by mineralisation. Key Engineering Materials; 2012: Trans Tech Publ., https://doi.org/10.4028/www.scientific.net/KEM.517.737.
[138]
Klemin D, Nadeev A, Ziauddin M, editors. Digital rock technology for quantitative prediction of acid stimulation efficiency in carbonates. SPE Annual Technical Conference and Exhibition; 2015: OnePetro.,
https://doi.org/10.2118/174807-MS.
[139]
Munoz H, Taheri A, Chanda E. Pre-peak and post-peak rock strain characteristics during uniaxial compression by 3D digital image correlation. Rock Mechanics and Rock Engineering. 2016;49:2541-54., https://doi.org/10.1007/s00603-016-0935-y.
[140]
Zhou Z, Cai X, Li X, Cao W, Du X. Dynamic response and energy evolution of sandstone under coupled static–dynamic compression: insights from experimental study into deep rock engineering applications. Rock Mechanics and Rock Engineering. 2020;53:1305-31., https://doi.org/10.1007/s00603-019-01980-9.
[141]
Krishnamurthy J, Manavalan P, Saivasan V. Application of digital enhancement techniques for groundwater exploration in a hard-rock terrain. International Journal of Remote Sensing. 1992;13(15):2925-42., https://doi.org/10.1080/01431169208904091.
[142]
Xiong Z, Wang G, Zhang Y, Cheng H, Chen F, Long W. Application of digital rock technology for formation damage evaluation in tight sandstone reservoir. Journal of Petroleum Exploration and Production Technology. 2022:1-10., https://doi.org/10.1007/s13202-022-01576-0.
[143]
Niu Y, Jackson SJ, Alqahtani N, Mostaghimi P, Armstrong RT. A comparative study of paired versus unpaired deep learning methods for physically enhancing digital rock image resolution. arXiv preprint arXiv:211208644. 2021., https://doi.org/10.48550/arXiv.2112.08644.
[144]
Ruspini L, Øren P, Berg S, Masalmeh S, Bultreys T, Taberner C, et al. Multiscale digital rock analysis for complex rocks. Transport in Porous Media. 2021;139(2):301-25., https://doi.org/10.1007/s11242-021-01667-2.
[145]
Ghanbarian B, Hadizadeh, J., and Haghighi, M. Digital rock technology: a new tool for reservoir rock analysis. Journal of Petroleum Science and Engineering. 2015;131:14-24.
[146]
Song R, Wang Y, Sun S, Liu J. Characterization and microfabrication of natural porous rocks: From micro-CT imaging and digital rock modelling to micro-3D-printed rock analogs. Journal of Petroleum Science and Engineering. 2021;205:108827., https://doi.org/10.1016/j.petrol.2021.108827.
[147]
Bai Y, Berezovsky V, Popov V, editors. Super Resolution for Digital Rock Core Images via FSRCNN. Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence; 2020., https://doi.org/10.1145/3409501.3409528.
[148]
Clarkson CR, Freeman, C. M., and Gerstle, W. H. Digital rock technology accelerates reservoir understanding. Oil & Gas Journal. 2017;115(6):40-3.
[149]
Wang H, Dalton L, Fan M, Guo R, McClure J, Crandall D, et al. Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM. Journal of Petroleum Science and Engineering. 2022;215:110596., https://doi.org/10.1016/j.petrol.2022.110596.
[150]
Han J, Han S, Kang DH, Kim Y, Lee J, Lee Y. Application of digital rock physics using X-ray CT for study on alteration of macropore properties by CO2 EOR in a carbonate oil reservoir. Journal of Petroleum Science and Engineering. 2020;189:107009., https://doi.org/10.1016/j.petrol.2020.107009.
[151]
Tariq F, Ali, M., and Rashid, A. Digital rock analysis for determining reservoir properties. Journal of Petroleum Exploration and Production Technology. 2019;9:377-86.
[152]
Burchette TP. Carbonate rocks and petroleum reservoirs: a geological perspective from the industry. Geological Society, London, Special Publications. 2012;370(1):17-37., https://doi.org/10.1144/SP370.14.
[153]
Geertsma J. Land subsidence above compacting oil and gas reservoirs. Journal of petroleum technology. 1973;25(06):734-44., https://doi.org/10.2118/3730-PA.
[154]
Litvinenko V. Digital economy as a factor in the technological development of the mineral sector. Natural Resources Research. 2020;29(3):1521-41.,
https://doi.org/10.1007/s11053-019-09568-4.
[155]
Babak R, Islam, A., and Khatib, Z. Digital rock technology: advantages, limitations, and future directions. Journal of Petroleum Exploration and Production Technology. 2018;8:731-42.
[156]
Foroughi S, Bijeljic B, Blunt MJ. Pore-by-pore modelling, validation and prediction of waterflooding in oil-wet rocks using dynamic synchrotron data. Transport in Porous Media. 2021;138(2):285-308., https://doi.org/10.1007/s11242-021-01609-y.
[157]
Alfarisi O, Raza A, Zhang H, Ozzane D, Sassi M, Zhang T. Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination. arXiv preprint arXiv:211104612. 2021., https://doi.org/10.48550/arXiv.2111.04612.
[158]
Jones RR, Mccaffrey KJ, Imber J, Wightman R, Smith SA, Holdsworth RE, et al. Calibration and validation of reservoir models: the importance of high resolution, quantitative outcrop analogues. Geological Society, London, Special Publications. 2008;309(1):87-98., https://doi.org/10.1144/SP309.7.
[159]
Alqahtani N, Alzubaidi F, Armstrong RT, Swietojanski P, Mostaghimi P. Machine learning for predicting properties of porous media from 2d X-ray images. Journal of Petroleum Science and Engineering. 2020;184:106514., https://doi.org/10.1016/j.petrol.2019.106514.
[160]
Li L, Sun, Y., and Zhang, Y. Calibration and validation of digital rock technology for reservoir rock analysis. Journal of Petroleum Science and Engineering. 2016;145:77-86.
[161]
Saxena N, Dietderich J, Alpak FO, Hows A, Appel M, Freeman J, et al. Estimating electrical cementation and saturation exponents using digital rock physics. Journal of petroleum science and engineering. 2021;198:108198., https://doi.org/10.1016/j.petrol.2020.108198.
[162]
Cao D, Hou Z, Liu Q, Fu F. Reconstruction of three-dimension digital rock guided by prior information with a combination of InfoGAN and style-based GAN. Journal of Petroleum Science and Engineering. 2022;208:109590., https://doi.org/10.1016/j.petrol.2021.109590.
[163]
Khatibi M, Mohammadi, S., Karimi, M., Fathi, E., & Khodaverdian, A. Assessment of Digital Rock Technology for Pore-Scale Analysis of Carbonate Reservoir Rocks. Journal of Petroleum Science and Engineering. 2018;167:177-87.
[164]
Bhattad P, Willson CS, Thompson KE. Effect of network structure on characterization and flow modeling using X-ray micro-tomography images of granular and fibrous porous media. Transport in Porous Media. 2011;90(2):363-91., https://doi.org/10.1007/s11242-011-9789-7.
[165]
Shabani Afrapoli M, Alipour S, Torsaeter O. Fundamental study of pore scale mechanisms in microbial improved oil recovery processes. Transport in Porous Media. 2011;90(3):949-64., https://doi.org/10.1007/s11242-011-9825-7.
[166]
Bondino I, McDougall SR, Hamon G. A pore-scale modelling approach to the interpretation of heavy oil pressure depletion experiments. Journal of Petroleum Science and Engineering. 2009;65(1-2):14-22., https://doi.org/10.1016/j.petrol.2008.12.010.
[167]
Hou J. Network modeling of residual oil displacement after polymer flooding. Journal of Petroleum Science and Engineering. 2007;59(3-4):321-32., https://doi.org/10.1016/j.petrol.2007.04.012.
[168]
Man H, Jing X. Pore network modelling of electrical resistivity and capillary pressure characteristics. Transport in Porous Media. 2000;41(3):263-85., https://doi.org/10.1023/A:1006612100346.
[169]
Øren P-E, Bakke S. Reconstruction of Berea sandstone and pore-scale modelling of wettability effects. Journal of petroleum science and engineering. 2003;39(3-4):177-99., https://doi.org/10.1016/S0920-4105(03)00062-7.
[170]
Gribble CM, Matthews GP, Laudone GM, Turner A, Ridgway CJ, Schoelkopf J, et al. Porometry, porosimetry, image analysis and void network modelling in the study of the pore-level properties of filters. Chemical engineering science. 2011;66(16):3701-9., https://doi.org/10.1016/j.ces.2011.05.013.
[171]
Schwarz BC, Devinny JS, Tsotsis TT. A biofilter network model—importance of the pore structure and other large-scale heterogeneities. Chemical Engineering Science. 2001;56(2):475-83.,
https://doi.org/10.1016/S0009-2509(00)00251-7.
[172]
Hinebaugh J, Bazylak A. Condensation in PEM fuel cell gas diffusion layers: a pore network modeling approach. Journal of the Electrochemical Society. 2010;157(10):B1382., https://doi.org/ 10.1149/1.3467837.
[173]
Jamshidi S, Boozarjomehry RB, Pishvaie MR. Application of GA in optimization of pore network models generated by multi-cellular growth algorithms. Advances in water resources. 2009;32(10):1543-53., https://doi.org/10.1016/j.advwatres.2009.07.007.
[174]
Ebrahimi AN, Jamshidi S, Iglauer S, Boozarjomehry RB. Genetic algorithm-based pore network extraction from micro-computed tomography images. Chemical Engineering Science. 2013;92:157-66., https://doi.org/10.1016/j.ces.2013.01.045.
[175]
Vrettos NA, Imakoma H, Okazaki M. Characterization of porous media by means of the Voronoi-Delaunay tessellation. Chemical Engineering and Processing: Process Intensification. 1989;25(1):35-45., https://doi.org/10.1016/0255-2701(89)85004-4.
[176]
Øren P-E, Bakke S. Process based reconstruction of sandstones and prediction of transport properties. Transport in porous media. 2002;46(2):311-43., https://doi.org/10.1023/A:1015031122338.
[177]
Wildenschild D, Vaz C, Rivers M, Rikard D, Christensen B. Using X-ray computed tomography in hydrology: systems, resolutions, and limitations. Journal of Hydrology. 2002;267(3-4):285-97., https://doi.org/10.1016/S0022-1694(02)00157-9.
[178]
Ketcham RA, Carlson WD. Acquisition, optimization and interpretation of X-ray computed tomographic imagery: applications to the geosciences. Computers & Geosciences. 2001;27(4):381-400., https://doi.org/10.1016/S0098-3004(00)00116-3.
[179]
Al-Raoush R, Willson C. Extraction of physically realistic pore network properties from three-dimensional synchrotron X-ray microtomography images of unconsolidated porous media systems. Journal of hydrology. 2005;300(1-4):44-64., https://doi.org/10.1016/j.jhydrol.2004.05.005.
[180]
Bakke S, Øren P-E. 3-D pore-scale modelling of sandstones and flow simulations in the pore networks. Spe Journal. 1997;2(02):136-49., https://doi.org/10.2118/35479-PA.
[181]
Arns J-Y, Sheppard A, Arns C, Knackstedt M, Yelkhovsky A, Pinczewski W, editors. Pore-level validation of representative pore networks obtained from micro-CT images. Proceedings of the international symposium of the society of core analysts; 2007.
[182]
Jones AC, Arns CH, Hutmacher DW, Milthorpe BK, Sheppard AP, Knackstedt MA. The correlation of pore morphology, interconnectivity and physical properties of 3D ceramic scaffolds with bone ingrowth. Biomaterials. 2009;30(7):1440-51., https://doi.org/10.1016/j.biomaterials.2008.10.056.
[183]
Sheppard A, Sok R, Averdunk H, editors. Improved pore network extraction methods. International Symposium of the Society of Core Analysts; 2005.
[184]
Rabbani A, Ayatollahi S, Kharrat R, Dashti N. Estimation of 3-D pore network coordination number of rocks from watershed segmentation of a single 2-D image. Advances in Water Resources. 2016;94:264-77., https://doi.org/10.1016/j.advwatres.2016.05.020.
[185]
Dong H, Fjeldstad S, Alberts L, Roth S, Bakke S, Øren P-E, editors. Pore network modelling on carbonate: a comparative study of different micro-CT network extraction methods. International symposium of the society of core analysts, Society of Core Analysts; 2008.
[186]
Al-Kharusi AS, Blunt MJ. Network extraction from sandstone and carbonate pore space images. Journal of petroleum science and engineering. 2007;56(4):219-31., https://doi.org/10.1016/j.petrol.2006.09.003.
[187]
Das A, Basu S, Kumar A, editors. Modelling of shale rock pore structure based on gas adsorption. E3S Web of Conferences; 2019: EDP Sciences.,
https://doi.org/10.1051/e3sconf/20199215006.
[188]
Raoof A, Nick HM, Hassanizadeh SM, Spiers C. PoreFlow: A complex pore-network model for simulation of reactive transport in variably saturated porous media. Computers & Geosciences. 2013;61:160-74., https://doi.org/10.1016/j.cageo.2013.08.005.
[189]
Okabe H, Blunt MJ. Prediction of permeability for porous media reconstructed using multiple-point statistics. Physical Review E. 2004;70(6):066135., https://doi.org/10.1103/PhysRevE.70.066135.
[190]
Valvatne PH, Piri M, Lopez X, Blunt MJ. Predictive pore-scale modeling of single and multiphase flow. Upscaling multiphase flow in porous media: Springer; 2005. p. 23-41., https://doi.org/10.1007/1-4020-3604-3_3.
[191]
Gharbi O, Blunt MJ. The impact of wettability and connectivity on relative permeability in carbonates: A pore network modeling analysis. Water Resources Research. 2012;48(12)., https://doi.org/10.1029/2012WR011877.
[192]
Mostaghimi P, Bijeljic B, Blunt MJ. Simulation of flow and dispersion on pore-space images. SPE Journal. 2012;17(04):1131-41., https://doi.org/10.2118/135261-PA.
[193]
Raeini AQ, Bijeljic B, Blunt MJ. Generalized network modeling of capillary-dominated two-phase flow. Physical Review E. 2018;97(2):023308., https://doi.org/ 10.1103/PhysRevE.97.023308.
[194]
Bolandtaba S, Skauge A. Network modeling of EOR processes: a combined invasion percolation and dynamic model for mobilization of trapped oil. Transport in porous media. 2011;89(3):357-82., https://doi.org/10.1007/s11242-011-9775-0.
[195]
Hammond PS, Unsal E. A dynamic pore network model for oil displacement by wettability-altering surfactant solution. Transport in porous media. 2012;92(3):789-817., https://doi.org/10.1007/s11242-011-9933-4.
[196]
Qin C-Z, Hassanizadeh SM. Pore-network modeling of solute transport and biofilm growth in porous media. Transport in Porous Media. 2015;110(3):345-67., https://doi.org/ 10.1007/s11242-015-0546-1.
[197]
Lu C, Yortsos YC, editors. A pore-network model of in-situ combustion in porous media. SPE International Thermal Operations and Heavy Oil Symposium; 2001: OnePetro., https://doi.org/10.2118/69705-MS.
[198]
Vickers NJ. Animal communication: when i’m calling you, will you answer too? Current biology. 2017;27(14):R713-R5., https://doi.org/10.1016/j.cub.2017.05.064.
[199]
Sorbie KS, Collins I, editors. A proposed pore-scale mechanism for how low salinity waterflooding works. SPE improved oil recovery symposium; 2010: OnePetro., https://doi.org/10.2118/129833-MS.
[200]
Watson MG, Bondino I, Hamon G, McDougall SR. A pore-scale investigation of low-salinity waterflooding in porous media: Uniformly wetted systems. Transport in Porous Media. 2017;118(2):201-23., https://doi.org/ 10.1007/s11242-017-0854-8.
[201]
Boujelben A, McDougall S, Watson M, Bondino I, Agenet N. Pore network modelling of low salinity water injection under unsteady-state flow conditions. Journal of Petroleum Science and Engineering. 2018;165:462-76., https://doi.org/10.1016/j.petrol.2018.02.040.
[202]
Prodanović M, Mehmani A, Sheppard AP. Imaged-based multiscale network modelling of microporosity in carbonates. Geological Society, London, Special Publications. 2015;406(1):95-113., https://doi.org/10.1144/SP406.9.
[203]
Lindquist WB, Lee SM, Coker DA, Jones KW, Spanne P. Medial axis analysis of void structure in three‐dimensional tomographic images of porous media. Journal of Geophysical Research: Solid Earth. 1996;101(B4):8297-310., https://doi.org/10.1029/95JB03039.
[204]
Sarker M, Siddiqui S, editors. Advances in micro-CT based evaluation of reservoir rocks. SPE Saudi Arabia Section Technical Symposium; 2009: OnePetro., https://doi.org/10.2118/126039-MS.
[205]
Silin D, Patzek T. Pore space morphology analysis using maximal inscribed spheres. Physica A: Statistical mechanics and its applications. 2006;371(2):336-60.,
https://doi.org/10.1016/j.physa.2006.04.048.
[206]
Blunt MJ. Multiphase flow in permeable media: A pore-scale perspective: Cambridge university press; 2017.
[207]
Mahabadi N, Dai S, Seol Y, Sup Yun T, Jang J. The water retention curve and relative permeability for gas production from hydrate‐bearing sediments: pore‐network model simulation. Geochemistry, Geophysics, Geosystems. 2016;17(8):3099-110., https://doi.org/10.1002/2016GC006372.
[208]
Zhao J, Qin F, Derome D, Carmeliet J. Simulation of quasi-static drainage displacement in porous media on pore-scale: Coupling lattice Boltzmann method and pore network model. Journal of Hydrology. 2020;588:125080., https://doi.org/10.1016/j.jhydrol.2020.125080.
[209]
Zhang J, Wang X, Zhang C, yan Feng H, Yu B, Yang W, et al. Self-lubricating interpenetrating polymer networks with functionalized nanoparticles enhancement for quasi-static and dynamic antifouling. Chemical Engineering Journal. 2022;429:132300., https://doi.org/10.1016/j.cej.2021.132300.
[210]
Regaieg M, Moncorgé A. Adaptive dynamic/quasi-static pore network model for efficient multiphase flow simulation. Computational Geosciences. 2017;21(4):795-806.
[211]
PETERSEN RT, BALHOFF MT, BRYANT S. Coupling multiphase pore-scale models to account for boundary conditions: application to 2D quasi-static pore networks. Journal of Multiscale Modelling. 2011;3(03):109-31., https://doi.org/10.1142/S1756973711000431.
[212]
Johnson A, Roy I, Matthews G, Patel D. An improved simulation of void structure, water retention and hydraulic conductivity in soil with the Pore‐Cor three‐dimensional network. European Journal of Soil Science. 2003;54(3):477-90., https://doi.org/10.1046/j.1365-2389.2003.00504.x.
[213]
Secanell M, Putz A, Wardlaw P, Zingan V, Bhaiya M, Moore M, et al. Openfcst: An open-source mathematical modelling software for polymer electrolyte fuel cells. ECS Transactions. 2014;64(3):655., https://doi.org/ 10.1149/06403.0655ecst.
[214]
Gostick J, Aghighi M, Hinebaugh J, Tranter T, Hoeh MA, Day H, et al. OpenPNM: a pore network modeling package. Computing in Science & Engineering. 2016;18(4):60-74., https://doi.org/10.1109/MCSE.2016.49.
[215]
Putz A, Hinebaugh J, Aghighi M, Day H, Bazylak A, Gostick JT. Introducing OpenPNM: an open source pore network modeling software package. ECS Transactions. 2013;58(1):79., https://doi.org/ 10.1149/05801.0079ecst.
[216]
Tranter T, Gostick J, Burns A, Gale W. Pore network modeling of compressed fuel cell components with OpenPNM. Fuel Cells. 2016;16(4):504-15., https://doi.org/10.1002/fuce.201500168.
[217]
Yang Y, Wang K, Zhang L, Sun H, Zhang K, Ma J. Pore-scale simulation of shale oil flow based on pore network model. Fuel. 2019;251:683-92., https://doi.org/10.1016/j.fuel.2019.03.083.
[218]
Esteves BF, Lage PL, Couto P, Kovscek AR. Pore-network modeling of single-phase reactive transport and dissolution pattern evaluation. Advances in Water Resources. 2020;145:103741., https://doi.org/10.1016/j.advwatres.2020.103741.
[219]
Fatt I. The network model of porous media. Transactions of the AIME. 1956;207(01):144-81., https://doi.org/10.2118/574-G.
[220]
Mahanta B, Vishal V, Ranjith P, Singh T. An insight into pore-network models of high-temperature heat-treated sandstones using computed tomography. Journal of Natural Gas Science and Engineering. 2020;77:103227., https://doi.org/10.1016/j.jngse.2020.103227.
[221]
Foroozesh J, Abdalla AIM, Zivar D, Douraghinejad J. Stress-dependent fluid dynamics of shale gas reservoirs: A pore network modeling approach. Journal of Natural Gas Science and Engineering. 2021;95:104243.,
https://doi.org/10.1016/j.jngse.2021.104243.
[222]
Mehmani A, Verma R, Prodanović M. Pore-scale modeling of carbonates. Marine and Petroleum Geology. 2020;114:104141., https://doi.org/10.1016/j.marpetgeo.2019.104141.
[223]
Dim P, Laudone G, Gibble C, Matthews G, Rigby S. Simulation of Refinery Catalyst Pore Structure from Mercury Porosimetry data using Porexpert. Nigerian Journal of Engineering and Applied Science, 2017, 4 (1): 50-57. 2017.
[224]
Dim P, Rigby S. Pore network Modelling of CAPRI Catalyst using Mercury Porosimetry and Porexpert, Proceedings of 6th International Conference on Chemical, Biological and Environmental Engineering 15-16, September, Paris France. 2014.
[225]
Li Y, Chi Y, Han S, Zhao C, Miao Y. Pore-throat structure characterization of carbon fiber reinforced resin matrix composites: Employing Micro-CT and Avizo technique. Plos one. 2021;16(9):e0257640., https://doi.org/10.1371/journal.pone.0257640.
[226]
Li Z, Liu D, Cai Y, Ranjith P, Yao Y. Multi-scale quantitative characterization of 3-D pore-fracture networks in bituminous and anthracite coals using FIB-SEM tomography and X-ray μ-CT. Fuel. 2017;209:43-53., https://doi.org/10.1016/j.fuel.2017.07.088.
[227]
Cole ME, Stout SD, Dominguez VM, Agnew AM. Pore Extractor 2D: An ImageJ toolkit for quantifying cortical pore morphometry on histological bone images, with application to intraskeletal and regional patterning. American Journal of Biological Anthropology. 2022;179(3):365-85., https://doi.org/10.1002/ajpa.24618.
[228]
Hu Z, Zhang R, Zhu K, Li D, Jin Y, Guo W, et al. Probing the Pore Structure of the Berea Sandstone by Using X-ray Micro-CT in Combination with ImageJ Software. Minerals. 2023;13(3):360., https://doi.org/10.3390/min13030360.
[229]
Blusseau S, Wielhorski Y, Haddad Z, Velasco-Forero S. Instance segmentation of 3D woven fabric from tomography images by Deep Learning and morphological pseudo-labeling. Composites Part B: Engineering. 2022;247:110333., https://doi.org/10.1016/j.compositesb.2022.110333.
[230]
Wu S, Wang Q, Zeng Q, Zhang Y, Shao Y, Deng F, et al. Automatic extraction of outcrop cavity based on a multiscale regional convolution neural network. Computers & Geosciences. 2022;160:105038., https://doi.org/10.1016/j.cageo.2022.105038.
[231]
Tang K, Meyer Q, White R, Armstrong RT, Mostaghimi P, Da Wang Y, et al. Deep learning for full-feature X-ray microcomputed tomography segmentation of proton electron membrane fuel cells. Computers & Chemical Engineering. 2022;161:107768., https://doi.org/10.1016/j.compchemeng.2022.107768.
[232]
Wu Y, Misra S, Sondergeld C, Curtis M, Jernigen J. Machine learning for locating organic matter and pores in scanning electron microscopy images of organic-rich shales. Fuel. 2019;253:662-76., https://doi.org/10.1016/j.fuel.2019.05.017.
[233]
Sadeghnejad S, Gostick J. Multiscale reconstruction of vuggy carbonates by pore-network modeling and image-based technique. SPE Journal. 2020;25(01):253-67., https://doi.org/10.2118/198902-PA.
[234]
Unsal E, Dane J, Dozier GV. A genetic algorithm for predicting pore geometry based on air permeability measurements. Vadose Zone Journal. 2005;4(2):389-97., https://doi.org/10.2136/vzj2004.0116.
[235]
Fischer U, Celia MA. Prediction of relative and absolute permeabilities for gas and water from soil water retention curves using a pore‐scale network model. Water Resources Research. 1999;35(4):1089-100., https://doi.org/10.1029/1998WR900048.
[236]
Dillard LA, Blunt MJ. Development of a pore network simulation model to study nonaqueous phase liquid dissolution. Water Resources Research. 2000;36(2):439-54., https://doi.org/10.1029/1999WR900301.
[237]
Gostick JT. Versatile and efficient pore network extraction method using marker-based watershed segmentation. Physical Review E. 2017;96(2):023307.,
https://doi.org/10.1103/PhysRevE.96.023307.
[238]
Sheppard AP, Sok RM, Averdunk H. Techniques for image enhancement and segmentation of tomographic images of porous materials. Physica A: Statistical mechanics and its applications. 2004;339(1-2):145-51., https://doi.org/10.1016/j.physa.2004.03.057.
[239]
Thompson KE, Willson CS, Zhang W. Quantitative computer reconstruction of particulate materials from microtomography images. Powder Technology. 2006;163(3):169-82., https://doi.org/10.1016/j.powtec.2005.12.016.
[240]
Rabbani A, Jamshidi S, Salehi S. An automated simple algorithm for realistic pore network extraction from micro-tomography images. Journal of Petroleum Science and Engineering. 2014;123:164-71., https://doi.org/10.1016/j.petrol.2014.08.020.
[241]
Bryant SL, King PR, Mellor DW. Network model evaluation of permeability and spatial correlation in a real random sphere packing. Transport in porous media. 1993;11(1):53-70.
[242]
Bryant S, Blunt M. Prediction of relative permeability in simple porous media. Physical review A. 1992;46(4):2004., https://doi.org/10.1103/PhysRevA.46.2004.
[243]
Øren P-E, Bakke S, Arntzen OJ. Extending predictive capabilities to network models. SPE journal. 1998;3(04):324-36., https://doi.org/10.2118/52052-PA.
[244]
Manchanda R, Olson JE, Sharma MM, editors. Permeability anisotropy and dilation due to shear failure in poorly consolidated sands. SPE Hydraulic Fracturing Technology Conference; 2012: OnePetro., https://doi.org/10.2118/152432-MS.
[245]
Raziperchikolaee S, Alvarado V, Yin S. Microscale modeling of fluid flow‐geomechanics‐seismicity: Relationship between permeability and seismic source response in deformed rock joints. Journal of geophysical research: solid earth. 2014;119(9):6958-75., https://doi.org/10.1002/2013JB010758.
[246]
Yang Z, Juanes R. Two sides of a fault: Grain-scale analysis of pore pressure control on fault slip. Physical Review E. 2018;97(2):022906., https://doi.org/10.1103/PhysRevE.97.022906.
[247]
Sun Q, Zhang N, Fadlelmula M, Wang Y. Structural regeneration of fracture-vug network in naturally fractured vuggy reservoirs. Journal of Petroleum Science and Engineering. 2018;165:28-41., https://doi.org/10.1016/j.petrol.2017.11.030.
[248]
Beg MS. Multiscale Pore Network Modeling of Hierarchical Media with Applications to Improved Oil and Gas Recovery: University of Waterloo; 2022.
[249]
Bultreys T, Singh K, Raeini AQ, Ruspini LC, Øren PE, Berg S, et al. Verifying pore network models of imbibition in rocks using time‐resolved synchrotron imaging. Water Resources Research. 2020;56(6):e2019WR026587., https://doi.org/10.1029/2019WR026587.
[250]
Berryman JG, Blair SC. Kozeny–Carman relations and image processing methods for estimating Darcy’s constant. Journal of Applied Physics. 1987;62(6):2221-8., https://doi.org/10.1063/1.339497.
[251]
Bondino I, Hamon G, Kallel W, Kac D. Relative Permeabilities from simulation in 3D rock models and equivalent pore networks: critical review and way forward1. Petrophysics-The SPWLA Journal of Formation Evaluation and Reservoir Description. 2013;54(06):538-46.
[252]
Singh K, Menke H, Andrew M, Lin Q, Rau C, Blunt MJ, et al. Dynamics of snap-off and pore-filling events during two-phase fluid flow in permeable media. Scientific reports. 2017;7(1):1-13., https://doi.org/ 10.1038/s41598-017-05204-4.
[253]
Schlüter S, Li T, Vogel H, Berg S, Wildenschild D. Time scales of relaxation dynamics during hydraulic non-equilibrium in two-phase flow. Water Resour Res. 2017;53:4709-24.
[254]
Armstrong RT, McClure JE, Berrill MA, Rücker M, Schlüter S, Berg S. Beyond Darcy's law: The role of phase topology and ganglion dynamics for two-fluid flow. Physical Review E. 2016;94(4):043113.
[255]
Reynolds CA, Menke H, Andrew M, Blunt MJ, Krevor S. Dynamic fluid connectivity during steady-state multiphase flow in a sandstone. Proceedings of the National Academy of Sciences. 2017;114(31):8187-92.,
https://doi.org/10.1073/pnas.1702834114.
[256]
Liu Y. Y., slotine. J–J & Barabási, A–L Controllability of complex networks Nature. 2011;473:167-76.
[257]
Arns CH, Arns, J.Y., and Mokhtari, M. Characterisation of pore structures using X-ray microtomography and image analysis. Advances in Water Resources. 2016;95:77-106., https://doi.org/10.1002/cjce.5450830122.
[258]
Arns CH, Knackstedt, M.A., and Pinczewski, W.V. Digital rock physics: A review. Proceedings of the Royal Society A. 2013;469.
[259]
Blunt MJ, Bijeljic, B., Dong, H., Gharbi, O., Iglauer, S., Mostaghimi, P., Paluszny, A., and Pentland, C. Pore-scale imaging and modelling. Advances in Water Resources. 2013;51., https://doi.org/10.1016/j.advwatres.2012.03.003.
[260]
Abedi M, Afshar, M.H., and Haghighat, E. Application of machine learning in digital rock physics: A review. Journal of Natural Gas Science and Engineering. 2020;84:103417.
[261]
Koteleva N, Frenkel I. Digital processing of seismic data from open-pit mining blasts. Applied Sciences. 2021;11(1):383., https://doi.org/10.3390/app11010383.
[262]
Singh J, Cilli P, Hosa A, Main I. Digital rock physics in four dimensions: simulating cementation and its effect on seismic velocity. Geophysical Journal International. 2020;222(3):1606-19., https://doi.org/10.1093/gji/ggaa271.
[263]
Zhang Y, Zhang, X., Qin, Y., Chen, X., and Wang, Y. Digital rock physics for reservoir prediction: A review. Journal of Natural Gas Science and Engineering. 2020;83.
[264]
Anderson J, Wealleans J, Ray J. Endodontic applications of 3D printing. International endodontic journal. 2018;51(9):1005-18.
[265]
Gong H, Zhao, W., Wu, Z., Wu, J., Zhang, W., and Wang, L. A review of digital rock physics: Opportunities, challenges, and future directions. Geoscience Frontiers. 2020;11(4):1289-304.
[266]
Lee H, Kwon, T., Lee, J., and Lee, K. Application of digital rock physics to medical imaging: A review. Medical Physics. 2019;46(8):3492-507.
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