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A novel digital extraction approach of pore network models from carbonates inspired by quantum genetic optimization techniques

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Abstract

Estimating hydraulic properties of carbonate is challenging due to its wider range of porosity and complex pore structures. To investigate the hydraulic properties of carbonate rocks, a novel approach inspired by quantum genetic optimization in this work is proposed to extract the optimal pore network model (PNM), which is applied to simulate the hydraulic properties. The pore network variables, such as porosity, pore and throat sizes, are considered as the optimal parameters, and the capillary pressure curve, breakthrough drainage pressure and permeability are calculated based on the constructed PNM. The computing time (CPU time) and memory usage (RAM usage) for the PNM extraction using the proposed approach and classical methods are compared. Results indicate that the proposed approach shows better computing efficiency than classical methods. Excellent agreements are found between the experimental and simulation results from the proposed and classical methods. The proposed approach provides promising tools to investigate the hydraulic properties of geomaterials.

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References

  1. Al-Kharusi A, Blunt MJ (2007) Network extraction from sandstone and carbonate pore space images. J Pet Sci Eng 56:219–231

    Article  Google Scholar 

  2. Al-Raoush RI, Willson CS (2005) Extraction of physically realistic pore network properties from three-dimensional synchrotron X-ray microtomography images of unconsolidated porous media systems. J Hydrol 300(1):44–64

    Article  Google Scholar 

  3. An S, Yao J, Yang Y, Zhang L, Zhao J, Gao Y (2016) Influence of pore structure parameters on flow characteristics based on a digital rock and the pore network model. J Nat Gas Sci Eng 31:156–163

    Article  Google Scholar 

  4. Bai H, Ge Y, Mariethoz G (2016) Utilizing spatial association analysis to determine the number of multiple grids for multiple-point statistics. Spat Stat 17(17):83–104

    Article  MathSciNet  Google Scholar 

  5. Biswal B, Manwart C, Hilfer R, Bakke S, Øren PE (1999) Quantitative analysis of experimental and synthetic microstructures for sedimentary rock. Physica A 273(3–4):452–475

    Article  Google Scholar 

  6. Chen F, Xiong H, Wang X, Yin ZY (2023) Transmission effect of eroded particles in suffusion using the CFD-DEM coupling method. Acta Geotech 18:335–354

    Article  Google Scholar 

  7. Chen XL, Liu J, Yu C, Wang S, Wu W (2023) Analytical solutions to thermal gradient enhanced diffusion of organic contaminants through unsaturated composite liner: considering the existence of capillary fringe. Acta Geotech 18(9):1–20

    Article  Google Scholar 

  8. Collins RE (1961) Flow of fluids through porous materials. Reinhold Pub, New York

    Google Scholar 

  9. Dudek G (2004) Unit commitment by genetic algorithm with specialized search operators. Electr Power Syst Res 72(3):299–308

    Article  Google Scholar 

  10. Dullien FA (1992) Porous media: fluid transport and pore structure, 2nd edn. Harcourt Brace, Academic, San Diego

    Google Scholar 

  11. Durr C, Hoyer P (1996) A quantum algorithm for finding the minimum. arXiv preprint arXiv:quant-ph/9607014

  12. Dong H, Blunt MJ (2009) Pore-network extraction from micro computerized tomography images. Phys Rev E 80:036307

    Article  Google Scholar 

  13. Han KH, Kim JH (2002) Genetic quantum algorithm and its application to combinatorial optimization problem. Evolut Comput 2000 Proc Congr IEEE 2:1354–1360

    Article  Google Scholar 

  14. Hinebaugh J, Bazylak A (2010) Condensation in PEM fuel cell gas diffusion layers: a pore network modeling approach. J Electrochem Soc 157:1382–1390

    Article  Google Scholar 

  15. Huang T, Li X, Zhang T, Lu DT (2013) Gpu-accelerated direct sampling method for multiple-point statistical simulation. Comput Geosci 57(57):13–23

    Article  Google Scholar 

  16. Ketcham RA, Carlson WD (2001) Acquisition, optimization and interpretation of X-ray computed tomographic imagery: applications to the geosciences. Comput Geosci 27(4):381–400

    Article  Google Scholar 

  17. Khishvand M, Akbarabadi M, Piri M (2016) Micro-scale experimental investigation of the effect of flow rate on trapping in sandstone and carbonate rock samples. Adv Water Resour 94:379–399

    Article  Google Scholar 

  18. Krzaczek M, Nitka M, Tejchman J (2023) A novel DEM-based pore-scale thermal-hydro-mechanical model for fractured non-saturated porous materials. Acta Geotech 18:2487–2512

    Article  Google Scholar 

  19. Lindquist WB, Sang-Moon L, Coker DA, Jones KW, Spanne P (1996) Medial axis analysis of void structure in three-dimensional tomographic images of porous media. J Geophys Res 101:8297–8310

    Article  Google Scholar 

  20. Li P, Li S (2008) Quantum-inspired evolutionary algorithm for continuous space optimization based on bloch coordinates of qubits. Neurocomputing 72(1):581–591

    Article  Google Scholar 

  21. Liu Y, Jeng DS, Xie H, Li CB (2023) On the particle morphology characterization of granular geomaterials. Acta Geotech 18:2321–2347. https://doi.org/10.1007/s11440-022-01733-z

    Article  Google Scholar 

  22. McCabe WL (2005) Unit operations of chemical engineering, 7th edn. McGraw-Hill, New York

    Google Scholar 

  23. Malossini A, Blanzieri E, Calarco T (2008) Quantum genetic optimization. IEEE Trans Evol Comput 12(2):231–241

    Article  Google Scholar 

  24. Moaddel A, Müter D, Gooya R, Sørensen HO, Stipp SLS (2018) A fuzzy logic based algorithm for defining and extracting pore network structure from tomography images of rocks. Adv Water Resour 119:197–209

    Article  Google Scholar 

  25. Nielsen MA, Chuang IL (2000) Quantum computation and quantum information. Cambridge University Press, Cambridge

    Google Scholar 

  26. Øren PE, Bakke S (2002) Process based reconstruction of sandstones and prediction of transport properties. Transp Porous Media 46(2–3):311–343

    Article  Google Scholar 

  27. Patzek TW, Silin DB (2001) Shape factor and hydraulic conductance in noncircular capillaries: I. One-phase creeping flow. J Colloid Interface Sci 236:295–304

    Article  Google Scholar 

  28. Rabbani A, Jamshidi S (2014) Specific surface and porosity relationship for sandstones for prediction of permeability. Int J Rock Mech Min Sci 71:25–32

    Article  Google Scholar 

  29. Rabbani A, Assadi A, Kharrat R, Dashti N, Ayatollahi S (2017) Estimation of carbonates permeability using pore network parameters extracted from thin section images and comparison with experimental data. J Nat Gas Sci Eng 42:85–98

    Article  Google Scholar 

  30. Rezanezhad F, Quinton WL, Price JS, Elrick D, Elliot TR, Heck RJ (2009) Examining the effect of pore size distribution and shape on flow through unsaturated peat using computed tomography. Hydrol Earth Syst Sci 13:1993–2002

    Article  Google Scholar 

  31. Raoof A, Hassanizadeh SM (2012) A new formulation for pore-network modeling of two-phase flow. Water Resour Res 48:1–13

    Article  Google Scholar 

  32. Sahimi M (2012) Flow and transport in porous media and fractured rock: from classical methods to modern approaches. Wiley

    Google Scholar 

  33. Scheperboer IC, Suiker ASJ, Bosco E, Clemens FHLR (2022) A coupled hydro-mechanical model for subsurface erosion with analyses of soil piping and void formation. Acta Geotech 17:4769–4798. https://doi.org/10.1007/s11440-022-01479-8

    Article  Google Scholar 

  34. Silin D, Patzek T (2006) Pore space morphology analysis using maximal inscribed spheres. Phys A 371(2):336–360

    Article  Google Scholar 

  35. Su D, Yan WM (2020) Prediction of 3D size and shape descriptors of irregular granular particles from projected 2D images. Acta Geotech 15:1533–1555. https://doi.org/10.1007/s11440-019-00845-3

    Article  Google Scholar 

  36. Tengattini A, Andò E, Einav I, Viggiani G (2023) Micromechanically inspired investigation of cemented granular materials: part I–from X-ray micro tomography to measurable model variables. Acta Geotech 18:35–55. https://doi.org/10.1007/s11440-022-01486-9

    Article  Google Scholar 

  37. Thovert JF, Salles J, Adler PM (2011) Computerized characterization of the geometry of real porous media: their discretization, analysis and interpretation. J Microsc 170(1):65–79

    Article  Google Scholar 

  38. Vasilyev L, Raoof A, Nordbotten JM (2012) Effect of mean network coordination number on dispersivity characteristics. Transp Porous Media 95(2):447–463

    Article  MathSciNet  Google Scholar 

  39. Varloteaux C, Békri S, Adler PM (2013) Pore network modelling to determine the transport properties in presence of a reactive fluid: from pore to reservoir scale. Adv Water Resour 53(2):87–100

    Article  Google Scholar 

  40. Wang J, Zhao J, Zhang Y, Wang D, Li Y, Song Y (2016) Analysis of the effect of particle size on permeability in hydrate-bearing porous media using pore network models combined with ct. Fuel 163:34–40

    Article  Google Scholar 

  41. Wang JP, Luan JY, Gao XG, Liu TH, Ando E, Francois B (2022) A micro-investigation of unsaturated sand in mini-triaxial compression based on micro-CT image analysis. Acta Geotech 17:4799–4821. https://doi.org/10.1007/s11440-022-01658-7

    Article  Google Scholar 

  42. Wang X, Zhang H, Yin ZY, Su D, Liu ZQ (2023) Deep-learning-enhanced model reconstruction of realistic 3D rock particles by intelligent video tracking of 2D random particle projections. Acta Geotech 18:1407–1430. https://doi.org/10.1007/s11440-022-01616-3

    Article  Google Scholar 

  43. Wu Y, Lin C, Ren L, Yan W, An S, Chen B, Wang Y, Zhang X, You C, Zhang Y (2018) Reconstruction of 3D porous media using multiple-point statistics based on a 3D training image. J Nat Gas Sci Eng 51:129–140

    Article  Google Scholar 

  44. Wildenschild D, Vaz CMP, Rivers ML, Rikard D, Christensen BSB (2002) Using x-ray computed tomography in hydrology: systems, resolutions, and limitations. J Hydrol 267(3):285–297

    Article  Google Scholar 

  45. Xiao N, Zhou X, Ling T (2022) Novel cooling–solidification annealing reconstruction of rock models. Acta Geotech 17:1785–1802. https://doi.org/10.1007/s11440-021-01307-5

    Article  Google Scholar 

  46. Xu L, Liu X, Liang L (2014) A pore network model reconstruction method via genetic algorithm. J Nat Gas Sci Eng 21:907–914

    Article  Google Scholar 

  47. Xu WJ, Zhang HY (2022) Meso and macroscale mechanical behaviors of soil–rock mixtures. Acta Geotech 17:3765–3782. https://doi.org/10.1007/s11440-022-01449-0

    Article  Google Scholar 

  48. Yang L, Ai L, Xue K, Ling Z, Li Y (2018) Analyzing the effects of inhomogeneity on the permeability of porous media containing methane hydrates through pore network models combined with CT observation. Energy 163:27–37

    Article  Google Scholar 

  49. Zhao Y, Sun Y, Liu S, Chen Z, Yuan L (2018) Pore structure characterization of coal by synchrotron radiation nano-CT. Fuel 215:102–110

    Article  Google Scholar 

  50. Zhao Z, Zhou XP, Qian QH (2020) Fracture characterization and permeability prediction by pore scale variables extracted from X-ray CT images of porous geomaterials. Sci China Technol Sci 63(5):755–767

    Article  Google Scholar 

  51. Zhang T, Du Y, Huang T, Li X (2015) Gpu-accelerated 3d reconstruction of porous media using multiple-point statistics. Comput Geosci 19(1):1–20

    Article  MathSciNet  Google Scholar 

  52. Zhang X, Nowamooz H (2023) Mechanical degradation of unstabilized rammed earth URE wall under salts and rising damp attack effect. Acta Geotech 18:1–18

    Google Scholar 

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Acknowledgements

The authors would like to thank for the support of the National Natural Science Foundation of China (Nos. 42207193, 52027814, 51839009), the Natural Science Foundation of Hubei Province (2022CFB609), the Open Foundation of the Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters (2022-MGDP-02) and the National Center for International Research on Deep Earth Drilling and5 Resource Development (DEDRD-2022-07).

Funding

National Natural Science Foundation of China, 42207193, Zhi Zhao, 52027814, Xiaoping Zhou, 51839009, Xiaoping Zhou, Natural Science Foundation of Hubei Province, 2022CFB609, Zhi Zhao, Open Foundation of the Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters, 2022-MGDP-02, Zhi Zhao, National Center for International Research on Deep Earth Drilling and Resource Development, DEDRD-2022-07, Zhi Zhao

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Authors and Affiliations

  1. School of Civil Engineering, Wuhan University, Wuhan, 430072, China

    Zhi Zhao, Yun-Dong Shou & Xiao-Ping Zhou

  2. The Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters, Anhui University of Science & Technology, Huainan, 232001, China

    Zhi Zhao

  3. National Center for International Research On Deep Earth Drilling and Resource Development, China University of Geosciences (Wuhan), Wuhan, 430074, China

    Zhi Zhao

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  1. Zhi Zhao
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Zhi Zhao was contributed writing–original drafts and codes; Yun-Dong Shou was involved in experiments and algorithms and Xiao-Ping Zhou was performed algorithms, review and editing.

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Correspondence to Xiao-Ping Zhou.

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Zhao, Z., Shou, YD. & Zhou, XP. A novel digital extraction approach of pore network models from carbonates inspired by quantum genetic optimization techniques. Acta Geotech. (2024). https://doi.org/10.1007/s11440-024-02310-2

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