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All issues Volume 146 (2020) E3S Web Conf., 146 (2020) 04002 References
Open Access
Issue
E3S Web Conf.
Volume 146, 2020
The 2019 International Symposium of the Society of Core Analysts (SCA 2019)
Article Number 04002
Number of page(s) 9
Section Pore Scale Imaging and Modeling
DOI https://doi.org/10.1051/e3sconf/202014604002
Published online 05 February 2020
  1. W. Xu, T. Tran, R. Srivastava, A.G. Journel, et al., Srivastava, R.M. “Reservoir characterization with probability field simulation,” in Proc. SPE Annu. Tech. Conf. Exhib, Society of Petroleum Engineers, 1992. [Google Scholar]
  2. M. Pirrone, A. Battigelli, L. Ruvo, et al., “Lithofacies Classification of Thin-Layered Turbidite Reservoirs Through the Integration of Core Data and Dielectric-Dispersion Log Measurements,” SPE Reservoir Eval. Eng., vol. 19, no. 2, pp. 226-238, Apr. 2016, Art. no. SPE-170748-PA. [CrossRef] [Google Scholar]
  3. C.H. Arns, M.A. Knackstedt, M.V. Pinczewski, and W. Lindquist, “Accurate estimation of transport properties from microtomographic images,” Geophysical Res. Lett., vol. 28, no. 17, pp. 3361-3364, Sep. 2001. [CrossRef] [Google Scholar]
  4. C.H. Arns, M.A. Knackstedt, W.V. Pinczewski, and E.J. Garboczi, “Computation of linear elastic properties from microtomographic images: Methodology and agreement between theory and experiment,” Geophys., vol. 67, no. 5, pp. 1396-1405, Sep. 2002. [CrossRef] [Google Scholar]
  5. C.H. Arns, M.A. Knackstedt, W.V. Pinczewski, and N.S. Martys, “Virtual permeametry on microtomographic images,” J. Petroleum Sci. Eng., vol. 45 no. 1-2, pp. 41-46, Nov. 2004. [CrossRef] [Google Scholar]
  6. J. Fredrich, A. DiGiovanni, and D. Noble, “Predicting macroscopic transport properties using microscopic image data.” J. Geophysical Res.: Solid Earth, vol. 111, no. B3, Mar. 2006. [CrossRef] [Google Scholar]
  7. C.H. Arns, M.A. Knackstedt, and N.S. Martys, “Cross-property correlations and permeability estimation in sandstone,” Phys. Rev. E, vol. 72, no. 4, pp. 046304, Oct. 2005. [Google Scholar]
  8. A. Dehghan Khalili, J.-Y. Arns, F. Hussain, Y. Cinar, W.V. Pinczewski, and C.H. Arns, “Permeability upscaling for carbonates from the pore-scale using multi-scale Xray-CT images,” SPE Reservoir Eval. Eng., vol. 16, no. 4, pp. 353, Mar. 2012. [Google Scholar]
  9. A. Fadili, and R. Ababou, “Dual homogenization of immiscible steady two‐phase flows in random porous media,” Water Resour. Res., vol. 40, no. 1, pp. 1-18, Jan. 2004. [CrossRef] [Google Scholar]
  10. S. Norouzi Apourvari and C.H. Arns, “Image-based relative permeability upscaling from the pore scale,” Adv. Water Resour., vol. 95, pp. 161-175, Sep. 2016. [Google Scholar]
  11. G.E. Archie, “Classification of carbonate reservoir rocks and petrophysical considerations,” AAPG Bull., vol. 36, no. 2, pp. 278-298, Feb. 1952. [Google Scholar]
  12. M. Skalinski, J. Kenter, S. Jenkins, et al., “Rock type definition and pore type classification of a carbonate platform, Tengiz Field, Republic of Kazakhstan,” in Proc. SPWLA 50th Annu. Logging Symp., Society of Petrophysicists and Well-Log Analysts, 2009, SPWLA-2009-62185. [Google Scholar]
  13. B. Li, X. Tan, F. Wang, P. Lian, W. Gao, and Y. Li, “Fracture and vug characterization and carbonate rock type automatic classification using X-ray CT images,” J. Petroleum Sci. Eng., vol. 153, pp. 88-96, May. 2017. [CrossRef] [Google Scholar]
  14. M. Dernaika, Y.N. Uddin, S. Koronfol, O. Al Jallad, G. Sinclair, Y. Hanamura, K. Horaguchi, et al., “Multi-scale rock analysis for improved characterization of complex carbonates,” in Proc. SPE Reservoir Characterisation Simul. Conf. Exhib., Society of Petroleum Engineers, 2015, SPE-175598. [Google Scholar]
  15. M. Schmitt, M. Halisch, C. M ̈uller, and C.P. Fernandes, “Classification and quantification of pore shapes in sandstone reservoir rocks with 3-D X-ray micro-computed tomography,” Solid Earth, vol. 7, no. 1, pp. 285-300, Feb. 2016. [CrossRef] [Google Scholar]
  16. K.R. Mecke, T. Buchert, and H. Wagner, “Robust morphological measures for large-scale structure in the universe,” arXiv preprint astro-ph/9312028, Dec. 1993. [Google Scholar]
  17. C.H. Arns, M.A. Knackstedt, W.V. Pinczewski, and K.R. Mecke, “Euler-Poincaré characteristics of classes of disordered media,” Phys. Rev. E, vol. 63, no. 3, pp. 031112, Feb. 2001. [Google Scholar]
  18. C. Arns, M. Knackstedt, and K. Mecke, “3D structural analysis: sensitivity of Minkowski functionals,” J. Microsc., vol. 240, no. 3, pp. 181-196, Dec. 2010. [CrossRef] [Google Scholar]
  19. R.T. Armstrong, J.E. McClure, V. Robins, et al., “Porous Media Characterization Using Minkowski Functionals: Theories, Applications and Future Directions,” Transport in Porous Media, pp. 1-31, Nov. 2018. [Google Scholar]
  20. J. Schmalzing and K.M.G. ́orski, “Minkowski functionals used in the morphological analysis of cosmic microwave background anisotropy maps,” Monthly Notices Roy. Astronomical Soc., vol. 297, no. 2, pp. 355-365, Jun. 1998. [NASA ADS] [CrossRef] [Google Scholar]
  21. C.H. Arns, MA Knackstedt, KR Mecke, “Characterisation of irregular spatial structures by parallel sets and integral geometric measures,” Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 241, no. 1-3, pp. 351-372, Jul. 2004. [CrossRef] [Google Scholar]
  22. S. Winitzki and A. Kosowsky, “Minkowski functional description of microwave background Gaussianity,” New Astron., vol. 3, no. 2, pp. 75-99, Mar. 1998. [NASA ADS] [CrossRef] [Google Scholar]
  23. C.H. Arns, M.A. Knackstedt, and K.R. Mecke, “Reconstructing complex materials via effective grain shapes,” Phys. Rev. Lett. Vol. 91, no. 21, pp. 215506 Nov. 2003, doi: 10.1103/PhysRevLett.91.215506. [Google Scholar]
  24. C.H. Arns, M.A. Knackstedt, and K.R. Mecke, “Boolean reconstructions of complex materials: Integral geometric approach,” Phys. Rev. E, vol. 80, no. 5, pp. 051303, Nov. 2009, doi: 10.1103/PhysRevE.80.051303. [Google Scholar]
  25. N.I. Ismail, “Rock-typing using the complete set of additive morphological descriptors, “ Ph.D. dissertation, School of Minerals and Energy Resour. Eng., Univ. New South Wales, Sydney, NSW, Australia, 2014. [Google Scholar]
  26. C.H. Arns, J. Mecke, K.R. Mecke, and D. Stoyan, “Second-order analysis by variograms for curvature measures of two-phase structures,” Europ. Phys. J. B: Cond. Matt., vol. 47, no. 3, pp. 397-409, Oct. 2005. [CrossRef] [Google Scholar]
  27. J.A. Rushing, K.E. Newsham, and T.A. Blasingame, “Rock typing: Keys to understanding productivity in tight gas sands,” in Proc. 2008 SPE Unconventional Reservoirs Conf., Keystone, Colorado, Jan. 2008. [Google Scholar]
  28. M. Turner, L. Knuefing, C. Arns, A. Sakellariou, T. Senden, A. Sheppard, R. Sok, A. Limaye, W.V. Pinczewski,and M. Knackstedt, “Three-dimensional imaging of multiphase flow in porous media,” Physica A: Statistical mechanics and its applications, vol. 339, no. 1-2, pp. 166-172, Aug, 2004. [CrossRef] [Google Scholar]
  29. M. Hilpert and C.T. Miller, “Pore-morphology-based simulation of drainage in totally wetting porous media,” Adv. Water Resour., vol. 24, no. 3-4, pp. 243-255, Feb. 2001. [CrossRef] [Google Scholar]
  30. I. Shikhov and C.H. Arns, “Evaluation of capillary pressure methods via digital rock simulations,” Transport in Porous Media, vol. 107, no. 2, pp. 623-640, Mar. 2015. [Google Scholar]
  31. R. Pini, S.C. Krevor, & S.M. Benson, “Capillary pressure and heterogeneity for the CO2/water system in sandstone rocks at reservoir conditions,” Adv. Water Resour., vol. 38, pp. 48-59, Mar. 2012. [CrossRef] [Google Scholar]
  32. A. Georgiadis, S. Berg, A. Makurat, G. Maitland, and H. Ott, “Pore-scale micro-computed-tomography imaging: Nonwetting-phase cluster-size distribution during drainage and imbibition,” Phys. Rev. E, vol. 88, no. 3, pp. 033002, Sep. 2013, doi: 10.1103/PhysRevE.88.033002 [Google Scholar]
  33. S.T. Hyde, I.S. Barnes, B. Ninham, “Curvature energy of surfactant interfaces confined to the plaquettes of a cubic lattice,” Langmuir, vol. 6, no. 6, pp. 1055-1062, Jun. 1990. [Google Scholar]

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