Journal of Thermal Science

, Volume 27, Issue 5, pp 479–486 | Cite as

Comprehensive Approach for Porous Materials Analysis Using a Dedicated Preprocessing Tool for Mass and Heat Transfer Modeling

  • Paweł Madejski
  • Paulina KrakowskaEmail author
  • Magdalena Habrat
  • Edyta Puskarczyk
  • Mariusz Jędrychowski


The paper presents a comprehensive, newly developed software–poROSE (poROus materials examination SoftwarE) for the qualitative and quantitative assessment of porous materials and analysis methodologies developed by the authors as a solution for emerging challenges. A low porosity rock sample was analyzed and thanks to the developed and implemented methodologies in poROSE software, the main geometrical properties were calculated. A tool was also used in preprocessing part of the computational analysis to prepare a geometrical representation of the porous material. The basic functions as elimination of blind pores in the geometrical model were completed and the geometrical model was exported for CFD software. As a result, it was possible to carry out calculations of the basic properties of the analyzed porous material sample. The developed tool allows to carry out quantitative and qualitative analysis to determine the most important properties characterized porous materials. In presented tool the input data can be images from X-ray computed tomography (CT), scanning electron microscope (SEM) or focused ion beam with scanning electron microscope (FIB-SEM) in grey level. A geometric model developed in the proper format can be used as an input to modeling mass, momentum and heat transfer, as well as, in strength or thermo-strength analysis of any porous materials. In this example, thermal analysis was carried out on the skeleton of rock sample. Moreover, thermal conductivity was estimated using empirical equations.


porous materials rock software thermal properties preprocessing 


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Project is financed by the National Centre for Research and Development in Poland, program LIDER VI, project no. LIDER/319/L–6/14/NCBR/2015: Innovative method of unconventional oil and gas reservoirs interpretation using computed X-ray tomography.


  1. [1]
    Krakowska P, Puskarczyk E, Jędrychowski M, Habrat M, Madejski P and Dohnalik M. Innovative characterization of tight sandstones from Paleozoic basins in Poland using X-ray computed tomography supported by nuclear magnetic resonance and mercury porosimetry. Journal Petroleum Science and Engineering, 2018, 166: 389–405.CrossRefGoogle Scholar
  2. [2]
    Janc K, Tarasiuk J, Bonnet ASand Lipinski P. Semiautomated algorithm for cortical and trabecular bone separation from CT scans. Computer Methods in Biomechanics & Biomechanical Engineering, 2011, 14/1: 217–218.CrossRefGoogle Scholar
  3. [3]
    Su B-L, Sanchez C and Yang X-Y. Hierarchically Structured Porous Materials: From Nanoscience to Catalysis, Separation, Optics, Energy, and Life Science. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, 2011.Google Scholar
  4. [4]
    Cui A, Wust R, Nassichuk B, Glover K, Brezovski R and Twemlow C. A Nearly Complete Characterization of Permeability to Hydrocarbon Gas and Liquid for Unconventional Reservoirs: A Challenge to Conventional Thinking. Unconventional Resources Technology Conference, 12–14 August, Denver, Colorado, USA, 2013, 1–17.Google Scholar
  5. [5]
    Peng S and Loucks B. Permeability measurements in mudrocks using gas-expansion methods on plug and crushed-rock samples. Marine Petroleum Geology, 2016, 73: 299–310.CrossRefGoogle Scholar
  6. [6]
    Suarez-Rivera R, Chertov M, Willberg D, Green S and Keller J. Understanding Permeability Measurements in Tight Shales Promotes Enhanced Determination of Reservoir Quality, SPE Canadian Unconventional Resources Conference, 30 October-1 November, Calgary, Alberta, Canada, 2012, 1–13.Google Scholar
  7. [7]
    Civan F. Porous media transport phenomena. John Wiley & Sons Inc., New Jersey, 2011.CrossRefGoogle Scholar
  8. [8]
    Krakowska P, Madejski P and Jarzyna J. Fluid flow modeling in tight Carboniferous sandstone. EAGE EartDoc database, 75th EAGE Conference & Exhibition incorporating SPE EUROPEC 2013, 10–13 June, London, United Kingdom, 2013, DOI: 10.3997/2214-4609.20130692Google Scholar
  9. [9]
    Krakowska P, Madejski P and Jarzyna J. Permeability estimation using CFD modeling in tight Carboniferous sandstone. EAGE EartDoc database, 76th EAGE Conference & Exhibition incorporating SPE EUROPEC 2014, 16–19 June, Amsterdam, Netherlands, 2014, DOI: 10.3997/2214-4609.20141607Google Scholar
  10. [10]
    Madejski P, Krakowska P, Puskarczyk E, Habrat M, Jędrychowski M. Gas flow modeling for permeability determination in porous rock sample using Maxwell slip model. Conference Materials of XIInternational Conference on Computational Heat, Mass and Momentum Transfer, Kraków 21–24 May, 2018, 1–8.Google Scholar
  11. [11]
    Habrat M, Krakowska P, Puskarczyk E, Jędrychowski M and Madejski P. The concept of a computer system for interpretation of tight rocks using X-ray computed tomography results, Studia Geotechnica et Mechanica, 2017, 39(1): 101–107.ADSCrossRefGoogle Scholar
  12. [12]
    Di Sipio E, Chiesa S, Destro E, Galgaro A, Giaretta A, Gola G, Manzella A. Rock Thermal Conductivity as Key Parameter for Geothermal Numerical Models. Energy Procedia, 2013, 40: 87–94CrossRefGoogle Scholar
  13. [13]
    Vélez MI, Blessent D, Córdoba S, López-Sánchez J, Raymond J, Parra-Palacio E. Geothermal potential assessment of the Nevado del Ruiz volcano based on rock thermal conductivity measurements and numerical modeling of heat transfer. Journal of South American Earth Sciences, 2018, 81: 153–164ADSCrossRefGoogle Scholar
  14. [14]
    Jaoude IB, Novakowski K, Kueper B. Identifying and assessing key parameters controlling heat transport in discrete rock fractures. Geothermics, 2018, 75: 93–104CrossRefGoogle Scholar
  15. [15]
    Guo Ch, Nian X, Liu Y, Qi Ch, Song J, Yu W. Analysis of 2D Flow and Heat Transfer Modeling in Fracture of Porous Media. Journal of Thermal Science, 2017, 26(4): 331–338.ADSCrossRefGoogle Scholar
  16. [16]
    Mielke P, Bar K, Sass I. Determining the relationship of thermal conductivity and compressional wave velocity of common rock types as a basis for reservoir characterization. Journal of Applied Geophysics, 2017, 140: 135–144.ADSCrossRefGoogle Scholar
  17. [17]
    Rerak M, Ocłoń P. Thermal analysis of underground power cable system. Journal of Thermal Science, 2017, 26: 465–471.ADSCrossRefGoogle Scholar
  18. [18]
    Ocłoń P, Bittelli M, Cisek P, Kroener E, Pilarczyk M, Taler D, Rao RV, Vallati A. The performance analysis of a new thermal backfill material for underground power cable system. Applied Thermal Engineering, 2016, 108: 233–250.CrossRefGoogle Scholar
  19. [19]
    Wu Y, Shi Y, Cai N, Ni M. Thermal Modeling and Management of Solid Oxide Fuel Cells Operating with Internally Reformed Methane. Journal of Thermal Science, 2018, 27(3): 203–212.ADSCrossRefGoogle Scholar
  20. [20]
    Liu J., Liu S, Sun S., Zhou W, I.H.I. S, Wang M,. Yan Y. Tomographic data fusion with CFD simulations associated with a planar sensor. Journal of Thermal Science, 2017, 26(2): 175–182.ADSCrossRefGoogle Scholar
  21. [21]
    Carmak V, Rybach L, Thermal properties. In: Geophysics - Physical Properties of Rocks, Chapter: Landolt-Bornstein Numerical Data and Functional Relationships in Science and Technology, New Series, Group V: Geophysics and Space Research, Springer-Verlag Berlin–Heidelberg, New York, M. Beblo (Eds.), 1982Google Scholar
  22. [22]
    Roy RF, Beck AE, Touloukian YS. Thermophysical properties of rock. In: Physical properties of rock and minerals, McGraw-Hill publisher/CINDAS data Series on material properties, vol. II-2, Columbus, USA, YSTouloukian (Eds.), 1981Google Scholar
  23. [23]
    Schon JH. Physical properties of rocks: fundamentals and principles of petrophysics. Elsevier B.V., Amsterdam, The Netherlands, 2004Google Scholar
  24. [24]
    Poelchau HS, Baker DR, Hantschel Th, Horsfield B, Wygrala B. Basin simulation and the design of the conceptual basin model. In: Petroleum and basin evaluation, Welte DH, Horsfield B, Baker DR (Eds), Springer, Berlin, 1997.Google Scholar

Copyright information

© Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Paweł Madejski
    • 1
  • Paulina Krakowska
    • 2
    Email author
  • Magdalena Habrat
    • 3
  • Edyta Puskarczyk
    • 2
  • Mariusz Jędrychowski
    • 4
  1. 1.Department of Power Systems and Environmental Protection Facilities, Faculty of Mechanical Engineering and RoboticsAGH University of Science and TechnologyKrakówPoland
  2. 2.Department of Geophysics, Faculty of Geology, Geophysics and Environmental ProtectionAGH University of Science and TechnologyKrakówPoland
  3. 3.Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental ProtectionAGH University of Science and TechnologyKrakówPoland
  4. 4.Department of Condensed Matter Physics, Faculty of Physics and Applied Computer ScienceAGH University of Science and TechnologyKrakówPoland

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