New filtration parameters from X-ray computed tomography for tight rock images


  • Paulina Krakowska-Madejska AGH University of Science and Technology, Faculty of Geology, Geophysics and Environmental Protection, Department of Geophysics, Krakow, Poland



new parameters for filtration properties, computed X-ray tomography, pore space, tight rocks, porosity, connectivity, pore channels


New parameters are proposed to evaluate the filtration properties of rocks obtained on the basis of 3D interpretation of images from X-ray computed tomography. The analyzed parameters are: global average pore connectivity, average blind pore connectivity, blind pore coefficient per object and blind pore coefficient per branch. The 3D pore space from computed X-ray tomography must be subjected to a process of pore space transformation into a skeleton. Then, the presented parameters can be evaluated, taking into consideration the pore channels (branches), pore channel connection points (junctions) and blind pores (pore without connection to the other pore). The calculations were made for low porosity sandstones, mudstones, limestones, and dolomites which differ in terms of age and depth of present deposition. The global average pore connectivity reflects the degree of development of the pore space in which the formation fluid can flow. The higher the global average pore connectivity, the most complex the pore structure can be expected. The higher the parameter of the average blind pore connectivity, the worse are the filtration properties of the rock. The higher the concentration of blind pore coefficient per object or branch, the worse the filtration properties of the rock. Moreover, new parameters were compared with the Euler characteristic and coordination number, revealing a high consistency.


Download data is not yet available.


Adeleye J.O. & Akanji L.T., 2022. A quantitative analysis of flow properties and heterogeneity in shale rocks using computed tomography imaging and finite-element based simulation. Journal of Natural Gas Science and Engineering, 106, 104742.

Al Balushi F. & Taleghani A.D., 2022. Digital rock analysis to estimate stress-sensitive rock permeabilities. Computers and Geotechnics, 151, 104960.

Arns C.H., Bauget F., Ghous A., Sakellarion A., Senden T.J., Sheppard A.P., Sok R.M. et al., 2005. Digital Core Laboratory: Petrophysical analysis from 3D imaging of reservoir core fragments. Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description, 46(4), 260–277.

Backeberg N.R., Iacoviello F., Rittner M., Mitchell T.M., Jones A.P., Day R., Wheeler J. et al., 2017. Quantifying the anisotropy and tortuosity of permeable pathways in clay-rich mudstones using models based on X-ray tomography. Scientific Reports, 7, 14838.

Burliga S. & Dohnalik M., 2011. Internal structure and origin of modern salt crust of Salar de Uyuni (Blivia) salt pan based on tomographic research. Geology, Geophysics & Environment, 37(2), 215–229.

Cnudde V. & Boone M.V., 2013. High‐resolution X‐ray computed tomography in geosciences: a review of the current technol-ogy and applications. Earth-Science Reviews, 123, 1–17.

Dohnalik M. & Jarzyna J., 2015. Determination of reservoir properties through the use of computed X-ray microtomography – eolian sandstone examples. Geology, Geophysics & Environment, 41(3), 223–248.

Golab A., Ward C.R., Permana A., Lennox P. & Botha P., 2013. High-resolution three-dimensional imaging of coal using mi-crofocus X-ray computed tomography, with special reference to modes of mineral occurrence. International Journal of Coal Geology, 113, 97–108.

Handwerger D., Suarez-Rivera R., Vaughn K. & Keller J., 2011. Improved petrophysical core measurements on tight shale reservoirs using retort and crushed samples. Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, October 2011, SPE-147456-MS.

Hormann K., Baranau V., Hlushkou D., Höltzel A. & Tallarek U., 2016. Topological analysis of non-granular, disordered po-rous media: determination of pore connectivity, pore coordination, and geometric tortuosity in physically reconstructed sili-ca monoliths. New Journal of Chemistry, 40, 4187–4199.

Karpyn Z.T., Alajmi A., Radaelli F., Halleck P.M. & Grader A.S., 2009. X-ray CT and hydraulic evidence for a relationship between fracture conductivity and adjacent matrix porosity. Engineering Geology, 103(3–4), 139–145.

Krakowska P., 2019. Detailed parametrization of the pore space in tight clastic rocks from Poland based on laboratory meas-urement results. Acta Geophysica, 67(6), 1765–1776.

Krakowska P. & Madejski P., 2019. Research on fluid flow and permeability in low porous rock sample using laboratory and computational techniques. Energies, 12(24), 4684.

Krakowska P., Puskarczyk E., Jędrychowski M., Habrat M., Madejski P. & Dohnalik M., 2018. Innovative characterization of tight sandstones from Paleozoic basins in Poland using X-ray computed tomography supported by nuclear magnetic reso-nance and mercury porosimetry. Journal Petroleum Science and Engineering, 166, 389–405.

Liu S., Sang S., Wang G., Ma J., Wang X., Wang W., Du Y. & Wang T., 2017. FIB-SEM and X-ray CT characterization of in-terconnected pores in high-rank coal formed from regional metamorphism. Journal of Petroleum Science and Engineering, 148, 21–31.

Lu X., Armstrong R.T. & Mostaghimi P., 2018. High-pressure X-ray imaging to interpret coal permeability. Fuel, 226, 573–582.

Osher J. & Schladitz K., 2009. 3D Images of Material Structures: Processing and Analysis. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Rabbani A., Ayatollahi S., Kharrat R. & Dashti N., 2016. Estimation of 3-D pore network coordination number of rocks from watershed segmentation of a single 2-D image. Advances in Water Resources, 94, 264–277.

Silin D. & Patzek T., 2006. Pore space morphology analysis using maximal inscribed spheres. Physica A: Statistical Mechanics and its Applications, 371(2), 336–360.

Sossa-Azuela J.H., Santiago-Montero R., Perez-Cisneros M. & Rubio-Espino E., 2013. Computing the Euler number of a binary image based on a vertex codification. Journal of Applied Research and Technology, 11(3), 360–370.

Soulaine C., Gjetvaj F., Garing C., Roman S., Russian A., Gouze P. & Tchelepi H.A., 2016. The impact of subresolution poros-ity of X-ray microtomography images on the permeability. Transport of Porous Media, 113, 227–243.

Suarez-Rivera R., Chertov M., Willberg D., Green S. & Keller J., 2012. Understanding permeability measurements in tight shales promotes enhanced determination of reservoir quality. Paper presented at the SPE Canadian Unconventional Re-sources Conference, Calgary, Alberta, Canada, October 2012, SPE-162816-MS.

Toriwaki J. & Yonekura T., 2002. Euler number and connectivity indexes of a three dimensional digital picture. Forma, 17, 183–209.

Tsakiroglu Ch.D. & Payatakes A.C., 2000. Characterization of the pore structure of reservoir rocks with the aid of serial sec-tioning analysis, mercury porosimetry and network simulation. Advances in Water Resources, 23(7), 773–789.

Ulusay R., Aydan O., Gerçek H., Ali Hindistan M. & Tuncay E., 2016. Rock Mechanics and Rock Engineering: From the Past to the Future. CRC Press, Taylor & Francis Group, London.

Vásárhelyi L., Kónya Z., Kukovecz A. & Vajtai R., 2020. Microcomputed tomography-based characterization of advanced materials: a review. Materials Today Advances, 8, 100084.

Vogel H.-J., 2002. Topological Characterization of Porous Media. [in:] Mecke K. & Stoyan D. (eds.), Morphology of Condensed Matter: Physics and Geometry of Spatially Complex Systems, Lecture Notes in Physics, 600, Springer, Berlin, Heidelberg, 75–92.

Wang J., Zhao J., Zhang Y., Wang D., Li Y. & Song Y., 2016. Analysis of the effect of particle size on permeability in hy-drate-bearing porous media using pore network models combined with CT. Fuel, 163, 34–40.

Wayne M.A., 2008. Geology of Carbonate Reservoirs: The Identification, Description, and Characterization of Hydrocarbon Reservoirs in Carbonate Rocks. Willey & Sons Inc., Hoboken.

Yu X., Butler S.K., Kong L., Mibeck B., Barajas-Olalde C., Burton-Kelly M.E. & Azzolina N.A., 2022. Machine learn-ing-assisted upscaling analysis of reservoir rock core properties based on micro-computed tomography imagery. Journal of Petroleum Science and Engineering, 219, 11108.




How to Cite

Krakowska-Madejska, P. (2022). New filtration parameters from X-ray computed tomography for tight rock images. Geology, Geophysics and Environment, 48(4), 381–392.