Creation of Saturation Maps from Two-Phase Flow Experiments in Microfluidic Devices

  • Yuhang Wang
  • Saman A. AryanaEmail author
Part of the Advances in Science, Technology & Innovation book series (ASTI)


Microfluidic devices provide an experimental platform for direct observations of flow in complex channel networks. In this work, two-phase displacement experiments are conducted using a microfluidic device, featuring a complex network that is representative of a sample of Berea sandstone. The porous medium is placed in the field of view of a high-resolution camera with a monochromatic sensor—data captured in the form of images cover the entire medium while maintaining the resolution needed to discern features as small as 10 μm. This paper presents the series of steps required to convert these images into saturation maps that may be used for comparisons with predictions of numerical simulation models. The main steps include: exclusion of the grains; perspective transformation to correct minor misalignments of the device in each experiment; calculation of the Representative Elementary Volume; local thresholding strategy to account for non-uniform illumination across the medium; and finally, calculation of saturation maps.


Porous media Multiphase flow Microfluidic device Image segmentation Nonparametric density estimation 



The corresponding author gratefully acknowledges the Donors of the American Chemical Society Petroleum Research Fund (55795-DNI9) for the support of this research.


  1. 1.
    Guo, F., Aryana, S.A.: An experimental investigation of nanoparticle-stabilized CO2 foam used in enhanced oil recovery. Fuel 186, 430–442 (2016)CrossRefGoogle Scholar
  2. 2.
    Buchgraber, M., Al-Dossary, M., Ross, C.M., Kovscek, A.R.: Creation of a dual-porosity micromodel for pore-level visualization of multiphase flow. J. Petrol. Sci. Eng. 86, 27–38 (2012)CrossRefGoogle Scholar
  3. 3.
    Guo, F., He, J., Johnson, P.A., Aryana, S.A.: Stabilization of CO2 foam using by-product fly ash and recyclable iron oxide nanoparticles to improve carbon utilization in EOR processes. Sustain. Energy Fuels 1(4), 814–822 (2017)CrossRefGoogle Scholar
  4. 4.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer Science & Business Media (2010)Google Scholar
  5. 5.
    Bear, J.: Dynamics of Fluids in Porous Media. Courier Corporation (2013)Google Scholar
  6. 6.
    Karadimitriou, N.K., Musterd, M., Kleingeld, P.J., Kreutzer, M.T., Hassanizadeh, S.M., Joekar-Niasar, V.: On the fabrication of PDMS micromodels by rapid prototyping, and their use in two-phase flow studies. Water Resour. Res. 49(4), 2056–2067 (2013)CrossRefGoogle Scholar
  7. 7.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recogn. 26(9), 1277–1294 (1993)CrossRefGoogle Scholar
  8. 8.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis, vol. 26. CRC Press (1986)Google Scholar
  9. 9.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  10. 10.
    Nagabhushana, S.: Computer Vision and Image Processing. New Age International (2005)Google Scholar
  11. 11.
    Sehairi, K., Chouireb, F., Meunier, J.: Comparison study between different automatic threshold algorithms for motion detection. In: 4th International Conference on IEEE Transactions, pp. 1–8. IEEE, Algeria (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of WyomingLaramieUSA

Personalised recommendations