Transport in Porous Media

, Volume 116, Issue 2, pp 687–703 | Cite as

Foam Generation Hysteresis in Porous Media: Experiments and New Insights

  • Mohammad LotfollahiEmail author
  • Ijung Kim
  • Mohammad R. Beygi
  • Andrew J. Worthen
  • Chun Huh
  • Keith P. Johnston
  • Mary F. Wheeler
  • David A. DiCarlo


Foam application in subsurface processes including environmental remediation, geological carbon-sequestration, and gas-injection enhanced oil recovery (EOR) has the potential to enhance contamination remediation, secure \(\hbox {CO}_{2}\) storage, and improve oil recovery, respectively. Nanoparticles are a promising alternative to surfactants in creating foam in harsh environments. We conducted \(\hbox {CO}_{2}\)-in-brine foam generation experiments in Boise sandstones with surface-treated silica nanoparticle in high-salinity conditions. All the experiments were conducted at the fixed \(\hbox {CO}_{2}\) volume fraction and fixed flow rate which changed in steps. The steady-state foam apparent viscosity was measured as a function of injection velocity. The foam flowing through the cores showed higher apparent viscosity as the flow rate increased from low to medium and high velocities. At very high velocities, once foam bubbles were finely textured, the foam apparent viscosity was governed by foam rheology rather than foam creation. A noticeable hysteresis occurred when the flow velocity was initially increased and then decreased, implying multiple (coarse and strong) foam states at the same superficial velocity. A normalized generation function was combined with CMG-STARS foam model to cover full spectrum of foam behavior in the experiments. The new model successfully captures foam generation and hysteresis trends in presented experiments in this study and data from the literature. The results indicate once foam is generated in porous media, it is possible to maintain strong foam at low injection rates. This makes foam more feasible in field applications where foam generation is limited by high injection rates that may only exist near the injection well.


Foam hysteresis Foam generation Foam modeling EOR Nanoparticle 

List of Symbols


Shear-thinning exponent in STARS foam model


Factor governing abruptness of dry-out calculation \((F _{\mathrm{dry-out}})\) in STARS foam model


Foam generation exponent in STARS foam model


Foam dry-out (coalescence) function in STARS foam model


Gas fractional flow (foam quality)


Water fractional flow


Foam generation function in STARS foam model


Normalized foam generation function introduced in improved STARS foam model


Normalized foam generation value for coarse foam in improved STARS foam model

\(\hbox {FM}\)

Foam resistance factor in STARS foam model


Reference rheology capillary number in STARS foam model


Reference water saturation in dry-out calculation \((F_{\mathrm{dry-out}})\) in STARS foam model


Critical foam generation capillary number in STARS foam model


Maximum resistance factor in STARS foam model


Foam shear-thinning function in STARS foam model


Water fractional flow


Permeability \([\hbox {L}^{2}]\)


Water relative permeability


Water endpoint relative permeability


Gas relative permeability


Gas endpoint relative permeability


Gas relative permeability in the presence of foam


Capillary number


Capillary number at which foam generation reaches its maximum limit


Gas relative permeability exponent


Water relative permeability exponent


Flow rate \([\hbox {L}^{3}\hbox {t}^{-1}]\)


Residual gas saturation


Normalized water saturation


Water saturation


Limiting water saturation


Residual water saturation


Total Darcy velocity \([\hbox {Lt}^{-1}]\)


Water Darcy velocity \([\hbox {Lt}^{-1}]\)


Total interstitial velocity \([\hbox {Lt}^{-1}]\)


Water interstitial velocity \([\hbox {Lt}^{-1}]\)

\(\Delta P\)

Pressure drop \([\hbox {ML}^{-1}\hbox {t}^{-2}]\)

\(\nabla P\)

Pressure gradient \([\hbox {ML}^{-2}\hbox {t}^{-2}]\)

\(\nabla \varPhi \)

Phase potential gradient \([\hbox {ML}^{-2}\hbox {t}^{-2}]\)

\(\mu _{\mathrm{g}}\)

Gas viscosity \([\hbox {ML}^{-1}\hbox {t}^{-1}]\)

\(\mu _{\mathrm{w}}\)

Water viscosity \([\hbox {ML}^{-1}t^{-1}]\)

\(\mu _{\mathrm{app}}^\mathrm{{f}}\)

Foam apparent viscosity \([\hbox {ML}^{-1}\hbox {t}^{-1}]\)

\(\sigma _{\mathrm{wg}}\)

Water–gas interfacial tension \([\hbox {Mt}^{-2}]\)

\(\phi \)




This work was supported by the Nanoparticles for Subsurface Engineering Industrial Affiliates Program at The University of Texas at Austin. We acknowledge the financial support from Denbury Resources Inc., and the donation of silica nanoparticles from Nissan Chemical America Corp. We would like to thank Dr. William R. Rossen and Dr. Rouhi Farajzadeh for helpful discussions.


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Mohammad Lotfollahi
    • 1
    Email author
  • Ijung Kim
    • 1
  • Mohammad R. Beygi
    • 1
  • Andrew J. Worthen
    • 2
  • Chun Huh
    • 1
  • Keith P. Johnston
    • 2
  • Mary F. Wheeler
    • 1
  • David A. DiCarlo
    • 1
  1. 1.Petroleum and Geosystems Engineering Department, The University of Texas at AustinAustinUSA
  2. 2.McKetta Department of Chemical Engineering, The University of Texas at AustinAustinUSA

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