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A Novel Technique to Determine Concentration-Dependent Solvent Dispersion in Vapex

Author

Listed:
  • Hadil Abukhalifeh

    (Department of Chemical Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada)

  • Ali Lohi

    (Department of Chemical Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada)

  • Simant Ranjan Upreti

    (Department of Chemical Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada)

Abstract

Vapex (vapor extraction of heavy oil and bitumen) is a promising recovery technology because it consumes low energy, and is very environmentally-friendly. The dispersion of solvents into heavy oil and bitumen is a crucial transport property governing Vapex. The accurate determination of solvent dispersion in Vapex is essential to effectively predict the amount and time scale of oil recovery as well to optimize the field operations. In this work, a novel technique is developed to experimentally determine the concentration-dependent dispersion coefficient of a solvent in Vapex process. The principles of variational calculus are utilized in conjunction with a mass transfer model of the experimental Vapex process. A computational algorithm is developed to optimally compute solvent dispersion as a function of its concentration in heavy oil. The developed technique is applied to Vapex utilizing propane as a solvent. The results show that dispersion of propane is a unimodal function of its concentration in bitumen.

Suggested Citation

  • Hadil Abukhalifeh & Ali Lohi & Simant Ranjan Upreti, 2009. "A Novel Technique to Determine Concentration-Dependent Solvent Dispersion in Vapex," Energies, MDPI, vol. 2(4), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:2:y:2009:i:4:p:851-872:d:5918
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    Cited by:

    1. Olalekan Alade & Dhafer Al Shehri & Mohamed Mahmoud & Kyuro Sasaki, 2019. "Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)," Energies, MDPI, vol. 12(12), pages 1-13, June.

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