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Machine Learning Approach to Predict the Flow Rate for an Immiscible Two-Phase Flow at Pore Scale for Enhanced Oil Recovery Application

[+] Author Affiliations
Arturo Rodriguez, V. M. Krushnarao Kotteda, Luis F. Rodriguez, Vinod Kumar, Arturo Schiaffino, Zackery R. Nieto

University of Texas at El Paso, El Paso, TX

Paper No. FEDSM2018-83050, pp. V002T09A003; 11 pages
doi:10.1115/FEDSM2018-83050
From:
  • ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting
  • Volume 2: Development and Applications in Computational Fluid Dynamics; Industrial and Environmental Applications of Fluid Mechanics; Fluid Measurement and Instrumentation; Cavitation and Phase Change
  • Montreal, Quebec, Canada, July 15–20, 2018
  • Conference Sponsors: Fluids Engineering Division
  • ISBN: 978-0-7918-5156-2
  • Copyright © 2018 by ASME

abstract

Due to global demand for energy, there is a need to maximize oil extraction from wet reservoir sedimentary formations, which implies the efficient extraction of oil at the pore scale. The approach involves pressurizing water into the wetting oil pore of the rock for displacing and extracting the oil. The two-phase flow is complicated because of the behavior of the fluid flow at the pore scale, and capillary quantities such as surface tension, viscosities, pressure drop, radius of the medium, and contact angle become important. In the present work, we use machine learning algorithms in TensorFlow to predict the volumetric flow rate for a given pressure drop, surface tension, viscosity and geometry of the pores. The TensorFlow software library was developed by the Google Brain team and is one of the most powerful tools for developing machine learning workflows. Machine learning models can be trained on data and then these models are used to make predictions. In this paper, the predicted values for a two-phase flow of various pore sizes and liquids are validated against the numerical and experimental results in the literature.

Copyright © 2018 by ASME

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