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Development of a New Void Fraction Correlation for Modeling Two-Phase Flow in Producing Geothermal Wells Using Artificial Neural Networks

[+] Author Affiliations
A. Álvarez del Castillo, E. Santoyo, O. García-Valladares

Universidad Nacional Autónoma de México, Temixco, MOR, México

Paper No. IMECE2010-40444, pp. 1229-1235; 7 pages
  • ASME 2010 International Mechanical Engineering Congress and Exposition
  • Volume 5: Energy Systems Analysis, Thermodynamics and Sustainability; NanoEngineering for Energy; Engineering to Address Climate Change, Parts A and B
  • Vancouver, British Columbia, Canada, November 12–18, 2010
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4429-8
  • Copyright © 2010 by ASME


An artificial neural network (ANN) was used to develop a new empirical correlation to estimate void fractions for modeling two-phase flows in geothermal wells. Flowing pressure, wellbore diameter, steam quality, fluid density and viscosity, and Reynolds numbers were used as input data. An explicit relationship among the input data was obtained from an ANN model. A computational architecture based on, the Levenberg-Marquardt optimization algorithm, the hyperbolic tangent sigmoid transfer-function, and the linear transfer-function, was designed. A geothermal database containing thirty-two data sets logged in production well tests were used both to train and to validate the ANN. The best training results were obtained for an ANN architecture of five neurons in the hidden layer, which made possible to predict void fractions with a satisfactory efficiency (R2 = 0.992). From this ANN training pattern, a new empirical correlation was developed and coupled into a wellbore simulator for modeling two-phase flows in other geothermal wells (to avoid bias). Four well-known engineering correlations for calculating the void fraction were simultaneous evaluated. The simulated results (obtained with the five void fraction correlations) were statistically compared with measured field data. A better agreement between simulated and field data was systematically obtained for the new ANN correlation with matching errors less than 3%. These results suggest that the new empirical correlation can be reliable used to estimate void fractions in two-phase geothermal wellbores.

Copyright © 2010 by ASME



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