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Modeling of CO2-brine interfacial tension: Application to enhanced oil recovery

Madani, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1080/10916466.2017.1391844
  3. Abstract:
  4. Development of reliable and accurate models to estimate carbon dioxide–brine interfacial tension (IFT) is necessary, since its experimental measurement is time-consuming and requires expensive experimental apparatus as well as complicated interpretation procedure. In the current study, feed forward artificial neural network is used for estimation of CO2–brine IFT based on data from published literature which consists of a number of carbon dioxide–brine interfacial tension data covering broad ranges of temperature, total salinity, mole fractions of impure components and pressure. Trial-and-error method is utilized to optimize the artificial neural network topology in order to enhance its capability of generalization. The results showed that there is good agreement between experimental values and modeling results. Comparison of the empirical correlations with the proposed model suggests that the current model can predict the CO2–brine IFT more accurately and robustly. © 2017 Taylor & Francis Group, LLC
  5. Keywords:
  6. Artificial neural network ; Brine ; Correlation ; Interfacial tension ; Brines ; Correlation methods ; Enhanced recovery ; Neural networks ; Oil well flooding ; Surface tension ; Artificial neural network topology ; Current modeling ; Empirical correlations ; Enhanced oil recovery ; Experimental apparatus ; Experimental values ; Feed-forward artificial neural networks ; Trial-and-error method ; Carbon dioxide
  7. Source: Petroleum Science and Technology ; Volume 35, Issue 23 , 2017 , Pages 2179-2186 ; 10916466 (ISSN)
  8. URL: http://www.tandfonline.com/doi/abs/10.1080/10916466.2017.1391844