Modeling of CO2-brine interfacial tension: Application to enhanced oil recovery |
| |
Authors: | Mohammad Madani Peyman Abbasi Ghasem Zargar Pezhman Abbasi |
| |
Affiliation: | 1. Department of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran;2. Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran |
| |
Abstract: | 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. |
| |
Keywords: | artificial neural network brine carbon dioxide correlation interfacial tension |
|
|