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On the Evaluation of Interfacial Tension (IFT) of CO 2 –Paraffin System for Enhanced Oil Recovery Process: Comparison of Empirical Correlations, Soft Computing Approaches, and Parachor Model

Author

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  • Farzaneh Rezaei

    (Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Amin Rezaei

    (Department of Petroleum Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz 71557-13876, Iran)

  • Saeed Jafari

    (Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Abdolhossein Hemmati-Sarapardeh

    (Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran)

  • Amir H. Mohammadi

    (Discipline of Chemical Engineering, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South Africa)

  • Sohrab Zendehboudi

    (Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL A1B 3X5, Canada)

Abstract

Carbon dioxide-based enhanced oil-recovery (CO 2 -EOR) processes have gained considerable interest among other EOR methods. In this paper, based on the molecular weight of paraffins (n-alkanes), pressure, and temperature, the magnitude of CO 2 –n-alkanes interfacial tension (IFT) was determined by utilizing soft computing and mathematical modeling approaches, namely: (i) radial basis function (RBF) neural network (optimized by genetic algorithm (GA), gravitational search algorithm (GSA), imperialist competitive algorithm (ICA), particle swarm optimization (PSO), and ant colony optimization (ACO)), (ii) multilayer perception (MLP) neural network (optimized by Levenberg-Marquardt (LM)), and (iii) group method of data handling (GMDH). To do so, a broad range of laboratory data consisting of 879 data points collected from the literature was employed to develop the models. The proposed RBF-ICA model, with an average absolute percent relative error (AAPRE) of 4.42%, led to the most reliable predictions. Furthermore, the Parachor approach with different scaling exponents (n) in combination with seven equations of state (EOSs) was applied for IFT predictions of the CO 2 –n-heptane and CO 2 –n-decane systems. It was found that n = 4 was the optimum value to obtain precise IFT estimations; and combinations of the Parachor model with three-parameter Peng–Robinson and Soave–Redlich–Kwong EOSs could better estimate the IFT of the CO 2 –n-alkane systems, compared to other used EOSs.

Suggested Citation

  • Farzaneh Rezaei & Amin Rezaei & Saeed Jafari & Abdolhossein Hemmati-Sarapardeh & Amir H. Mohammadi & Sohrab Zendehboudi, 2021. "On the Evaluation of Interfacial Tension (IFT) of CO 2 –Paraffin System for Enhanced Oil Recovery Process: Comparison of Empirical Correlations, Soft Computing Approaches, and Parachor Model," Energies, MDPI, vol. 14(11), pages 1-25, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3045-:d:561331
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    References listed on IDEAS

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    1. Zendehboudi, Alireza & Tatar, Afshin & Li, Xianting, 2017. "A comparative study and prediction of the liquid desiccant dehumidifiers using intelligent models," Renewable Energy, Elsevier, vol. 114(PB), pages 1023-1035.
    2. Hemmati-Sarapardeh, Abdolhossein & Varamesh, Amir & Husein, Maen M. & Karan, Kunal, 2018. "On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 313-329.
    3. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
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