On the Evaluation of Interfacial Tension (IFT) of CO2–Paraffin System for Enhanced Oil Recovery Process: Comparison of Empirical Correlations, Soft Computing Approaches, and Parachor Model

Farzaneh Rezaei,Amin Rezaei,Saeed Jafari,Abdolhossein Hemmati-Sarapardeh,Sohrab Zendehboudi,Amir H Mohammadi

doi:10.3390/en14113045

Abstract

Carbon dioxide-based enhanced oil-recovery (CO2-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 CO2–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 CO2–n-heptane and CO2–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 CO2–n-alkane systems, compared to other used EOSs.

Highlights

We proposed the implantation of some algorithms, such as Levenberg–Marquardt (LM), to optimize the multilayer perception (MLP) neural network model, and the imperialist competitive algorithm (ICA), particle swarm optimization (PSO), gravitational search algorithm (GSA), genetic algorithm (GA), and ant colony optimization (ACO) for optimization of the radial basis function (RBF) neural network model
To develop the mathematical model using group method of data handling (GMDH), the experimental data were classified into three groups based on the molecular weights of n-alkanes, including less than 128 g·mol−1 from C4 H10 (58) to C8 H18 (114), more than or equal to 128 and less than 170 g·mol−1 from
The real interfacial tension (IFT) data were compared against the results obtained from the Parachor approaches coupled with a number of equations of state, including the Zudkevitch–Joffe (ZJ), Schmidt–Wenzel (SW), Redlich–Kwong (RK), two- and threeparameter Peng–Robinson (PR2 and PR3, respectively), and two- and three-parameter

Summary

Introduction

Miscible and/or immiscible injection of various gases, including nitrogen, flue gas, carbon dioxide, and natural gas, are considered as the most conventional EOR techniques [6,7,8]. As an efficient EOR strategy, CO2 injection can lead to more oil recovery by reducing oil viscosity, swelling, and vaporizing during miscible flooding [9]. CO2 injection is a widely accepted method due to the reduction of greenhouse gas emissions by sequestrating CO2 in underground formations for a long term [10,11,12,13,14,15]. CO2 injection, which has been introduced for both EOR and CO2 sequestration purposes, is further intended to decrease the emission of greenhouse gases [12,16]

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Conclusion