On the evaluation of the interfacial tension of immiscible binary systems of methane, carbon dioxide, and nitrogen-alkanes using robust data-driven approaches

• Published in: Alexandria engineering journal Vol. 61; no. 12; pp. 11601 - 11614
• Main Authors: Mahdaviara, Mehdi, Nait Amar, Menad, Ostadhassan, Mehdi, Hemmati-Sarapardeh, Abdolhossein
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2022; Elsevier
• Subjects: Cascaded Feedforward Neural Network (CFNN); Decision Tree Learning (DT); Interfacial tension (IFT); n-alkanes; Natural gas; Cascaded Feedforward Neural Network (CFNN); Decision Tree Learning (DT); Interfacial tension (IFT); n-alkanes; Natural gas
• ISSN: 1110-0168
• DOI: 10.1016/j.aej.2022.04.049

Gas injection has emerged over the recent decades as a promising technology to enhance oil recovery in various fields worldwide. The efficiency and success of a gas injection operation can be assessed through a number of vital experimental studies. Interfacial Tension (IFT) between the injected gas and the displacing fluid is a key parameter playing an eminent role in the foregoing studies. The main scope of this work is making a progress in modeling the IFTs between diverse n-alkanes and Methane (CH4), Carbon Dioxide (CO2), and Nitrogen (N2) natural gases. For this purpose, two smart AI-based approaches of Cascaded Feedforward Neural Network (CFNN) and Decision Tree Learning (DT) were used to simultaneously model the IFTs between foregoing immiscible binary systems as a function of pressure, temperature, the gases properties, and the properties of the liquid. Several statistical measures and graphical descriptions were employed to aid the accuracy analysis of the proposed models. Both developed CFNN and DT networks represented desirable close-to-reality predictions in all binary systems. Besides, CFNN established itself as the most robust model for all studies binary systems with RMSE values of 0.5924, 0.5649, and 0.5870 mN/m, and R2 values of 0.9902, 0.9910, and 0.9904 for the train, test, and overall data, respectively.