Modeling interfacial tension and minimum miscibility pressure in paraffin-nitrogen systems: Application to gas injection processes

Nitrogen has emerged as an attractive gas for many petroleum and chemical engineering applications such as gas lift, gas recycling, pressure maintenance, and enhanced oil recovery, especially for some high-pressure reservoirs. Accurate determination of interfacial tension (IFT) in nitrogen/hydrocarbon systems is crucial and required for numerous applications in chemical and petroleum industries. The experimental measurements of IFT are costly, cumbersome and time consuming, especially at high pressure-high temperature conditions. In this study, the IFT of normal alkanes from n-C-5 to n-C-16 (as the representatives of crude oil) and nitrogen is modeled at a wide range of pressure (from 0.1 to 69 MPa) and temperature (from 295 to 442 K) using group method of data handling (GMDH). Three inputs were used for modeling including pressure, temperature, and molecular weight of normal alkane. To develop the most efficient model, 60% of the dataset was used for model development and the remaining 40% was used to check the validity and accuracy of the developed model. The proposed model predicts the data satisfactorily with an average absolute relative error of 3.81% and 3.91% in training and testing subsets, respectively. Then, the proposed model was compared to the well-known IFT models, namely Linear Gradient Theory (LGT), Density Gradient Theory (DGT), and Parachor approaches combined with the Volume Translated Predictive Peng Robinson Equation of State (VT-PPR EOS). The results demonstrate that the proposed model not only is superior to the existing models in terms of accuracy, but also can predict the IFT with a simple mathematical expression with a quite low computational cost. Finally, a simple-to -use algorithm was proposed to calculate the minimum miscibility pressure (MMP) in normal alkane/nitrogen systems based on the measurements with vanishing interfacial tension (VIT) technique, which predicts the MMP with high accuracy. (C) 2017 Elsevier Ltd. All rights reserved.