Artificial Intelligence-based Modeling of Interfacial Tension for Carbon Dioxide Storage

Abstract: A key variable for determining carbon dioxide (CO2) storage capacity in sub-surface reservoirs is the interfacial tension (IFT) between formation water (brine) and injected gas. Establishing efficient and precise models for estimating CO2 – brine IFT from measurements of independent variables is essential. This is the case because laboratory techniques for determining IFT are time-consuming, costly and require complex interpretation methods. For the datasets used in the current study, correlation coefficients between the input variables and measured IFT suggest that CO2 density and pressure are the most influential variables, whereas brine density is the least influential. Six artificial neural network configurations are developed and evaluated to determine their relative accuracy in predicting CO2–brine IFT. Three models involve multilayer perceptron (MLP) tuned with Levenberg-Marquardt, Bayesian regularization and scaled conjugate gradient back-propagation algorithms, respectively. Three models involve the radial basis function (RBF) trained with particle swarm optimization, differential evolution, and farmland fertility optimization algorithms, respectively. The six models all generate CO2– brine IFT predictions with high accuracy (RMSE <0.7 mN/m). However, the RBF models consistently provide slightly higher IFT prediction accuracies (RMSE <0.54 mN/m) than the MLP models.