Data-driven modeling of interfacial tension in impure CO2-brine systems with implications for geological carbon storage

Global climate change is a pressing problem, and given the current state of development of renewable energies, Carbon Capture and Storage (CCS) appears a promising solution. Deep saline aquifers are the most accessible geological locations for safe CO2 sequestration, where CO2 can be securely trapped mostly by structural and capillary trapping mechanisms. The efficiency of both mechanisms is governed by multiphase flow characteristics that are heavily dependent on interfacial tension (IFT) between the gaseous and aqueous phases. In this study, we conduct a comprehensive data-driven study on the interfacial tension of pure and impure gas-brine mixtures within saline aquifers. We carefully collect 2517 experimental data, upon which we develop seven machine-learning based models for predicting the IFT. These include, namely, four differently optimized multilayer perceptron models, a radial basis function neural network, a least squares support vector machine, and a group method of data handling-type neural network. The three most accurate intelligent models are subsequently incorporated into a state-of-the-art committee machine intelligent system. We use a pseudo (average) critical temperature variable to capture the gas phase impurities while developing a general-in-purpose predictive tool for any gas mixture. We comprehensively evaluate the accuracy and statistical validity of our models, and demonstrate their robustness relative to major empirical correlations currently in use. The analyses reveal the non-trivial behavior of IFT against various parameters. Finally, we elucidate the impact of gas and brine impurities on the storage capacity of saline aquifers at various in-situ conditions.