Extensive Study on the Influencing Parameters of Sc CO₂ Foam Viscosity for Enhanced Oil Recovery and Carbon Sequestration: A Machine Learning Approach

Foam flooding has been used to control gas mobility during oil displacement and CO₂ sequestration processes in subsurface porous media, mitigating the negative impacts of low gas viscosity, reservoir heterogeneity, and gravity override. In this research, we study the application of machine learning (ML) to develop a data-driven prediction of the effective viscosity of supercritical CO₂ foam (Sc-CO₂) for enhanced oil recovery (EOR) and CO₂ sequestration. The ML approach is used to overcome the challenge of using physical correlations to account for the effect of key experimental parameters on the viscosity of supercritical CO₂ foam. The experimental data for evaluating the effective Sc-CO₂ foam viscosity were measured using a high-pressure high-temperature foam rheometer (Model 8500) under different temperatures (50-110 °C), pressures (1000-3000 psi), foam qualities (30-90%), and surfactant concentrations (0.1-0.5 wt.%) at shear rates between 100-1450 s^-1. A total of 5,552 data points were used as primary data for developing supervised ML regression models. Machine learning algorithms from the Scikit-learn library, such as K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting (GB), and AdaBoosting (AB), were used. The results revealed that machine learning algorithms generated models for the effective viscosity of Sc-CO₂ foam with predictive accuracies of 0.989, 0.987, 0.941, and 0.723 for RF, KNN, GB, and AB, respectively. The RF and KNN algorithm demonstrated superior performance among all the other algorithms, with RF being better in terms of accurate viscosity prediction across different viscosity values. This paper provides data-driven approach that can predict the effective foam viscosity under different reservoir conditions which leads to the design of an optimum injection strategy and effectively controls Sc-CO₂ mobility for EOR and CO₂ sequestration.