Predicting and optimizing CO2 foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension

Abstract

Carbon dioxide foam injection stands as a promising method for enhanced oil recovery (EOR) and carbon sequestration. However, accurately predicting its efficiency amidst varying operational conditions and reservoir parameters remains a significant challenge for conventional modeling techniques. This study explores the application of machine learning (ML) methodologies to develop a robust model for matching experimental values in CO2 foam flooding scenarios. Leveraging a comprehensive dataset encompassing diverse surfactants and rock types, with varied porosity and permeability, our model demonstrates accurate predictions across a wide spectrum of conditions. By focusing on key parameters such as foam apparent viscosity, interfacial tension (IFT), injected foam volume, initial oil saturation, porosity, and permeability, we unveil the pivotal role of these factors in determining CO2 foam EOR performance. Through rigorous analysis, we identify the relative importance of each input parameter, with injected foam volume, apparent viscosity, and IFT emerging as dominant factors. The most accurate model was deep neural network (DNN) (R2 value of 0.99). Higher foam viscosity and lower IFT were found to significantly enhance oil recovery rates, though their effects plateau beyond certain thresholds (apparent viscosities above 1200 cP and IFT values below 0.2 mN/m). The findings underscore the potential of ML-driven approaches in enhancing CO2 foam EOR predictions, offering insights crucial for optimizing foam flooding performance across diverse reservoir settings.