Machine learning prediction and optimization of CO2 foam performance for enhanced oil recovery and carbon sequestration: Effect of surfactant type and operating conditions

Abstract

Foam flooding handles the challenges of gas injection constituting a high viscosity medium that sweeps oil more efficiently meanwhile creating a barrier that prevents leakage of gas phase. Therefore, it is a promising enhanced oil recovery (EOR) method, simultaneously an efficient carbon dioxide (CO2) storage technology. The foam's performance displays susceptibility to various reservoir and operational parameters. Numerous laboratory experiments would need to be carried out to study foams and find the optimum foam formulation for the given core conditions. Data-driven approaches can be an alternative method to the time-consuming experimental and conventional modeling techniques, which often face challenges to incorporate the effect of important related parameters such as temperature and type of surfactant to the performance of foam. In the current study, machine learning (ML) models were constructed to predict the CO2 foam apparent viscosity in carbonate and sandstone formations focusing on experimental data from the literature for CO2 foams stabilized by different surfactants. Various anionic, cationic, and nonionic surfactants were considered and characterized based on the hydrophilic-lipophilic balance (HLB) number. Predictive models were developed using ML algorithms based on the data available for eight key process parameters. Artificial neural networks and extra randomized tree algorithms provided the most precise prediction among the applied algorithms. Various predictions for foam apparent viscosity on hypothetical case studies were made on altered input parameters, enabling CO2 mobility to co-optimize EOR and CO2 sequestration while selecting the optimum surfactant for each case.