Publications
Article · Gas Science and Engineering · July 24, 2026
Physics-guided machine learning using engineered dimensionless parameters to predict CO2 trapping indices in heterogeneous carbonate saline aquifers
Ilya Bayanov, Irina Spivakova, Peyman Pourafshary, Masoud Riazi
CO2 Sequestration and Geologic InteractionsReservoir Engineering and Simulation MethodsEnhanced Oil Recovery TechniquesCO2 geological storagedimensionless parametersphysics-guided machine learning
More by Peyman Pourafshary
- Effect of Monovalent–Divalent Ions on Fines-Migration Initiation in Sandstone: DLVO Analysis and Experimental ValidationEnergies · 2026
- Experimental Evaluation of Relative Permeability Modifiers Application to Control Water Cut in Fields in KazakhstanSPE Annual Technical Conference and Exhibition · 2026
- Functionalized carbon nanotubes enhance CO2 mass transfer and uptake in high-salinity brineSeparation and Purification Technology · 2026
- Critical review of emulsion stability characteristics: additives, experiments, and challengesJournal of Molecular Liquids · 2026
