Dimensionless numbers enable accurate scaling from laboratory to field conditions. Estimating the oil recovery factor is important for evaluating the performance of different recovery methods and designing effective EOR processes. However, conventional dimensionless times and capillary numbers often fail to accurately predict recovery performance. This study investigates the scaling criteria used to evaluate immiscible and miscible floods by deriving new dimensionless groups or modifying the existing ones. Our findings were published in SPE-224001-MS, where we conducted a comprehensive dimensionless analysis on existing literature data to predict the oil recovery factors in water, chemical, and solvent flooding experiments.

This work analyzes core flooding experiments under various conditions to develop and modify dimensionless numbers by incorporating viscosity ratios, oil density, reservoir thickness, porosity, and permeability. A log-linear relationship was observed between recovery factor and dimensionless time (t_D) for water flooding, indicating that oil recovery increased with t_D. Injection velocity and reservoir characteristics influenced recovery performance. Incorporating porous media dimensions and permeability into dimensionless capillary number reduced scatter in residual oil saturation datasets studied in this research. A new dimensionless number, N_Ca*, was proposed by incorporating reservoir height and oil density into an existing diffusion number, which improved the predictive ability of the dimensionless number for solvent flooding data and reducing data scatter.

Key Takeaways:

• Incorporating porous media dimensions and permeability in dimensionless capillary number reduced residual oil saturation variability.
• Injection velocity, porosity, permeability, and reservoir length must be incorporated in the classical dimensionless numbers for accurate predictive ability.