A new approach for predicting oil recovery factor during immiscible CO2 flooding in sandstones using dimensionless numbers

• Published in: Journal of petroleum exploration and production technology 2019-09, Vol.9 (3), p.2325-2332
• Main Authors: Zivar, Davood, Pourafshary, Peyman
• Format: Article
• Language: English
• Subjects: Analytics; Capillary number; Capillary pressure; Carbon dioxide; Composition; Correlation; Correlation analysis; Crude oil; Data; Data analysis; Data recovery; Dimensionless numbers; Earth and Environmental Science; Earth Sciences; Energy Systems; Enhanced oil recovery; Flood predictions; Flooding; Gas flooding; Geology; Hydrologic data; Immiscible gas injection; Industrial and Production Engineering; Industrial Chemistry/Chemical Engineering; Injection; Methods; Miscibility; Monitoring/Environmental Analysis; New records; Offshore Engineering; Oil recovery; Original Paper - Production Engineering; Parameters; Permeability; Porosity; Pressure ratio; Recovery; Sandstone
• ISSN: 2190-0558; 2190-0566
• DOI: 10.1007/s13202-019-0630-0

CO 2 injection is one of the most promising techniques to enhance oil recovery. The most favorable properties of CO 2 made this method popular and it has been widely used since 1950. Experimentally, the effect of CO 2 injection on incremental oil recovery is widely measured by the core-flooding approach. An accurate estimation of the recovery factor is required to analyze the performance of the method to design the enhanced oil recovery method successfully. Hence, knowledge of the effects of different parameters on recovery is essential. Various reported experimental CO 2 core-flooding data for the immiscible condition in sandstones were analyzed to develop the parametric relationships affecting ultimate oil recovery using data analytics. Selected data support a wide range of porosity (10.8–37.2%), permeability (1–18000 mD), injection pressure (2.73–11.44 MPa), injection rate (0.1–1.0 cm 3 /min), and crude oil types, which enhance the methodology used to develop more comprehensive dimensionless numbers and correlations to predict the oil recovery. Series of new dimensionless numbers were defined and used for the study to develop a correlation for predicting oil recovery factor. Capillary number, relative radius, injection pressure ratio, and oil composition number are used as dimensionless numbers in our approach. The oil recovery prediction by the developed correlation was in agreement with the experimental data. The proposed correlation shows that capillary number is the most effective parameter when predicting oil recovery.