Variation patterns and distribution characteristics of crude oil components during CO2 flooding in deep oil reservoirs

• Published in: Physics of fluids (1994) Vol. 37; no. 7
• Main Authors: Chen, Shun, Dong, Pingchuan, Zhang, Youheng, Li, Lili
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 01.07.2025
• Subjects: Carbon dioxide; Crude oil; Enhanced oil recovery; Equations of state; High temperature; Interlayers; Optimization; Permeability; Porosity; Reservoirs; Saturation; Spatial distribution
• ISSN: 1070-6631; 1089-7666
• DOI: 10.1063/5.0274105

Deep reservoirs present challenges for CO2-enhanced oil recovery (EOR) attributable to high temperatures, pressures, and low porosity and permeability. While core flooding experiments have been widely employed to investigate crude oil compositional changes after CO2 injection, research on spatial distribution and compositional evolution under complex reservoir conditions remains limited. Understanding these variations is critical for optimizing CO2-EOR applications. This study establishes a reservoir model using the Peng–Robinson equation of state and numerical simulations to analyze crude oil compositional changes under varying injection rates, oil saturation, CO2 mole fractions, crude oil production rates, injection pressures, porosity, and permeability. Results indicate that increasing CO2 injection raises the second peak of C1 extraction in mid-stage displacement. When residual oil saturation is 0.6, C1 in the first to fifth layers increases 184.58-fold, demonstrating a top-layer displacement. Higher oil saturation shifts the stable component from C6 to C5. Increasing CO2 mole fraction from 0.2 to 1 reduces C29+ variation by 16.13% while raising production from 60 to 90 m3/d increases residual C29+ by 4%. Raising the injection pressure from 5to 20 MPa increases C1 in the first to fifth layers by up to 287-fold. As porosity rises from 0.22 to 0.28, interlayer crude oil distribution becomes more uniform. Higher permeability (100–400 mD) decreases C1 by 74.59% and increases C29+ by 42.95%. Gray relational analysis identifies CO2 mole fraction as the most influential factor. These findings offer critical insights into the optimization of CO2-EOR in deep reservoirs.