Enhanced oil recovery in tight conglomerate reservoirs using CO2 flooding: A visual model with fractal-based zoning

CO 2 flooding has emerged as a valuable method for enhancing oil recovery (EOR) in fossil fuel reservoirs. However, the impact of micro-heterogeneity, particularly variations in pore sizes, on CO 2 flooding following water flooding in conglomerate reservoirs remains insufficiently understood. This study introduces an advanced visual model integrating outcrop and nuclear magnetic resonance (NMR) analyses to overcome the limitations of traditional micromodels. Simulating reservoir conditions, the model evaluates oil displacement and sweep efficiency through a fractal-based pore classification system, categorizing pores into four types: small pores (P1), medium pores (P2 and P3), and large pores (P4). This classification provides a comprehensive analysis of residual oil patterns during water and CO 2 flooding. Results show that water flooding primarily displaces oil from larger pores (P3 and P4), leaving residual oil trapped in smaller pores (P1 and P2). After 0.4 PV injection, oil begins migrating from smaller to larger pores(P4), reaching an oil recovery efficiency of 28.91% at 0.8 PV. In contrast, CO 2 flooding significantly expands the sweep area and improves displacement efficiency despite minor gas channeling. NMR analysis indicates that CO 2 flooding rapidly mobilizes oil in large pores (P4), while its effect on smaller pores (P1 and P2) remains limited. The cumulative signal amplitude decreases from 2914 to 2498, resulting in a displacement efficiency of 10.15% and a total recovery factor of 39.06%. This study provides valuable insights into optimizing CO 2 immiscible flooding strategies and improving oil recovery efficiency in tight conglomerate reservoirs.