TY  - JOUR
AU  - WANG, Xiaopu
AU  - MA, Kefan
AU  - WANG, Qingxuan
AU  - ZHANG, Liming
AU  - ZHANG, Kai
AU  - ALFARISI, Omar
AU  - LI, Zhaomin
AU  - LI, Binfei
PY  - 2026
TI  - Analysis of the seepage mechanism of brine-CO2 oil displacement and storage in heterogeneous porous media with carbonate coating
JO  - Experimental Technology and Management
SN  - 1002-4956
SP  - 39
EP  - 45
VL  - 43
IS  - 4
AB  - ObjectiveCarbonate reservoirs have become strategic targets for reserve expansion in China and the Middle East, driven by the dual goals of reducing carbon emissions and ensuring energy security. However, their significant heterogeneity, complex pore structures, and wettability changes present considerable challenges to the efficiency of CO2-based enhanced oil recovery (EOR). At the pore level, the interaction of capillary forces, viscous forces, and the evolution of multiphase interfaces causes unstable displacement fronts and severely limits sweep efficiency in low-permeability areas.MethodsTo tackle these issues, this study aims to reveal the pore-scale multiphase seepage mechanisms of brine–CO2 displacement in carbonate-coated heterogeneous porous media. This provides a microscopic foundation for optimizing CO2 flooding parameters and enhancing sweep performance in actual carbonate reservoirs. A heterogeneous pore network was constructed using a microfluidic chip, and calcium carbonate was coated in situ to simulate authentic carbonate reservoir surfaces and wettability. A series of visualization experiments were conducted at a controlled temperature (40 ℃). CO2 foam flooding and brine flooding at different injection rates were compared. A CCD imaging system was used to capture pore-scale evolution of oil, water, and gas phases, and gas saturation and residual oil distributions were quantified through image processing. To improve the accuracy of residual oil characterization, the ResNet152 deep neural network was trained on 2885 labeled microfluidic sub-images from CO2 flooding, CO2–water alternating flooding, and brine flooding. Using weighted cross-entropy loss, AdamW optimization, and learning rate scheduling, the model achieved high classification accuracy for dispersed, mixed, and heterogeneous residual oil.ResultsResults showed that flooding performance was strongly affected by injection rate and pore-structure heterogeneity. At moderate flow rates (0.5–3 μL·min–1), CO2 foam greatly improved sweep efficiency, nearly eliminating residual oil saturation. Foam viscosity and the Jamin effect effectively suppressed viscous fingering and prevented preferential flow through high-permeability channels, forcing the displacing phase into low-permeability areas. Conversely, at very low injection rates (0.1 μL·min–1), foam instability caused large dispersed gas bubbles, limiting gas saturation to 25%, and hindered oil droplet mobilization, resulting in a high residual oil saturation of 42%. Gas saturation displayed a parabolic relationship with flow rate, with the maximum (93%) at 1 μL·min–1, where bubble size was smallest, and foam stability was optimal. Deep-learning-based oil classification also showed that brine flooding and CO2–water alternating flooding primarily produced dispersed residual oil, whereas surfactant-assisted CO2 flooding created a mixture of dispersed (49%), mixed (36%), and heterogeneous (14%) oil, reflecting foam instability and uneven sweep in highly heterogeneous zones. The model achieved a validation accuracy of 93%, confirming its effectiveness in pore-scale residual oil identification.ConclusionsThis study clarifies the mechanisms underlying brine–CO2 displacement in carbonate-coated heterogeneous media. Calcium carbonate coating increases hydrophobicity, delays breakthrough in high-permeability pathways, and significantly enhances sweep in low-permeability zones, reducing residual oil by up to 28%. CO2 foam flooding is highly sensitive to injection rate, with moderate flow rates producing stable foam, high gas saturation, and efficient oil mobilization, whereas very low or high rates reduce displacement stability. By combining microfluidic visualization and deep-learning image analysis, this research offers microscopic insights for optimizing CO2 flooding conditions and provides technical guidance for deploying CO2-based EOR in Middle Eastern carbonate reservoirs. The findings also support international cooperation under the Belt and Road Initiative and contribute to global efforts in the low-carbon, efficient development of carbonate oilfields.
UR  - https://doi.org/10.16791/j.cnki.sjg.2026.04.005
DO  - 10.16791/j.cnki.sjg.2026.04.005