Mechanistic Study of CO 2 -Based Oil Flooding in Microfluidics and Machine Learning Parametric Analysis

Chunxiu Shen, Lianjie Hou, Ze Zhou, Yanxing Wang, Omar Alfarisi, Sergey E. Chernyshov, Junrong Liu, Shuyang Liu, Jianchun Xu and Xiaopu Wang ()
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Chunxiu Shen: State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
Lianjie Hou: State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
Ze Zhou: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Yanxing Wang: Oilfield Development Division, PetroChina Changqing Oilfield Company, Xi’an 710200, China
Omar Alfarisi: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Sergey E. Chernyshov: Oil and Gas Technologies Department, Perm National Research Polytechnic University, 614990 Perm, Russia
Junrong Liu: State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
Shuyang Liu: State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
Jianchun Xu: State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China
Xiaopu Wang: State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), Qingdao 266580, China

Energies, 2025, vol. 18, issue 16, 1-16

Abstract: CO 2 -enhanced oil recovery (CO 2 -EOR) has gained prominence as an effective oil displacement method with low carbon emissions, yet its microscopic mechanisms remain incompletely understood. This study introduces a novel high-pressure microfluidic visualization system capable of operating at 0.1–10 MPa without confining pressure and featuring stratified porous media with a 63 μm minimum throat size to provide unprecedented insights into CO 2 and CO 2 -foam EOR processes at the microscale. Through quantitative image analysis and advanced machine learning modeling, we reveal that increasing the CO 2 injection pressure nonlinearly reduces residual oil saturation, achieving near-complete miscibility at 6 MPa with only 2% residual oil—a finding that challenges conventional thresholds for miscibility in heterogeneous systems. Our work uniquely demonstrates that CO 2 -foam flooding not only mobilizes capillary-trapped oil films but also dynamically alters interfacial tension and the pore-scale fluid distribution, a phenomenon previously underexplored. Support Vector Regression (R 2 = 0.71) further uncovers a nonlinear relationship between the surfactant concentration and residual oil saturation, offering a data-driven framework for parameter optimization. These results advance our fundamental understanding by bridging microscale dynamics with field-applicable insights, while the integration of machine learning with microfluidics represents a methodological leap for EOR research.

Keywords: foam injection; pressure-resistant microfluidic chip; heterogeneous porous media; microvisualization system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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