From Displacement Physics to Intelligent and Sustainable EOR: Multiscale Evolution of Enhanced Oil Recovery Mechanisms, Technologies, and Strategies Aligned with the Sustainable Development Goals (2000–2026)
DOI:
https://doi.org/10.5281/zenodo.23377477Palavras-chave:
Enhanced Oil Recovery, Sustainable Development Goals (SDGs), Energy Sustainability, Flow in Porous Media, Enhanced Oil Recovery (EOR), Artificial IntelligenceResumo
Enhanced Oil Recovery (EOR) is a fundamental approach for increasing oil production from mature reservoirs, in which a significant fraction of the oil remains trapped due to capillary forces, unfavorable mobility ratios, heterogeneity, and preferential flow. This review analyzes the evolution of physics applied to EOR between 2000 and 2026, highlighting the transition from methods based on the optimization of isolated properties toward integrated, multiscale, and reservoir-specific strategies. The main mechanisms associated with low-salinity water and smart water, polymers, surfactants, ASP processes, miscible CO₂ and CO₂-foam, nanotechnology, microfluidics, and digital rock physics are discussed. The analysis demonstrates that mobility, capillarity, wettability, interfacial tension, miscibility, rheology, and transport act in a coupled manner, making it inadequate to evaluate any technology based on a single performance parameter. The development of microfluidics, computed tomography, and digital rock reconstructions has expanded the ability to directly observe pore-scale phenomena and relate them to macroscopic responses. More recently, physics-informed machine learning, Physics-Informed Neural Networks (PINNs), neural networks, and neural operators have been used to integrate experimental data and physical laws for the prediction and optimization of recovery processes. Parallelly, it is demonstrated that the evolution of contemporary EOR extends beyond production maximization by directly aligning with the United Nations Sustainable Development Goals (SDGs) of the 2030 Agenda, highlighting resource optimization in mature fields (SDG 12 - Responsible Consumption and Production), digitalization and AI innovation (SDG 9 - Industry, Innovation, and Infrastructure), permanent geological carbon storage via CCUS (SDG 13 - Climate Action), and transitional energy efficiency (SDG 7 - Affordable and Clean Energy). It is concluded that the main frontier of contemporary EOR does not lie in the search for a universal technology, but rather in the combination of physical mechanisms, experimental characterization, computational modeling, artificial intelligence, and global sustainability guidelines. This integration establishes the foundations for intelligent, adaptive, multiscale, sustainable EOR guided by the characteristics and uncertainties of each reservoir. It is concluded that the main frontier of contemporary EOR does not lie in the search for a universal technology, but rather in the combination of physical mechanisms, experimental characterization, computational modeling, and artificial intelligence. This integration establishes the foundations for intelligent, adaptive, multiscale EOR guided by the characteristics and uncertainties of each reservoir.
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