Advances in Thermal Enhanced Oil Recovery: From Fundamental Mechanisms to Field-Scale Applications and Screening Frameworks

Thermal traditional enhanced oil recovery (EOR) screening has long relied on static lookup tables that filter candidate reservoirs using fixed, isolated property cutoffs such as depth, net pay, permeability, and viscosity. While useful for preliminary sorting, these static matrices fail to capture non-isothermal thermodynamics, transient geomechanical stress fields, dynamic fluid-phase behaviors, and the stringent economic and environmental constraints governing modern heavy oil assets. This manuscript introduces a dynamic, four-stage integrated decision architecture that transforms thermal EOR (TEOR) screening from a passive collection of property thresholds into an actionable, physics-governed engineering workflow. The primary novelty of this work lies in its holistic synthesis across five previously siloed operational domains: steam-solvent hybrids, late-stage SAGD with non-condensable gas (NCG) co-injection, electromagnetic/radio-frequency heating, machine learning applications, and CCUS-coupled recovery. Unlike previous reviews that analyze standalone recovery techniques or unconstrained “black-box” predictive models, this framework explicitly integrates operational boundary exceptions (for example, Vacuum Insulated Tubing, Downhole Steam Generation, and solvent prepads) that extend traditional depth, thin-pay, and tight-matrix screening limits. Furthermore, it embeds concrete, physics-informed machine learning applications—ranging from facies proxy models and spatial–temporal steam chamber predictors to dynamic Steam-Oil Ratio (SOR) optimizers and real-time failure detectors—directly into sequential screening gates. Every parameter cutoff across Cyclic Steam Stimulation, Steam-Assisted Gravity Drainage, In Situ Combustion, and Electromagnetic Heating is thoroughly justified through fluid dynamics, heat transfer thermodynamics, geomechanics, and combustion kinetics. By systematically evaluating candidate reservoirs through four sequential decision gates, Thermodynamic Viability, Container Integrity, Lifecycle Carbon and Economic Balancing, and Modular Field Pilots with Real-Time Sentinel Monitoring, this architecture provides petroleum engineers and asset managers with an end-to-end roadmap to de-risk capital deployment, optimize thermal efficiency, and align heavy oil operations with global decarbonization imperatives.