Molecular Insights into Heavy Oil Mobilization Mechanism in Hybrid Thermal Processes: A QCM-D and NEMD Simulation Study
Bibliographic record
Abstract
Hybrid thermal processes are promising enhanced oil recovery (EOR) methods integrating thermal and chemical mechanisms. Optimizing these processes requires a clear understanding of multiphase interactions among water, oil, rock surfaces, and additives. This study combined quartz crystal microbalance with dissipation (QCM-D) experiments and nonequilibrium molecular dynamics (NEMD) simulations to characterize the microscopic occurrence and displacement dynamics of heavy oil under hybrid thermal systems. Results demonstrate that surface wettability significantly affects the occurrence and mobilization of heavy oil. Specifically, heavy oil on hydrophobic surfaces is difficult to unlock. However, hybrid CO 2 -surfactant systems can mobilize this stable oil. During displacement, CO 2 and the surfactant synergistically promote tilting desorption of heavy oil, enlarging the oil–water interface and reducing oil-surface interaction, thereby improving recovery. Key operating parameters are assessed for field implementation. This study provides theoretical support for a broader deployment of hybrid thermal processes, demonstrates potential for concurrent CO 2 utilization, and supports environmentally responsible oil-recovery practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".