Molecular Transition Mechanisms of Heavy Oil in Hybrid CO2-surfactant Thermal Systems for Post-steam Reservoirs
Bibliographic record
Abstract
A hybrid CO2-surfactant thermal system can efficiently and eco-friendly improve the oil recovery rate for post-steam heavy oil reservoirs by modifying heavy oil characteristics.However, the complex conditions of high-temperature and high-pressure reservoirs hinder experimental investigations into the microscopic mechanisms of this system.In this study, Molecular Dynamics (MD) simulations reveal the molecular interaction mechanism between a hybrid thermal system, heavy oil, and pore surfaces.The hybrid thermal systems comprise CO2 and a cost-effective SDS (sodium dodecyl sulfate) surfactant.The effects of surface wettability, external temperature, and CO2 concentration on the transition of heavy oil microstructure are investigated.Results show that the hybrid CO2-surfactant systems can effectively improve the microstructure of heavy oil by promoting the thermal expansion process.Moreover, surface wettability and CO2 concentration significantly affect the microstructure of heavy oil.Specifically, the thermal expansion of heavy oil is suppressed on hydrophobic surfaces compared to hydrophilic surfaces.This is because an oil-surface interaction promotes the formation of dense clusters of asphaltenes within a heavy oil layer, making the heavy oil on hydrophobic surfaces more challenging to recover.Meanwhile, the concentration of CO2 determines the state of a hybrid thermal system and ultimately affects the distribution of heavy oil.The hybrid thermal system with moderate dynamic activity and a high effective distribution ratio of CO2 can efficiently improve the microstructure of heavy oil for recovery and demonstrate the potential of CO2 storage.Furthermore, an optimum CO2 concentration of 10 wt.% is recommended for designing the hybrid thermal system.This study provides insights into the molecular transition mechanism of heavy oil in various hybrid thermal systems.It offers valuable theoretical guidance for designing efficient and eco-friendly heavy oil recovery operations to support the transition towards carbon neutrality.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".