Hybrid CO2 thermal system for post-steam heavy oil recovery: Insights from microscopic visualization experiments and molecular dynamics simulations
Bibliographic record
Abstract
The hybrid CO 2 thermal technique has achieved considerable success globally in extracting residual heavy oil from reserves following a long-term steam stimulation process. Using microscopic visualization experiments and molecular dynamics (MD) simulations, this study investigates the microscopic enhanced oil recovery (EOR) mechanisms underlying residual oil removal using hybrid CO 2 thermal systems. Based on the experimental models for the occurrence of heavy oil, this study evaluates the performance of hybrid CO 2 thermal systems under various conditions using MD simulations. The results demonstrate that introducing CO 2 molecules into heavy oil can effectively penetrate and decompose dense aggregates that are originally formed on hydrophobic surfaces. A stable miscible hybrid CO 2 thermal system, with a high effective distribution ratio of CO 2 , proficiently reduces the interaction energies between heavy oil and rock surfaces, as well as within heavy oil. A visualization analysis of the interactions reveals that strong van der Waals (vdW) attractions occur between CO 2 and heavy oil molecules, effectively promoting the decomposition and swelling of heavy oil. This unlocks the residual oil on the hydrophobic surfaces. Considering the impacts of temperature and CO 2 concentration, an optimal gas-to-steam injection ratio (here, the CO 2 : steam ratio) ranging between 1:6 and 1:9 is recommended. This study examines the microscopic mechanisms underlying the hybrid CO 2 thermal technique at a molecular scale, providing a significant theoretical guide for its expanded application in EOR. • Occurrence model for remaining oil through visualization experiment and MD simulations. • The microscopic mechanism of remaining oil extraction during the application of hybrid CO 2 thermal system used. • A suitable gas-to-steam ratio for hybrid CO 2 thermal system recommended for heavy oil development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".