Insights into Foamy Oil Phenomenon in Porous Media: Experimental and Numerical Investigation
Bibliographic record
Abstract
Cyclic Solvent Injection (CSI) is a method for enhanced heavy oil recovery, offering a reduced environmental impact. CSI processes typically involve fluid flow through both wormholes and the surrounding porous media in reservoirs. Therefore, understanding how foamy oil behavior differs between bulk phases and porous media is crucial for optimizing CSI operations. However, despite CSI’s advantages, limited research has explained why foamy oil, a key mechanism in CSI, displays weaker strength and stability in bulk phases than in porous media. To address this gap, three advanced visual micromodels were employed to monitor bubble behavior from nucleation through collapse under varying porosity with a constant pressure reduction. A sandpack depletion test in a large cylindrical model further validated the non-equilibrium bubble-reaction kinetics observed in the micromodels. Experiments showed that, under equivalent operating conditions, bubble nucleation in porous media required less energy and initiated more rapidly than in a bulk phase. Micromodels with lower porosity demonstrated up to a 2.5-fold increase in foamy oil volume expansion and higher bubble stability. Moreover, oil production in the sandpack declined sharply at pressures below 1800 kPa, indicating the onset of critical gas saturation, and yielded a maximum recovery of 37% of the original oil in place. These findings suggest that maintaining reservoir pressure above critical gas saturation pressure enhances oil recovery performance during CSI operations.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".