Alkali‐surfactant foam improves extraction of oil from porous media
Bibliographic record
Abstract
Abstract Capillary forces result in the trapping of the oleic phase in porous media even after extensive flushing with brine. Alkali‐surfactant‐polymer formulations drastically diminish capillary forces, whereas adding polymer to the water phase increases viscous forces, resulting in highly efficient extraction of the residual oil. However, by virtue of its scale, the above process requires a large quantity of chemicals, which poses a threat to the environment. Here, we demonstrate that replacing the polymer with a gas such as nitrogen, flue gas, or carbon dioxide achieves equally superior oil extraction efficiency when using a much smaller amount of chemicals. Mobilized oil is first displaced as a continuous phase (oil‐bank) and then as an oil‐in‐water dispersion. Microflow visualization experiments reveal that dispersed oil spreads at the gas–liquid interface (surfactant solutions) due to the presence of adsorbed surfactant molecules. Our dry‐cleaning extraction of hydrocarbons has a wide spectrum of applications and is particularly useful for the production of hydrocarbons from underground formations while mitigating the impact of chemicals on the environment.
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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".