<i>In situ</i> surfactant generation via saponification in alkali-assisted steam flooding for heavy oil recovery
Bibliographic record
Abstract
This study explores alkali-assisted steam flooding as an effective approach for enhancing oil recovery in heavy oil reservoirs with high acid content. This method eliminates the need for costly surfactants by generating soap in situ. The research identifies naphthenic acids as the dominant acidic components in crude oil, with a double bond equivalent value of 3 or 4. Upon reacting with the sodium hydroxide (NaOH) solution, the interfacial tension decreased to ∼10−2 mN/m, and the acid–base reaction produced soap, altering the rock's wettability from oil-wet to water-wet. The emulsion viscosity was reduced with NaOH at an oil–water ratio of 3:7. Alkali-assisted steam flooding demonstrated that after the first alkali injection, the water cut was controlled, leading to a more than 10% increase in oil recovery. Alternating steam and alkali injections further improved the saponification reaction, resulting in a noticeable reduction in emulsion droplet diameter in the recovered oil samples, with most droplets concentrated in the 4–10 μm range. The shift in droplet diameter distribution indicates a transition from an initially thermally dominated emulsification process to a more stable emulsification system driven by alkali-induced interfacial activity. This study provides valuable insights into the mechanisms of alkali-assisted steam flooding for enhancing recovery in high-acid heavy oil reservoirs.
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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".