Amphiphilic-Polymer-Assisted Hot Water Flooding toward Viscous Oil Mobilization
Bibliographic record
Abstract
Heavy oil recovery is the most challenging part for the petroleum industry, and several techniques including steam and water injection are currently used that are either energy intensive, expensive, or less efficient. This study aims to evaluate a novel amphiphilic viscosity reducing polymer ( DN-1 ) used along with hot water flooding for promoting viscous oil mobilization. Its performance for heavy oil mobilization was evaluated using viscosity measurements at different temperatures followed by core flooding experiments. Our results indicate that DN-1 at low concentrations could form 3D networks and stable aggregates. DN-1 featured good interfacial properties including low CMC, low IFT, and small contact angle. Even at low concentration it could form dynamic oil in water emulsions, thereby causing easy emulsification and fast demulsification. The injection pressure of DN-1 was much smaller than that of the conventional polymers or associative polymers used for heavy oil development and is beneficial for oil recovery in real reservoir scenarios where a high-pressure gradient cannot be achieved. Interestingly, unlike polymer flooding, this amphiphilic polymer increased displacing phase viscosity, while it simultaneously reduced the displaced oil viscosity. Finally, DN-1 -assisted hot water flooding could impressively achieve an ultimate recovery factor of 75.7% at 115 °C that was similar to that by hot water flooding at 175 °C. Even when DN-1 was injected at 55 °C, which was the reservoir temperature, the oil recovery factor was still 12.8% higher than hot water flooding, proving it as an efficient material in improving heavy oil recovery at low temperatures. As per our knowledge, this is the first report on the use of amphiphilic-polymer-assisted hot water flooding for heavy oil recovery.
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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".