Simultaneous/sequential alkaline‐surfactant‐polymer flooding in fractured/non‐fractured carbonate reservoirs
Bibliographic record
Abstract
Alkaline‐Surfactant‐Polymer flooding is one of the most novel chemical enhanced oil recovery methods in the petroleum industry. This method has attracted interest due to its remarkable advantages. In this work, a series of ASP floods are conducted on fractured/non‐fractured carbonate rocks. The performance of the tests was investigated by various ASP floods consisting of five types of polymers, two surfactants and one common alkaline. ASP was flooded simultaneously and sequentially in four defined scenarios after water flooding. The results showed that although using hydrolysed polymers increases the recovery factor in the fractured medium, sulfonated polymers increase oil recovery even more due to their capability to increase viscosity in saline solutions. Also, since polymers with a higher molecular weight have a larger hydraulic diameter, they increase oil recovery considerably. Moreover, using cationic surfactant improves the efficiency of ASP flooding due to the serious reduction of interfacial tension (IFT) in comparison to using the anionic one. In addition, according to the four scenarios' results, pushing ASP by polymer is much more effective than pushing it with brine. Also, the difference in final oil recovery between simultaneous and sequential ASP floods is more remarkable when ASP is pushed by brine. Thus, choosing a process completely depends on the slug type and the injected volume.
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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".