On the evaluation of Alkaline‐Surfactant‐Polymer flooding in a field scale: Screening, modelling, and optimization
Bibliographic record
Abstract
Abstract Chemical enhanced oil recovery methods, including alkaline surfactant polymer (ASP) flooding, have found special significance for oil reservoirs. ASP flooding provides different situations based on ratio of injection of three ASP components. This study identifies the behaviour of injection components and their interaction, investigating the synergy between them. To this end, an Iranian oil reservoir is studied. The factors influencing the injection process are identified and then a combination of these factors and the concentration of ASP components are modelled. All modelling results are fitted with R‐squared and adjusted R‐squared values above 0.96. Finally, the optimization is conducted to determine the best injection scenario. The most important aspect of this study is to investigate simultaneously the effect of different parameters of ASP in a real case. The screening results show that the effect of polymer on viscosity is the most influential factor in ASP flooding. The modelling results show that water cut in the case of simultaneous injection of three components is less than other cases of injection. This represents the synergy of components in the injection process during ASP flooding. The miscibility between water and polymer solution also leads to less produced water and more produced oil. In the final stage, the optimization results show that the optimal scenario is injection of surfactant and polymer with the greatest amount of miscibility between water and polymer solution. It can be concluded that with this approach, ASP injection can efficiently be analyzed and optimized from a technical point of view.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".