Design of Tailor‐Made Water‐Soluble Copolymers for Enhanced Oil Recovery Polymer Flooding Applications
Bibliographic record
Abstract
The goal in this study is to shed light on the ambiguous procedure of making AAm/AAc copolymers for polymer flooding applications for enhanced oil recovery (EOR). Despite the extensive use of these copolymers in polymer flooding, a well‐established recipe for the required desirable properties of AAm/AAc copolymers does not exist in the literature. Therefore, the knowledge from copolymerization kinetics and copolymer structure/property relationships is implemented to tailor‐make copolymers for polymer flooding. The detailed knowledge of the copolymerization kinetics enables to design copolymers with desirable properties, such as high molecular weight, high AAm content in the copolymer, and random distribution of anionic charges along the copolymer chain. Moreover, rheological properties show that copolymers with higher AAc content in the copolymer have higher solution viscosity and elasticity, both of which are desirable properties. In general, the main factors that should be considered when designing a polymer for polymer flooding for EOR applications are described. image
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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".