Evaluating the performance of designed terpolymers for polymer flooding
Bibliographic record
Abstract
Abstract In this work, polymeric materials designed for enhanced oil recovery (EOR) were evaluated for their intended application. Properties including viscosity, flow through porous media (resistance factor and residual resistance factor), and heavy oil displacement (incremental oil recovery) were assessed for designed terpolymers of 2‐acrylamido‐2‐methylpropane sulphonic acid (AMPS), acrylamide (AAm), and acrylic acid (AAc). The same properties were evaluated for two commercially available reference materials (e.g., partially hydrolyzed polyacrylamides or HPAM) with similar characteristics, which allowed for direct comparison between the newly designed terpolymers and materials that are currently on the market for the polymer flooding application. The incremental oil recovery directly associated with polymer flooding, which includes both the polymer flooding and post‐polymer waterflooding stages (excluding the initial waterflooding injection (or secondary) oil recovery), demonstrates that the designed terpolymers provided a higher incremental recovery (42% and 58%) than the reference materials (33% and 46%). Therefore, the terpolymers provided a higher contribution to incremental (or enhanced) oil recovery than the typical HPAM. Additionally, both designed terpolymers showed better injectivity in unconsolidated porous media and are less likely to cause plugging than the commercially available reference materials. Therefore, using a targeted design approach ultimately led to polymeric materials with excellent performance for EOR polymer flooding applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".