Investigation of alkali and salt resistant copolymer of acrylic acid and <i>N</i> ‐vinyl‐2‐pyrrolidinone for medium viscosity oil recovery
Bibliographic record
Abstract
Abstract Heavy oil reservoirs, unsuited for thermal applications, are being exploited using chemical enhanced oil recovery (CEOR) techniques. The most widely used polymer in CEOR applications is hydrolyzed polyacrylamide (HPAM). However, it hydrolyzes very rapidly under alkaline conditions, making it susceptible for alkaline polymer flooding, the main variant of chemical EOR techniques. To overcome this shortfall of conventional HPAM, a copolymer P(AA‐ co ‐VP) of acrylic acid (AA) and N ‐vinyl‐2‐pyrrolidinone (NVP) was synthesized, that can offer stability and positive synergism against alkali. In the research presented herein, rheological properties of HPAM and P(AA‐ co ‐VP) were compared in terms of viscosity and elasticity for typical alkali‐polymer (AP) flood operations. The core flooding experiments were conducted using the heavy oil samples collected from a reservoir in Alberta. The shear rheological and dynamic viscoelastic properties of P(AA‐ co ‐VP) copolymer improved in presence of strong alkali while the conventional HPAM showed much higher viscosity loss, becoming less effective for AP heavy oil recovery operations. In the presence of alkali, 45.9% and 47.3% incremental recovery factor are shown by HPAM and the newly synthesized P(AA‐ co ‐VP) copolymer. Although the incremental recovery factor shown by newly synthesized polymer is slightly higher, it resulted in a three times lower residual resistance factor than HPAM. Lower residual resistance factor is important for ensuring good transport properties during polymer flooding. AP flooding conducted using P(AA‐ co ‐VP) copolymer could effectively overcome the drawbacks of conventional HPAM polymer, thereby improving the heavy oil recovery and transport in porous media.
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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) | 0.000 | 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".