Enhancing the Performance of HPAM Polymer Flooding Using Nano CuO/Nanoclay Blend
Bibliographic record
Abstract
A single polymer flooding is a widely employed enhanced oil recovery method, despite polymer vulnerability to shear and thermal degradation. Nanohybrids, on the other hand, resist degradation and maintain superior rheological properties at different shear rates. In this article, the effect of coupling CuO nanoparticles (NPs) and nanoclay with partially hydrolyzed polyacrylamide (HPAM) polymer solution on the rheological properties and the recovery factor of the nanohybrid fluid was assessed. The results confirmed that the NP agents preserved the polymer chains from degradation under mechanical, chemical (i.e., salinity), and thermal stresses and maintained good extent of entanglement among the polymer chains, leading to a strong viscoelastic attribute, in addition to the pseudoplastic behavior. The NP additives increased the viscosity of the HPAM polymer at shear rates varying from 10–100 s−1. The rheological properties of the nanohybrid systems varied with the NP additive content, which in turn provided a window for engineering a nanohybrid system with a proper mobility ratio and scaling coefficient, while avoiding injectivity issues. Sandpack flooding tests confirmed the superior performance of the optimized nanohybrid system and showed a 39% improvement in the recovery ratio relative to the HPAM polymer injection.
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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".