Application and Optimization of Rheological Mathematical Models in Preparation and Injection Process of Polymer Flooding
Bibliographic record
Abstract
The impact of shear on the properties of polymer solutions is not fully accounted for in the simulation of conventional rheological models. To optimize the application of these mathematical models, the shear rheological characteristics of polyacrylamide at various concentrations were investigated under different shear rates and shear modes. The results indicate that pseudoplastic fluid polymers exhibit two distinct rheological characteristics within their "shear thinning" rate range. The critical shear rate at this threshold signifies the point at which shear stress begins to disrupt the structure of the polymer solution, resulting in a rheological curve that transitions from high to low shear rates without reverting back to a low-to-high state. The concentration of the solution has minimal effect on the critical shear rate of the polymer, which is primarily determined by the inherent properties of the polymer itself. The application of rheological models during preparation and injection processes can elucidate the effects of shear on polymers by analyzing changes in apparent viscosity. Furthermore, the rheological model following supercritical shear rates requires modifications to the consistency coefficient (K) and flow index (n) based on shear rheological data obtained from high to low shear rates.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".