Improving the Thermal Stability of Hydrophobic Associative Polymer Aqueous Solution Using a “Triple-Protection” Strategy
Bibliographic record
Abstract
Because of their high viscoelasticity, Hydrophobic Associative Water-Soluble Polymers (HAWSPs) have been widely used in many industrial fields, especially in oilfield flooding and fracturing. However, one major problem which limits the wide applications of HAWSPs is their weak resistance to high temperatures. Once the temperature increases over 100 °C, the viscosity of the fracturing fluid decreases rapidly, because high temperatures reduce fluid viscosity by oxidizing the polyacrylamide chains and weakening the association of hydrophobic groups. To improve the high temperature resistance of one HAWSP, a triple-protection strategy was developed. First, rigid N-vinyl-2-pyrrolidone moiety was introduced into the polymer chains. Second, an environmentally-friendly deoxidizer, carbohydrazide, was selected to prevent polymer oxidization by scavenging dissolved oxygen. Results showed that both the rigid groups and the deoxidizer improved the temperature resistance of the polymer and helped it maintain high viscosity under high temperature and shear rate. Using these two protection strategies, the resistant temperature of the polymer could reach 160 °C. However, the polymer network still got severely damaged at further elevated temperatures. Therefore, as the third protection strategy, the pre-added high temperature responsive crosslinking agent was applied to form new networks at elevated temperatures. The results have shown that the optimized polymer solution as a kind of fracturing fluid showed good temperature resistance up to 200 °C.
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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".