Properties of Polymer Solution and Gel with Different Electrolytes
Bibliographic record
Abstract
Abstract The polymer clews and flow performance under the effect of electrolytes have been studied. Laboratory experiments were used to determine macroscopic pressure and flow resistance. Optical microscope was used to examine dimensions and shapes of polymer clews. Experimental results showed that polymer clews and flow resistance force were influenced by the type and concentration of electrolytes. The curling of polymer clews had different optimal concentration scope for different types of electrolytes. Molecular clews became smaller when the concentration of electrolytes was higher under certain concentration scope, and became larger when the concentration of electrolytes exceeded the scope. And the effect of Ca2+ to polymer clews was bigger than Mg2+,both were bigger than that of K+ and Na+. With coagulant it was more likely to form intra-molecular cross-linked reaction when the concentration of electrolytes was higher, which provided a higher flow resistance force and helped to attain a high recovery. When the succeeding water salinity was lower, the polymer Clews would swell, and pressure and resistance force would be further enhanced. Mechanisms of polymer clews curling and stretching under the influence of electrolyte and coagulant have been analyzed by Stern- Grahame Double-Layer Model and catching and re-catching theory in stratum through coordinating microscopic and macroscopical experiment. The polymer clews would curl and stretch because of the compression of the double layer by ion binding of electrolytes. When succeeding water was diluted, the decrease of electrolyte concentration resulted in the swelling of clews of polymer molecules, re-catching process took place, thus flow resistance in porous media increased. The objectives of this study was to perform in-depth profile modification by changing molecular clews shape and dimensions, as well as to recycle the produced water in the oilfield for improved oil production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".