Impact of aluminium acetate particles size on the gelation kinetics of polyacrylamide‐based gels: Rheological and molecular simulation study
Bibliographic record
Abstract
Abstract Inorganically crosslinked polymers have been intensively implemented for conformance control treatments in oil and gas wells at low temperatures (<90°C) because of their faster gelation time compared to organically crosslinked gels. Crosslinkers such as chromium acetate are known to be toxic, and aluminium‐based alternatives have been introduced. This study aims to investigate the impact of aluminium acetate (AlAc) particle size on the gelation kinetics of polyacrylamide (PAM)‐based gels at a pH of 5 and temperature of 75°C. Moreover, bentonite is used as an additive to delay the crosslinking of PAM/AlAc gels. Reducing the particle size increases the specific surface area of the particles and provides more crosslinking sites. Therefore, lower PAM concentrations (up to 5 wt.%) could be used without AlAc settling. Using 7 wt.% PAM/1 wt.% AlAc with sizes of 25 and 48 μm revealed a crosslinking time of 17 and 115 min, respectively. The addition of bentonite at low crosslinker concentrations (0.5–1 wt.%) did not decrease the gel strength of 7 wt.% PAM/1 wt.% AlAc significantly. The gelation time was extended after adding 1 wt.% bentonite to the formulation where the delay was attributed to the adsorption of AlAc on the bentonite surface that was illustrated by molecular simulation.
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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".