An experimental study comparing the stability of colloidal dispersion gels with normal polymeric solutions for enhanced‐oil‐recovery purposes
Bibliographic record
Abstract
Abstract Waterflooding in oil reservoirs is associated with several drawbacks. Recently, colloidal dispersion gels (CDG) have been proposed as a new method for conformance control to address these drawbacks. CDGs are prepared by mixing crosslinkers and polymers in very low concentrations as a result of intramolecular crosslinking. In this research, a comprehensive study was conducted on the stability of sulfonated polyacrylamide (SPAM) solutions and CDGs prepared using the same SPAM in harsh conditions. The effects of high temperature, shear rate, salts, and high mechanical degrading on the viscosity of both polymeric solution types were studied using a rheometer. The results showed that, in similar conditions, CDGs are less susceptible to salts than are normal SPAM solutions. Moreover, both solutions have nearly the same sensitivity to temperature. The effect of extremely high shear rates was also studied, and the results suggested that, in similar conditions, CDG is less susceptible to high shear rates than normal SPAM solutions. Being treated at 2500 1/s for 2 hours, the CDG solution lost almost 0% of its original viscosity, while the SPAM solution lost 0.97% of its original viscosity. Given that CDG is more stable in harsh reservoir conditions, it may be a suitable substitute to the highly sensitive polymeric solutions for improving the volumetric sweep efficiency of waterfloods.
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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 | 0.001 |
| 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".