Uso de nanoparticulas de silice para la estabilizacion de finos en lechos empacados de arena Ottawa
Bibliographic record
Abstract
Las particulas finas debilmente cementadas a la matriz porosa pueden ser liberadas y movilizadas causando reducciones en la porosidad y permeabilidad de un yacimiento y disminuyendo el recobro de petroleo Con el fin de determinar el dano de formacion por migracion de finos y dar una posible solucion a este problema se desarrollo un sistema de adsorcion en lechos empacados en los cuales se simulo experimentalmente la estabilizacion de los finos mediante el uso de nanoparticulas Los lechos adsorbentes usados fueron preparados con arena Ottawa y esferas de vidrio radio promedio de 053 mm Se usaron tres lechos de arena uno sometido a un proceso de lavado lecho humectable al agua otro sometido a un proceso de dano usando un crudo colombiano extrapesado lecho humectable al aceite y un ultimo compuesto de arena tratada con nanoparticulas de silice 515 nm Con las esferas de vidrio se prepararon dos lechos uno con las esferas lavadas y otro con las esferas impregnadas con nanoparticulas La suspension de finos se preparo con nanoparticulas de alumina 50 nm y agua destilada Se observo que los lechos tratados con nanoparticulas seguian los patrones idealizados de las curvas de ruptura indicando que las nanoparticulas de silice inhiben la migracion de finos debido a su alta capacidad adsortiva
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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".