Effects of nanosilica on fine migration and location distribution of blockage at different <scp>pH</scp> and temperatures: Modelling and experimental studies
Bibliographic record
Abstract
Abstract There are a lot of fine particles in sandstone reservoirs that are not strongly bonded to the rock's surface. These fine particles move with the fluid flow and cause formation damage and facilities' erosion. An effective method for preventing fine migration is using nanoparticles (NPs). In this study, modelling and experimental methods were utilized to investigate NPs' effect on fine migration. The Derjaguin–Landau–Verwey–Overbeek (DLVO) theory was used to model NPs' effect on the stability of fines on the rock's surface. Long sand packs were utilized to study the silicon dioxide NPs' effect on fine migration and location distribution of blockage in experiments at different pH and temperatures. Modelling results showed that the presence of NPs reduces the zeta potential of fine surfaces from −27.6 and −35.6 mV to −1.8 and −6.7 mV at pH = 8 and pH = 12, respectively. Based on the DLVO theory, it was concluded that NPs increase the particles' stability on the rock's surface. The experimental results showed that although fine migration increases with an increase in pH and temperature, the presence of SiO 2 decreases it by 58% and 38% at pH = 8 and 12, respectively. The results showed that the performance of NPs reduced with an increase in pH and temperature. It was shown that the presence of NPs influences the location distribution of pressure drop and leads to permeability being more homogeneous in porous media during low salinity water injection.
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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 | 0.001 |
| 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 | 0.001 | 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".