Zeta-Potential Investigation and Experimental Study of Nanoparticles Deposited on Rock Surface To Reduce Fines Migration
Bibliographic record
Abstract
Summary Fines migration is a noticeable problem in petroleum-production engineering. Plugging of throats in porous media occurs because of detachment of fine particles from sand surfaces. Thus, the study of interactions between fines and pore surfaces and the investigation of governing forces are important factors to consider when describing the mechanism of the fines-release process. The main types of these forces are electric double-layer repulsion (DLR) and London–van der Waals attraction (LVA). It may be possible to alter these forces with nanoparticles (NPs) as surface coatings. In comparison with repulsion forces, NPs increase the effect of attraction forces. In this paper, we present new experiments and simple modeling to observe such properties of NPs. For this purpose, the surfaces of pores were coated with different types of NPs: magnesium oxide (MgO), silicon dioxide (SiO2), and aluminum oxide (Al2O3). A zeta-potential test was used to examine changes in the potential of the pore surfaces. Total interaction energy was then mathematically calculated to compare different states. Total interaction energy is a fitting criterion that gives proper information about the effect of different NPs on surface properties. Consequently, total interaction plots are found to be suitable tools for selecting the best coating material. On the basis of experimental results, the magnitude of change in zeta potential for the MgO NP was 45 mV. Our model demonstrated that the magnitude of the electric DLR in comparison with the LVA of the probe and plate surface was considerably diminished when MgO NPs were used to coat the surface of the plate, which agrees completely with our experimental observation.
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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".