Zeta-Potential Investigation and Experimental Study of Nanoparticles Deposited on Rock Surface To Reduce Fines Migration
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it