Investigation of silica nanoparticles grafted with sulphonated polymer for enhanced oil recovery at high temperature and high salt
Bibliographic record
Abstract
Abstract Nano‐fluids' application for enhanced oil recovery (EOR) has attracted noticeable attention and formed a new research area in recent years. Currently, the greatest challenge in this area is to formulate stable nano‐fluids for oil reservoirs with high temperatures and salinity. To overcome the limitations of its application in high‐temperature drilling, polymer‐coated nanoparticles (SiO 2 ‐PAMPS NPs) were prepared via solution polymerization of 2‐acrylamide‐2‐methyl‐1‐propane sulphonic acid (AMPS) from the surface of aminopropyl‐functionalized silica nanoparticles. The SiO 2 ‐PAMPS NPs were characterized by Fourier‐transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and dynamic light scattering (DLS). The results indicated that the AMPS was successfully grafted onto the surface of silica nanoparticles, and the average diameter of SiO 2 ‐PAMPS NPs was about 16 nm. The nano‐fluids showed noticeable stability in American Petroleum Institute (API) brine (2 wt.% CaCl 2 and 8 wt.% NaCl) at 90°C beyond 46 days. When amphipathic nanoparticles were introduced to brine at 90°C, the potential of the nano‐fluids in recovering oil was evaluated by investigating the interfacial tension with kerosene oil and the oil contact angle in the nano‐fluids. The contact angle of the glass sheet surface before treatment was about 144°, while after SiO 2 ‐PAMPS NPs treatment for 72 h, it became about 92°. Meanwhile, the nano‐fluids showed an excellent enhancing emulsibility property, which plays a vital role in promoting the development of EOR in high‐temperature and high‐salt environments.
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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) | 0.000 | 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 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".