A high temperature resistance and hyper‐dispersed nanoparticle grafted by multi‐monomer via in‐situ polymerization: Preparation, characterization, and its imbibition performance
Bibliographic record
Abstract
Abstract In recent years, the excellent cleaning‐oil capacity of nanofluid has attracted extensive attention and it has been widely used in oilfields as an imbibition agent. To solve the poor dispersion stability of nanoparticles in high‐temperature and high‐salt reservoir, this paper has prepared a hyper‐dispersed nanoparticle, polymer grafted nanosilica (PNS) that is anti‐salt monomer and anti‐temperature monomer simultaneously grafting on the surface of nanosilica via in‐situ polymerization reaction. Its imbibition performance has been discussed. Results show that (1) The two monomers are all successfully grafted on the surface of nanosilica via optimization of in‐situ polymerization reaction conditions. This hyper‐dispersed nanoparticle has 14.5% final grafting rate, approximately 18 nm in particle size, and −41.2 mV of zeta potential; (2) PNS fluid has excellent dispersion stability and its grain size has no obvious change in 20 wt.% of NaCl or 4 wt.% of CaCl 2 fluids at room temperature (25°C), as well as in 18 wt.% of NaCl or 4 wt.% of CaCl 2 fluids at 130°C after 1 week; (3) Crude oil–water interface tension can be decreased and the hydrophilicity is significantly boosted by this PNS fluid. Meanwhile, the 10 −3 mN/m of interface tension can be obtained and the contact angle of water phase is decreased to 12.8° by surfactant erucamide propyl hydroxysulfobetaine (EHSB) synergizing with PNS fluid; (4) Oil recovery efficiency replaced by PNS in surfactant fluid has reached 39.3%. That is mainly because the hyper‐dispersed nanoparticle provides a stronger spreading force to enhance the efficiency of cleaning oil.
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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".