Interfacial dilatational rheology and displacement mechanism of nano‐flooding system
Bibliographic record
Abstract
Abstract In the field of crude oil extraction, nano‐flooding technology can significantly improve the oil displacement efficiency and provide important technical support for tertiary oil recovery. Although a host of studies have been carried out in this domain, its dilatational rheology and displacement mechanism are rarely reported. In order to address this gap in the extant literature, as a crucial component of this investigation, the interfacial rheology of the nano‐flooding system and the simulated crude oil was systematically studied with the aid of the JMP2000A interface expansion rheometer. Meanwhile, a micro‐displacement experiment was carried out to elucidate its displacement mechanism. The obtained results show that the nano‐flooding system initially increases as its concentration, solution pH, and aging time increase, after which a decline is observed. However, with the change of salinity, it shows a trend of increasing first and then decreasing. Moreover, according to the multi‐factor test results yielded by the relevant software, the relative error is less than 5%, which fully meets the needs of the field and provides an important basis for the field construction. The findings further suggest that the modulus between the nano‐flooding system and the simulated crude oil can be increased by 84‐fold (i.e., from 0.9271 to 78.0739 mN/m). In addition, during the displacement process, the fingering phenomenon of the displacement fluid can be reduced via piston‐like displacement. The swept volume also improves the oil washing efficiency of crude oil, demonstrating that the proposed strategy achieves the purpose of enhancing oil recovery.
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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 | 0.001 |
| 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 | 0.001 |
| 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".