Challenges for Extending the Application of Nanoparticles in High Salinity Reservoirs
Bibliographic record
Abstract
Abstract Nanoparticles, through many researches, has proven its capability to be an enhance oil recovery agent. In this study, we aim to investigate the performance of natural Zeolite in nanoscale on the recovery of crude oil compared to the normal water flooding method. The natural Zeolite nanoparticles are dispersed in seawater, however nanoparticles stability in saline water have been reported to be a challenge. Therefore and in order to investigate the performance of these natural Zeolite nanoparticles on oil recovery, we first stabilize them in seawater. Natural Zeolite nanoparticles of different concentrations (i.e. 0.02, 0.03, 0.05 wt%) were dispersed in seawater, where stability tests showed nanoparticles precipitations in less than an hour. This problem, of nanoparticles precipitation in seawater, was investigated by studying the performance of the Zeolite nanoparticles in each electrolyte that exits in seawater. The study results showed good stability of the Zeolite nanoparticles in NaCl solution that has a concentration of 0.14 wt%, however Zeolite nanoparticles will destabilize at higher concentrations of NaCl. Divalent salts that exist in seawater (i.e. MgCl2 and CaCl2) were also tested. The Zeolite nanoparticles were found to be destabilized even at very low concentrations of these salts. In the current work, we added surfactants to seawater to help in stabilizing the Zeolite nanoparticles. After initial screening of several surfactants, Polyvinylpyrrolidone (PVP) showed to be the best candidate to stabilize Zeolite nanoparticles in seawater. Experiments were then carried out using Zeolite nanoparticles plus (PVP) all dispersed in seawater. The effect of this dispersant on interfacial tension (IFT) was investigated where results revealed decrease in IFT values. The dispersant was shown also to change the wettability to more water wet condition which was due to the Zeolite nanoparticles, as a dispersant of only seawater and (PVP) was tested and found to not alter the wettability.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".