Silica Nanoparticle Enhancement in the Efficiency of Surfactant Flooding of Heavy Oil in a Glass Micromodel
Bibliographic record
Abstract
The synergistic effects of fumed-Si nanoparticles (Si-NPs) in combination with sodium dodecyl sulfate (SDS) surfactant as suitable agents for oil displacing in enhanced oil recovery (EOR) are evaluated using a 5-spot glass micromodel. Optimum oil recovery (45%) is obtained for SDS near the critical micelle concentration; however, the addition of fumed silica nanoparticles (Si-NPs) enables a further 13% enhancement in oil recovery for the maximum concentration of the SDS/Si-NPs (2.2 wt %) as well as delaying the breakthrough point. The optimum mass ratio of SDS:Si-NP (1:11) suggests that the Si-NPs are aggregated by the SDS micelles, consistent with increased viscosity upon addition of Si-NPs. The presence of the Si-NPs also greatly increases the wettability on the glass surface with a decrease in the contact angle from 73° for SDS (1800 ppm) to 11° for SDS/Si-NPs (1800 ppm/2.0 wt %). The effective changes in the oil sweeping mechanism are directly observed in the glass micromodel and correlate to these physical measurements. The results demonstrated that addition of Si-NPs to SDS solutions made a significant improvement to oil recovery values and is potentially beneficial in EOR applications.
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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) | 0.001 | 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".