Beneficial Advantages of Nanoparticle-Enhanced Surfactant-Assisted Low Salinity Waterflooding Process
Bibliographic record
Abstract
Abstract The application of nanoparticles (NPs) to improve oil recovery is gaining wide acceptance in the petroleum industry in recent times. Due to their size and set chemical characteristics, NPs can be used to enhance oil recovery in carbonate reservoirs by altering the rock wettability and reducing oil–brine interfacial tension (IFT). Also, when used with surface-active agents like surfactant (cationic, anionic or non–ionic) in low–salinity waterflooding (LSWF), NPs can enhance the performance of surfactant. This study focuses on the implications of combining green NPs with surfactant and low–salinity water for EOR applications in carbonate reservoirs. A NP was synthesized from a green source, rice husk, and then characterized using XRD, FTIR, TGA and SEM analyses. A cationic surfactant, Aspiro S 6420, was added in the nanoemulsion of silica nanoparticle (SNP) and 1% diluted seawater (dSW). The SNP-Surfactant-1%dSW nanoemulsion was investigated for its beneficial effects for EOR applications. Zeta potential measurements were carried out for various brine dilutions, then for 1% dSW and surfactant, and finally 1%dSW–Surfactant–SNP nanoemulsions. The measurements showed that the zeta potentials are highly positive, confirming the stability of the nanoemulsions and alteration of rock wettability. Interfacial tension (IFT) between oil and brine were measured at a temperature of 86°C. The addition of surfactant (Aspiro S 6420) led to significant drop in IFT between oil and brine. Finally, when SNPs were added to the 1%dSW–Surfactant emulsions, the IFT reduced significantly, confirming that the combination of low salinity brine–cationic surfactant (Aspiro S 6420)–SNP can be used as a promising injection fluid to recover oil from carbonate reservoirs.
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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".