Spontaneous in-situ emulsification and enhanced oil recovery using functionalised silica nanoparticles: Insights from spontaneous imbibition and micromodel flooding tests
Bibliographic record
Abstract
• Cost-effective one step modification approach for scalable EOR application. • Functionalised SiO 2 NPs with ALS/SOS surfactant enhanced wettability and reduce IFT. • Stable emulsions formed via spontaneous in-situ emulsification enhanced efficiency. • Nanofluids enabled deeper penetration and mobilisation of trapped oil. • Significant oil recovery achieved through novel nanofluid formulations. This study investigates the potential of functionalised silica nanoparticles (SiO 2 NPs) for enhanced oil recovery (EOR), employing environmentally friendly and cost-effective materials. SiO 2 NPs were optimally modified with ammonium lauryl sulfate (ALS) and sodium (C14-16) olefin sulfonate (SOS) surfactants under optimal conditions without binding agents, representing their first application in EOR. Characterisation techniques, including Fourier-transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA), confirmed the effective functionalisation of SiO 2 NPs. Transmission electron microscopy (TEM) images revealed that the morphology, structure, and shape of the NPs remained unchanged post-functionalisation, with an average diameter of 20 nm. The performance of ALS-NPs and SOS-NPs was assessed through spontaneous imbibition and microfluidic tests. ALS-NPs and SOS-NPs achieved oil recovery rates of approximately 66 % and 70 %, respectively, in spontaneous imbibition tests. Microfluidic model tests corroborated these findings, with oil recovery rates of approximately 74 % for ALS-NPs and 80 % for SOS-NPs. Both functionalised nanofluids demonstrated superior oil recovery compared to surfactants alone and non-functionalised SiO 2 NPs. The application of these nanofluids facilitated the spontaneous formation of smaller, more stable emulsion droplets, enhancing displacement efficiency and reducing pore blockages. Moreover, the nanofluids improved oil recovery by reducing interfacial tension (IFT), altering rock wettability, and forming stable oil-in-water emulsions. The grafting approach of ALS- and SOS-based nanofluids demonstrates greater efficiency by requiring lower surfactant concentrations, thereby making the process cost-effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".