Surface-modified silica nanoparticles for enhanced oil recovery in sandstone cores
Bibliographic record
Abstract
Enhanced Oil Recovery (EOR) techniques traditionally employ chemical flooding; however, recent integration of nanoparticles (NPs) with water flooding has demonstrated promising enhancements in oil recovery.Silica (SiO 2 ) NPs have been widely used in this regard, however, the primary challenge is their tendency to aggregate in solution due to their ultra-small size and large surface area, which can inhibit their effectiveness in EOR applications.To address this, we modified the surface of commercially sourced SiO 2 NPs using two different agents: polyethylenimine (PEI) for polymer coating and Triethoxy(octyl)silane (TOS) for covalent surface functionalisation, without any binders or preliminary coatings.The modified NPs (TOS-NPs and PEI-NPs) were characterized using techniques such as SEM, TEM, FTIR, TGA, BET and DLS.Their efficacy in EOR was evaluated within sandstone cores through measurements of contact angle, interfacial tension (IFT), and spontaneous imbibition tests.Results indicated that the surface modifications substantially improved the stability of the SiO 2 NPs.Specifically, TOS-NPs effectively altered the wettability of sandstone cores from strongly oil-wet (27⁰) to strongly water-wet (165⁰), in contrast to PEI-NPs (16⁰) and unmodified SiO 2 NPs (94⁰).IFT measurements revealed a 78 % reduction with PEI-NPs, compared to only 5 % with TOS-NPs.In spontaneous imbibition tests, TOS-NPs achieved the highest oil recovery, approximately 64.9 % without noticeable core plugging, compared to 53.0 % for the unmodified NPs and 9.8 % by PEI-NPs.These results, alongside the contact angle measurements, confirm that for the sandstone wettability alteration of the rock could be the major contributing mechanism for EOR using TOS-NPs.
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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".