Application of <scp>MgO</scp> based‐nanofluid for controlling the growth of asphaltene flocs under static and micromodel dynamic conditions
Bibliographic record
Abstract
Abstract In this study, the impact of magnesium oxide (MgO) nanoparticles on the control of asphaltene aggregates growth was examined. The investigation began with static testing, followed by dynamic testing, where nanofluid was injected into a constructed glass micromodel simulating a porous medium. The results obtained from light microscopy and asphaltene dispersant tests demonstrated that the MgO nanoparticles with an average diameter of 50 nm postpone the asphaltene onset point (AOP) and delay the growth of asphaltene aggregates in crude oil. Also, the results obtained from these experiments illustrated the performance of synthesized nanoparticles in various concentrations on inhibition of asphaltene deposit in the crude oil medium, in the order of 750 > 1500 > 100 > 1000 > 500 ppm. The results from both microscopy and ADT experiments strongly validate the effectiveness of MgO nanoparticles across varying concentrations, highlighting the optimal dosage of 750 ppm. Images of nanofluid flooding at the optimal concentration in the glass micromodel demonstrate effective nanoparticle inhibition and enhanced oil recovery from the porous medium. These findings corroborate the results obtained from ADT and microscopy tests. The results of FT‐IR analysis show the adsorption of asphaltene particles on the surfaces of MgO nanoparticles in wavelengths of 2900–3000 cm −1 . Moreover, dynamic light scattering (DLS) analysis results indicated that the average diameter of suspended particles was 3580 nm before adsorption and 6230 nm after adsorption, indicating the controlled adsorption of asphaltene onto the surface of MgO nanoparticles. The findings from this study can be applied to manage asphaltene formation across all stages of oil processing and production.
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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) | 0.000 | 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 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".