Role of Particle Size and Surface Acidity of Silica Gel Nanoparticles in Inhibition of Formation Damage by Asphaltene in Oil Reservoirs
Bibliographic record
Abstract
The main objective of this study is to evaluate the effect of particle size and surface acidity of synthesized silica gel nanoparticles on the inhibition of formation damage caused by asphaltene precipitation/deposition. Silica gel nanoparticles were synthesized through the sol–gel method, and their characterization was performed via N 2 physisorption at −196 °C, field emission scanning electron microscopy (FESEM), dynamic light scattering (DLS) measurements, and NH 3 temperature-programmed desorption (TPD). The size of the synthesized nanoparticles ranged from 11 to 240 nm. The ability of the nanoparticles to adsorb asphaltenes and to reduce asphaltene self-association was evaluated using batch-mode experiments. The kinetics of asphaltene aggregate growth in the presence and absence of nanoparticles were evaluated using DLS measurements in different Heptol solutions. The smallest nanoparticles (11 nm) had the highest adsorptive capacity for n -C 7 asphaltenes among the nanoparticles studied. Therefore, these nanoparticles were modified using acid, base, and neutral treatments, which showed the following order S11A ≫ S11B ≃ S11N ≃ S11 according to the n -C 7 asphaltene affinity and the reduction of its mean aggregate size in the bulk phase. The surface acidity values obtained through of temperature-programmed desorption test ranged from 1.07 and 1.32 mmol/g. In general, the asphaltene self-association was reduced to a higher degree as the amount of adsorbed asphaltene increased. Additionally, in this study, the performance of a nanofluid treatment was tested under flow conditions in porous media under typical reservoir conditions using the nanoparticles with the best performance in batch-mode experiments. Indeed, nanofluid treatment with silica nanoparticles increased the effective permeability to oil and enhanced the oil recovery with an increase in the recovery factor of 11% under the conditions reported here. This approach has the major benefit of being scalable to a producing field, and the study provides an understanding of the roles of size and surface acidity of silica nanoparticles in the wettability alteration and inhibition of formation damage caused by asphaltenes and their influences on asphaltene aggregate size in the oil matrix and the adsorbed phases.
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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".