Fly Ash Nanoparticle-Stabilized Emulsions for Improve Mobility Control Application
Bibliographic record
Abstract
Abstract Nanoparticles have demonstrated their capacity to increase emulsion stability by forming what is known as a Pickering emulsion, which is predicted to improve EOR processes by improving conformity control. The goal of this work is to develop a novel way of beneficially utilizing the main waste product from coal power-generation plants - fly ash - by generating fly ash nanoparticle-stabilized emulsions for improved mobility control, especially under high-salinity conditions. First, the ball-milling method was used to decrease the grain size of fly ash, which was too big for injection into reservoirs. Second, fly ash nanoparticles were used to measure the synergy between nanoparticles and surfactants in the creation of oil-in-brine emulsions. Third, the emulsion stability was tested using a microscope and a rheometer with three different surfactants (cationic, nonionic, and anionic). Finally, oil replacement experiments were conducted using intra-formation heterogeneous cores to investigate the recovery enhancement effect of in situ injection of fly ash nanoparticles and cationic surfactant (CS). Thermally treated fly ash (TTFA) nanoparticles with an average size of 150 nm were produced using nano-milling and thermal treatment techniques. The use of either a cationic or nonionic surfactant in conjunction with nanoparticles resulted in strong and stable emulsions. The cationic surfactant had the greatest synergy, while the anionic surfactant had the least, indicating that electrostatic interactions with the surfactant and the liquid/liquid interface were key factors. The in-situ emulsion formed by the fly ash nanoparticles and the cationic surfactant (FA-CS) produced an additional 8.5 % of the original oil in place (OOIP) recovery after waterflooding. This indicates that the emulsion has better mobility control performance and higher crude oil recovery. This study not only has the potential to minimize the amount of surfactant used for emulsion-based EOR mobility control of fly ash nanoparticles but also to sequester fly ash in the subsurface strata.
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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".