The effect of inorganic solids on emulsion layer growth in asphaltene‐stabilized water‐in‐oil emulsions
Bibliographic record
Abstract
Oilfield emulsions are often stabilized by asphaltenes but inorganic solids can impact both emulsion stability and rag layer accumulation. In this study, the effect of inorganic solids on emulsion stability and emulsion layer growth was investigated using batch and continuous separations performed on water‐in‐oil emulsions stabilized by asphaltenes. The emulsions were prepared at 60 °C from an organic phase consisting of solids, asphaltenes, n‐heptane, and toluene and an initial water phase volume of 0.50. Three types of coarse solids were considered: 12 μm kaolin, 18–32 μm silica, and 32–63 μm silica with wettabilities ranging from 50 to 125°. In batch experiments, the coalescence rate was determined from the change in height of the free water and oil layers over time as the emulsion coalesced. In continuous experiments, emulsion layer growth was measured as the emulsion was continuously fed into a vertical separator. The data were modelled with a material balance that included a coalescence rate equation. In the absence of solids, the continuous emulsion layer growth rate and ultimate stability correlated well to batch coalescence parameters. The addition of the coarse solids at concentrations below 5 g/L accelerated coalescence rates. Above 5 g/L, the solids increased emulsion stability indicating that they form a steric barrier between the droplets. Even if the feed is below this threshold, the solids accumulate in the rag layer until the threshold is reached, the emulsion becomes stable, and the performance of the continuous separation can no longer be predicted from batch tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".