Effect of the Surfactant on Asphaltene Deposition on Stainless-Steel and Glass Surfaces
Bibliographic record
Abstract
Surfactant dispersants have been introduced as a proper candidate to mitigate the problems caused by asphaltene precipitation, such as clogging wells, flowlines, and surface facilities in oil industry. In this work, we study the effects of dodecylbenzenesulfonic acid (DBSA) as a surfactant on asphaltene deposition on stainless-steel and glass surfaces. Experiments were conducted to measure asphaltene precipitation in the bulk system and asphaltene deposition on the stainless-steel and glass surfaces. Results revealed that the surfactant delays the asphaltene onset in the bulk system. However, asphaltene deposition on the stainless-steel surface was increased at all measured concentrations of the surfactant, while the deposition rate on the glass surface decreased by increasing the surfactant concentration. Affinity of the surfactant molecules to the stainless-steel surface was verified in asphaltene deposition and removal tests. The results revealed that the DBSA surfactant is able to remove deposited asphaltene on glass surfaces at high concentrations. This study reveals the importance of surface properties when the surfactant is used as an asphaltene dispersant.
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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".