Regular solution theory applied to asphaltene related phase behaviour
Bibliographic record
Abstract
Abstract Asphaltenes are the least soluble fraction of crude oil and they can phase separate from the oil upon a change in temperature, pressure, or composition. This phase separation, often described as precipitation, may be beneficial (eg, in a partial deasphalting process) or detrimental (eg, in pipeline and surface equipment fouling). A phase behaviour model that can predict the onset and amount of asphaltene precipitation and is compatible with process simulators is desirable for the design and operation of these processes. This brief review focuses on the regular solution modelling approach. The regular solution model is based on activity coefficients and therefore is well suited for liquid‐liquid phase separations such as asphaltene precipitation. The model and its internal correlations are presented and its performance on asphaltene phase separation from mixtures of heavy oils and solvent is demonstrated. Recent updates to this approach are presented and potential future applications are discussed. The strengths and limitations of this approach for oilfield applications are highlighted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".