Viscosity Estimation of Liquid Hydrocarbon Mixtures: An Application to Solvent-Based Enhanced Oil Recovery
Bibliographic record
Abstract
Abstract Various mixing rules exist to estimate the viscosity of liquid hydrocarbon mixtures given the viscosities of the individual components. These models generally work well for mixtures of components of similar viscosity but can produce quite inaccurate estimates for mixtures of light and heavy components. Enhanced oil recovery processes for heavy-oil reservoirs utilize light hydrocarbon solvents as an injectant to enhance the oil recovery. The mixture viscosity is a fundamental parameter for modelling solvent-based recovery techniques using reservoir simulation. In the present work, a large database of liquid mixture viscosity literature data has been constructed and used to test a diverse set of mixing rules. In addition to the general formulation, the impact of the component basis (i.e. the use of mole, mass, or volume fractions) was examined. The database includes a wide range of mixtures, which range in viscosity from 0.1 to about 100,000 cp. A power law mixing rule using pure component volume fractions calculated at standard conditions is shown to be, in general, very effective. Additionally, it was found that a modified Arrhenius model could provide similar accuracy as the proposed formulation but is somewhat more complicated apply.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".