Prediction of solubility of CH<sub>4</sub>, C<sub>2</sub>H<sub>6</sub>, CO<sub>2</sub>, N<sub>2</sub> and CO in bitumen
Bibliographic record
Abstract
Abstract An accurate model to calculate the solubility of light hydrocarbons (methane, CH4 and ethane, C2H6) and non‐hydrocarbon gases (carbon dioxide, CO2, nitrogen, N2 and carbon monoxide, CO) in bitumen is required for the optimal design of bitumen and heavy oil recovery processes and transportation. In this work, we used the Krichevsky–Ilinskaya equation to predict the solubility of light solvents (CH4, C2H6, CO2, N2 and CO) in bitumen from five reservoirs in Alberta, Canada. The Peng–Robinson (PR) equation of state (EoS) is used to treat the gas phase. The proposed model is then verified using available experimental solubility data of light solvents in bitumen, and good agreement is observed. The experimental data cover wide ranges of pressures and temperatures. The results show that the proposed model represents the available solubility data of light hydrocarbons (CH4 and C2H6) and non‐hydrocarbon solvents (CO2, N2 and CO) in bitumen with absolute average relative deviations of <4.2% and 5.4%, respectively. These results can be applied to heavy oil and bitumen recovery processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".