Phase Behavior of Mixtures of Bitumen, <i>n</i>-Pentane, and Water at Conditions Relevant to Solvent-Assisted Thermal Recovery
Bibliographic record
Abstract
Solvent-assisted thermal in situ heavy oil recovery processes are currently being evaluated by the oil industry as an alternative to purely thermal processes. In these processes, bitumen, solvent (typically an n -alkane), and water mix and can form multiple phases, including vapor, light hydrocarbon liquid, heavy hydrocarbon liquid, and aqueous phases. Phase behavior data are required to define the phases present in the drainage layer that flows to the producing well. In this study, phase boundaries were measured for bitumen/ n -pentane and bitumen/ n -pentane/water mixtures at temperatures from 180 to 280 °C and pressures up to 17 MPa. Saturation pressures (vapor/liquid–vapor, liquid–liquid/liquid–liquid–vapor, and aqueous-liquid/aqueous-liquid–vapor boundaries) were measured using a constant composition pressure step method. Water solubility limits (liquid/aqueous-liquid boundaries) were measured using a saturation pressure method based on the change of slope at the onset. Heavy phase onsets (liquid/liquid–liquid boundaries) were measured using two methods: a light phase method based on the change in asphaltene content at the onset, and a titration performed on a high pressure microscope. The onset was assessed in terms of the n -pentane content in the bitumen. The addition of n -pentane to mixtures of bitumen and water was found to increase the saturation pressure by up to 4 MPa and increase the mass-based water solubility limit by up to 2.5 wt %. The addition of water to mixtures of bitumen and n -pentane was found to increase the saturation pressure by up to 8 MPa and decrease the heavy phase onset by up to 2.0 wt %. The mixtures were found to be supercritical above the critical point of n -pentane with critical pressure depending on the n -pentane content of the mixture. The Advanced Peng–Robinson equation of state could be tuned to match the phase boundaries in the subcritical region but was inaccurate or failed to converge near and in the supercritical region.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".