Three-Phase Equilibrium Study for Heavy Oil-Solvent-Steam System at High Temperatures
Bibliographic record
Abstract
Abstract Reservoir simulation and research activities of heavy oil production processes such as SAGD, VAPEX or the steam-solvent combined hybrid process require accurate values of equilibrium constants (K-values) at elevated temperatures. However, such data is rarely available in open literature either because of the temperature limitation of PVT equipment or difficulties in performing high temperature three phase vapor liquid equilibrium experiments. In this paper, with the assistance of a state-of-the-art fully visual PVT cell specifically developed for heavy viscous oils and capable of performing fluid phase behavior studies at temperatures up to 250°C and effectively mix samples with viscosity up to 1,000 cP at test temperature, a heavy oil-solvent-steam equilibrium case study is presented. The heavy oil was mixed with solvent and water at specified weight ratio, temperature and pressure in the heavy oil PVT cell. After achieving equilibrium, the volumes of vapor, liquid and water phases were measured. The samples from each phase were drawn for compositional analysis using gas chromatography and K-values of the hydrocarbon components were examined. The component compositions from this experiment were utilized for equation of state (EoS) model turning. This case study shows that with proper care, the EoS can be characterized and used to match with experimental data for heavy oil.
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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) | 0.002 | 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".