In‐situ upgrading of heavy oil using nano‐catalysts: A computational fluid dynamics study of hydrogen and vacuum residue injection
Bibliographic record
Abstract
Abstract A novel nano‐catalytic in‐situ upgrading technology (ISUT) using hot fluid injection for heavy oil and bitumen production and recovery has been proposed recently (Pereira‐Almao et al., in‐situ upgrading via hot fluid injection, US Patent US20150114636A1). In this method, a vacuum distillation unit is used to separate the vacuum residue of the produced oil. The nano‐catalysts are then dispersed into the vacuum residue (VR) and are re‐injected in the reservoir, along with hydrogen. However, because of the difference in the interface forces in the residue/gas mixture, segregation of gas is possible. A practical hydrogen to VR ratio of 120 Scc/cc is employed to study the physical separation possibility within actual reservoir/well geometry. VR and hydrogen are injected at 357 and 100 °C, respectively. Computational fluid dynamics (CFD) is employed to model the VR + H2injection using the volume of fluid (VOF) method. Sensitivity analysis on reservoir pressure, mixing layout, and hydrogen solubility is also conducted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".