Flow of nanodispersed catalyst particles through porous media: Effect of permeability and temperature
Bibliographic record
Abstract
Abstract The proposed in situ catalytic upgrading of heavy oil to achieve an environmentally sustainable method for heavy oil recovery requires the placement of nanodispersed catalyst particles deep into the formation where it can accelerate the high‐temperature upgrading reactions. In continuation of the previous work [Zamani et al., Energy Fuels 24, 4980‐4988 (2010)], this paper presents results of several new experiments carried out to examine the effects of other parameters, including the connate brine salinity, absolute permeability, sand‐bed temperature and particle concentration on the propagation of nanoparticles in porous media. The results show that lower permeability, increased operating temperature and higher particle concentration did not significantly affect the propagation of nanodispersed catalyst suspension through the sand‐bed. Virtually the same filtration behaviour, displaying a rapid increase of effluent concentration at 1 pore volume injected to a steady concentration close to the inlet concentration was seen in all experiments. A classical phenomenological approach was used to model the macroscopic propagation behaviour of suspended particles in the porous medium. The model was successful in history matching the effluent composition profile observed in the experiments and the deposition profile obtained from post‐test analysis of the sand‐bed. © 2011 Canadian Society for Chemical Engineering
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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) | 0.000 | 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".