Propagation of Nanocatalyst Particles Through Athabasca Sands
Bibliographic record
Abstract
Abstract This paper presents results of an experimental study that systematically examined the propagation of nanodispersed catalyst suspension in sand packs at Athabasca reservoir conditions. The concentration and size distribution of the particles at the injection and production end were measured. The pressure drops in different segments along length of the sand pack were monitored continuously. The retention behavior of particles at the end of each experiment was examined by measuring the catalyst concentration in the bed as a function of the distance from injection end of the sand pack and also by analysis of extracted samples using scanning electron microscopy. This research is a part of a large multidisciplinary effort aimed at developing a nanoparticles based process for in situ upgrading of heavy oil by catalytic hydrogenation during thermal recovery processes. An essential element of such in situ upgrading is the placement of nanodispersed catalyst particles deep into the formation where it can accelerate the high temperature upgrading reactions. Therefore, an understanding of the propagation behavior of nanoparticles in reservoir sand is essential for developing such technology. The results of this work would also be useful for modeling any other process involving transport of nanoparticles through porous media. The results show that it is possible to propagate the nanodispersed catalyst suspension through sand beds without causing permeability damage but a small fraction of the injected particles are retained in the sand. It was found that much higher retention occurs in the entrance region of the bed and such retention was higher in the Athabasca sand beds than in clean silica sand with the same flow and suspension properties. A modified deep bed filtration model was developed to history match the macroscopic propagation behavior of suspended particles in sand beds. To best of our knowledge, this is the first experimental study on transport of nanoparticles dispersed in viscous oil through sand beds. It provides valuable information on propagation and retention behavior of nanoparticles. Considering the rapidly rising use of nanoparticles in industry, such transport will be encountered in many industrial applications and environmental problems.
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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) | 0.001 | 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".