Experimental Study on Transport of Ultra-Dispersed Catalyst Particles in Porous Media
Bibliographic record
Abstract
In situ upgrading of heavy oil by catalytic hydrogenation using submicrometer sized dispersed catalysts during thermal recovery is a promising new idea to achieve an environmentally sustainable method for unlocking heavy oil and bitumen resources. This requires placement of the ultradispersed catalyst particles deep into the formation where it can accelerate the high-temperature upgrading reactions. The objective of this work was to investigate the feasibility of transporting such ultradispersed catalyst particles through porous rock formations. This paper presents the results of experiments carried out to systematically examine the propagation of ultradispersed catalyst suspensions in sand packs. These experiments involved the injection of submicrometer-sized catalyst particles suspended in oil into a sand pack and analysis of the produced fluid samples and the sand bed. The results show that it is possible to propagate the ultradispersed catalyst suspension through sand beds. However, a fraction of the catalyst particles are retained by the sand (around 14 to 18%), and much higher retention occurs in the entrance region of the bed. Particles appear to be deposited on sand surfaces by an attachment mechanism deep inside the bed, but larger particles appear to be strained by mechanical trapping near the inlet face. The deposition of particles was found to be almost irreversible in the sense that the deposited particles could not be remobilized by reverse flow of the suspending medium.
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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".