Use of Nano-Metal Particles as Catalyst Under Electromagnetic Heating for Viscosity Reduction of Heavy Oil
Bibliographic record
Abstract
Abstract In order for heavy oil and bitumen recovery to be efficient, all components present within the oil must be produced. To achieve a highly efficient production process is it essential that we are able to produce asphaltenic components and limit their precipitation. Solvent and conventional thermal techniques are largely limited in their ability to crack asphaltenic components. Thus, new technologies and catalysts are needed to be more efficiently recover heavy oil. When nano-sized metal particles are present, they catalyze the breaking of carbon-sulfur bonds with in asphaltenic components. The result of this process is an increase in saturates and aromatics, while simultaneously reducing the aphaltene content. This process dramatically lowers the viscosity of heavy oil and bitumen by significantly reducing the average molecular weight. This effect can be dramatically increased by having a strong hydrogen donor present, and can be completely inhibited by the removal of all hydrogen donors. When conducting these types of reactions in-situ, it is very difficult and expensive to introduce strong hydrogen donors. Therefore, it is imperative that hydrogen donors be created within the oil rather than be introduced from an external source. In this paper, we investigated the effects of microwave radiation, using a s 2.45 GHz emitter, on the recovery of heavy oil from a sand pack. Experiments were conducted with and without nano-sized nickle catalyst being present. Heavy oil samples were heated at different power levels until recovery of heavy oil leveled out. In all cases, the nano-nickle catalysts performed better than their microwave oil counterparts. This is due to the increased cracking and vaporization which was demonstrated by Greff and Babadagli (2011) to take place in the presence of nano-size metal catalyst and microwaves.
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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".