Enhanced Heavy Oil Recovery by in Situ Prepared Ultradispersed Multimetallic Nanoparticles: A Study of Hot Fluid Flooding for Athabasca Bitumen Recovery
Bibliographic record
Abstract
Many in situ recovery methods have been developed to extract heavy oil and bitumen from deep reservoirs. The “underground refinery” approach using a nanosize ultradispersed (UD) catalyst is one of the alternatives to surface upgrading that may become the “next generation” of oil sands industry improvement. Water-in-vacuum gas oil microemulsions containing trimetallic (W, Ni, and Mo) ultradispersed colloidal nanoparticles could penetrate inside the porous medium and react with the bitumen. This study is aimed at developing a catalytic-enhanced oil recovery method for Athabasca bitumen recovery through the viscosity reduction mechanism with the aid of trimetallic nanoparticles. In this study, series of experiments were conducted at a pressure of 3.5 MPa, residence time of 36 h, and temperatures from 320 to 340 °C in an oil sands packed bed column. Results of three consecutive categories of hot fluid injection (in the presence or absence of trimetallic nanoparticles) are presented. For the first category, the obtained experimental results showed that the recovery curve for vacuum gas oil injection without nanocatalysts was at a plateau. In the second series of tests, observations proved that adding a certain percentage of pentane enhanced the recovery performance of injection tests. The third phase of experiments was conducted in the presence of trimetallic nanocatalysts in emulsion with vacuum gas oil. Results showed the effectiveness of nanocatalysts for enhancing the recovery performance compared with the cases of no nanoparticle implementation.
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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".