Mo–Ni, Mo–Fe and Mo–Ni–W nanoparticles for down-hole upgrading
Bibliographic record
Abstract
Patented in situ (in reservoir) upgrading technology (ISUT) converts a portion of the reservoir into a “catalytic reactor” by irreversibly attaching a low percentage of nanoparticles to the reservoir's sand/rock. The nanoparticles are prepared as a dispersion in hot vacuum residue and the hot dispersion is injected into the reservoir with low quantities of hydrogen. MoNi, MoFe and MoNiW nanoparticles suspended in bitumen were prepared, in similar way as in ISUT, and the suspensions treated in a batch reactor. Two different sulfiding agents (ammonium sulfide and thiocarbamide) were added during the preparation of the nanoparticles-bitumen suspension. Particle diameters obtained for the suspensions were determined by NTA. The particles were recovered after reaction and its surface composition determined by XPS . Reaction products were analyzed by high temperature simulated distillation, density, viscosity, sulfur and nitrogen content and stability (P-value). MoNi, MoFe and MoNiW with average sizes of 84, 101 and 93 nm, respectively, were produced. XPS characterization of the Mo–Ni–W particles before reaction showed that Mo and Ni are only partly sulfided, while W was present as WO 3 . The proportion of the metals in the sulfided state increases after reaction from 39 to 94, 35 to 77 and a 0 to 50 (atomic %) for Mo, Ni and W, respectively. In addition, VR conversions were similar by comparing the thermal and catalytic tests, indicating that VR cracking is mainly thermal. However, viscosity, MCR and S contents were lower, and API gravity was higher when a catalyst was used. Also, catalytic products proved more stable than products obtained from thermal reaction. The reaction carried out using dispersions where thiocarbamide was the sulfiding agent provided products with slightly betterer properties in spite of showing larger particle diameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".