Investigating Deposition of Nanocatalysts in Porous Media for Catalytic Downhole Upgrading Processes
Bibliographic record
Abstract
Abstract A recently proposed in-reservoir upgrading method, called In-Situ Upgrading Technology (ISUT) integrates both downhole upgrading and recovery enhancement in one single process. This method employs catalytic nanoparticles of nickel and molybdenum (NiMo) which are dispersed in vacuum residue (VR), the lowest quality fraction of oil. The dispersed nanocatalyst in VR along with hydrogen are then injected in the reservoir to carry out hydroprocessing reactions. The upgraded liquid and produced gases displace the oil-in-place towards the production well. Therefore, it is important to investigate the deposition of nanoparticles and the correlation with the injected hydrocarbon molecules in porous medium. In this study, a continuous setup was designed and constructed to investigate the deposition of nanoparticles in a sandpack that mimics the oilsand reservoirs. The VR containing nanocatalysts was injected to decorate the sandpack with catalyst. The operating conditions were set to 350 °C, 1000 psig (7 MPag) and 48 hr of residence time. Once certain concentration of catalyst was deposited in the sandpack, the process was stopped and the sandpack was taken and cut in ten sections for analysis. A comprehensive post-mortem analysis including investigation of catalytic particle deposition as well as characterization of the entrapped hydrocarbons in each segment was carried out. It was already observed that the particles irreversibly and totally retain in the porous medium in ISUT process. In this work, an attempt to correlate the deposition of catalyst mechanism with the hydrocarbons that might help the deposition process was performed. It is assumed that the polar components, mainly asphaltenes, play a key role in carrying the catalytic particles and allow them to deposit. Several implicit and explicit analysis were performed to investigate this assumption in this work. Results showed that the sections containing the highest concentration of catalyst contain heavier molecules such as asphaltenes.
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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".