In- Situ Heavy Oil Upgrading through Ultra-Dispersed Nano-Catalyst Injection in Naturally Fractured Reservoirs: Experimental Section
Bibliographic record
Abstract
Abstract Conventional reserves in Mexico are nowadays in depletion, unconventional or not straight forward reservoirs are the next challenge for maintaining the oil production plateau of the Mexican industry. Several EOR processes are under study in order to deal with this situation, one promising process for naturally fracture reservoirs is the "In Situ Upgrading" which produces EOR and Upgrading by injection of a hot fluid, proposedly vacuum residue from the heavy oil produced, with suspended nano-catalytic particles flowing down-hole along with dissolved hydrogen as precursor of partial Hydroprocessing of the residue. The combination of thermal and chemical reactions with a catalyst leads to In-Situ Upgrading (ISU) of the residue injected and the in situ oil at the conditions created in the reservoir near the surroundings of the injection well bore, while the heat carried to the reservoir, the diluents produced by upgrading processes, the steam and hot water generated warm up progressively the rock and the heavy oil in the porous media, enhancing oil extraction. Testing the reactivity of the oil from a particular field has to be assessed at temperatures, pressures and residence time characteristics for the targeted reservoir in order to determine the suitability of this EOR process in that environment.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".