Experimental Simualtion Of Hot Fluid Injection Process for In-reservoir Upgrading
Bibliographic record
Abstract
Nowadays, the industry recovers bitumen by injecting heat via steam to reduce bitumen’s viscosity in the reservoir so it flows easily to the surface. A high heat capacity fluid could eventually replace steam in this role. Vacuum Residue (VR) is the heaviest, most viscous and richest in contaminants amongst the different bitumen fractions, thus is the one deserving most upgrading, and it also has the highest heat capacity of oil fractions. The Centre for In Situ Energy at University of Calgary has proposed the use of VR to simultaneously recover and upgrade in situ, as it can be a carrier of heat but also of nano-dispersed catalysts and dissolved hydrogen into a reservoir which can enhanced upgrading. The design and construction of a new reactivity test unit for evaluating the injection of ultra-dispersed catalyst suspended on Athabasca vacuum residue (AVR) using dispersed hydrogen in sand pack media has been completed and extensively tested in this work. The deposition of the catalyst particles on the surface of the porous medium was studied and the amount of metal inside the reactor quantified. The results for catalytic evaluation showed a residue fraction conversion of up to 23 wt. %. Finally, the study of the injection of industrial VR and Athabasca bitumen to a porous medium with ultra-dispersed catalyst was carried out at typical reservoir conditions. The results showed a considerable improvement of the feedstock producing a conversion in the residue fraction of 32 wt. % and 15 wt.% respectively.
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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".