Experimental Study of Simultaneous Athabasca Bitumen Recovery and Upgrading Using Ultradispersed Catalysts Injection
Bibliographic record
Abstract
Abstract The worldwide global demand for oil has grown to 80 million barrels per day, and is estimated to grow by 50% in next 20 years while conventional resources are declining. Unconventional reserve of heavy oil and bitumen has been considered as long term replacement for conventional resources. There are a large number of research projects in order to achieve this goal with technological efficiency. In situ upgrading of heavy oil and bitumen using Ultra Dispersed (UD) submicronic catalysts is a promising idea to improve the quality of produced liquid. In this process, hydrogen and a catalytic suspension are injected to the reservoir to react with heavy oil in the porous media. Nano catalyst particles enhance the recovery of oil by viscosity reduction. Series of experiments have been designed in an elemental model under typical reservoir condition. Tests are performed at a pressure of 500 Psi, residence time of 36 h, and temperatures from 300 to 340 (°C). This paper presents the results of the in situ upgrading obtained along with the recovery of bitumen evidenced using ultra dispersed catalysts in an experimental rig. These experiments involve the injection of UD catalyst particles and hydrogen into a sand pack which is saturated with Athabasca bitumen. Produced liquids were analyzed with different techniques and results were demonstrated in recovery curves. Also, results of each experiment have been compared with base steam injection case to evaluate recovery performance of the catalyst suspended in a hot fluid. Temperature profile distributions and produced gas analysis are demonstrated to justify the quality of reaction through porous media. Produced liquids from media have higher API gravity and lower viscosity which shows a successful in situ upgrading process.
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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) | 0.001 | 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".