Experimental Study of Hot Fluid Injection into Athabasca Oil Sand Reservoir
Bibliographic record
Abstract
Abstract With more than 170 billion barrels of estimated oil sands, Canada has the second largest oil reserve in the world. 80 percent of these reserves are deep underground and can not be accessed by surface mining. Many In-Situ methods have been developed to extract heavy oil and bitumen from deep reservoirs. Once produced, bitumen is transferred to upgraders converting low quality oil to synthetic crude oil. Transportation of bitumen to a location for upgrading is a challenging aspect of bitumen production. In addition to processing costs, the level of contaminants in bitumen (sulphur, metal and nitrogen) requires special equipments, and has also environmental repercussions. The "Underground Refinery" using nano-size Ultra-Dispersed (UD) catalyst is one of the alternatives to surface upgrading that may become the "next generation" of oil sands industry improvement. In order to perform a successful downhole upgrading process several steps should be implemented: catalyst placement (or presence) in an appropriate zone; reactants and co-reactant interaction, including heavy oil, catalyst and hydrogen; and creation of necessary reaction condition to obtain higher quality products, such as pressure and temperature. This paper will present results of hot fluid injection in an elemental experimental model. Sets of experiments in various temperatures have been conducted to investigate effect of hot fluid on recovery enhancement. Results of each experiment compared with base SAGD process. Temperature profile distribution has been performed for each scenario. Results show that mixed hot fluid injection increased original oil recovery of bitumen up to 39% which shows (5–10 percent) recovery enhancement compared to pure hot fluid injection.
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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".