Numerical Simulation Study on Parameters related to Athabasca Bitumen Recovery with SAGD
Bibliographic record
Abstract
The world’s total oil reserves are to some extent dominated by heavy oil. The heavy oil reserves are doubled in volume compared to conventional oil reserves. As conventional oil reservoirs are depleting, heavy oil and bitumen possesses a great potential in covering parts of the future energy demand. The possibility of horizontal drilling has created a pathway for SAGD (Steam Assisted Gravity Drainage), which is the most preferred heavy oil and bitumen recovery method. The mechanism of SAGD involves two parallel horizontal wells, one for production and one for injection. The production well is situated at the bottom of the reservoir and the injection well is placed above. Steam is injected and heats up the oil which is then able to flow to the production well by gravity drainage. In the present thesis, a numerical study of parameters has been performed in relation to SAGD implementation in the Athabasca field. The thermal simulator utilized is CMG STARS. The Athabasca field is located in Northern Alberta in the Western Canada Sedimentary basin. Due to the complexity of core extraction in bitumen reservoirs, a comprehensive sensitivity analysis is significant in order to determine the appropriate production approach. The present study confirmed that a decrease in viscosity and increase in porosity yielded higher oil recoveries. All oil recoveries found in 3D simulations were within model uncertainties compared to the 2D result. Increase in horizontal and vertical permeabilities resulted in higher oil recovery up to a certain limit, where exceeding permeabilities provided limited increase in oil recovery. The effect of different vertical well spacing proved to have minor effect on amount of oil produced. Yet, based on cumulative steam oil ratio (CSOR) it was proposed to maintain a vertical well spacing in the range of 3.5 to 7 meters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".