Simulation Sensitivity Study and Design Parameters Optimization of SAGD Process
Bibliographic record
Abstract
Abstract The process of Steam Assisted Gravity Drainage or better known by its English acronym as SAGD has been successfully tested and commercially implemented in the last decade. It has been particularly successful exploiting the Athabasca oil sands in Alberta. Some of the reservoir features that are frequently present in the oil sands include the presence of bottom water, top water and gas caps. All of them, representing potential thief zones and are inherent to the reservoir nature. Characterize and understand how those features affect the SAGD process is a key element of early project planning. In the present study, a simulation work to asses the feasibility of SAGD process for a typical section of the McMurray Formation in Alberta was conducted to asses the impact of the mentioned reservoir features. This study confirms that the presence of bottom water is harmful to the SAGD process; thicker bottom water will be more detrimental. A vertical separation of 5 m to the bottom water was determined as optimum for the production well in order to achieve better recovery and economics. Depleted gas pools associated, or in partial communication with bitumen reservoirs represent a potential risk to the effectiveness of SAGD. The connectivity of gas pools to bitumen reservoirs depends on the vertical permeability and thickness of the material that lay in between; this determines their potential to prevent steam to escape up to depleted gas caps. An optimal range of inter-well spacing among 50 m and 80 m was established for the average reservoir conditions evaluated in this study.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".