Pseudo-Component, Thermal, Reservoir Simulation Study of a Proposed, Low Pressure, Steam-Assisted Gravity Drainage Pilot Project In Northeast Alberta
Bibliographic record
Abstract
Abstract Bitumen production from the Athabasca Oil Sands in northeast Alberta, Canada typically uses steam-assisted gravity drainage (SAGD) techniques. For shallow bitumen resources, low pressure, SAGD (LP-SAGD) and expanding solvent, LP-SAGD (SLP-SAGD) techniques are viable options for such developments as these methods maximize bitumen production and control steam-oil ratios (SORs), while maintaining asset integrity throughout the life of the project. In Alberta, applications requesting approval for pilot-scale, thermal exploitation of bitumen resources are submitted to the Energy Resources Conservation Board for review. As part of the application process, thermal reservoir simulation studies are often conducted, forecasting 10 years of asset development using SAGD. The authors recently completed a 3D, pseudo-component, thermal, reservoir simulation study of the proposed Clearwater West, Phase 1 Pilot Project located southeast of Fort McMurray, Alberta. The simulation program, STARS™, was used for this investigation. Three potential development strategies for the Pilot Project were evaluated. These strategies included LP-SAGD, where 100% coldwater equivalent (CWE) steam is continuously injected into the reservoir for 10 years. The other cases were SLP-SAGD, where the injection stream consists of 75% CWE steam and 25% solvent is continuously injected into the reservoir for either 10 years or for 7 years, followed by wellpair blowdown and the termination of solvent injection. Comparisons of key modelling parameter results, such as steam chamber development, bitumen production and recovery, SORs and solvent loss, were completed for all development strategies. A sensitivity study was also conducted.
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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) | 0.000 | 0.000 |
| 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".