A review of design factors in steam and gas push for eco-friendly oil sands production and its field application in Canada
Bibliographic record
Abstract
Steam and gas push (SAGP) reduces greenhouse gas emissions by co-injecting non-condensable gas (NCG) with steam, preventing heat loss to thief zones and maintaining steam chamber pressure and temperature. However, NCG can hinder steam chamber growth, reducing oil production than steam-assisted gravity drainage (SAGD). Additionally, determining the type, concentration, and injection timing of NCG based on the given reservoir conditions can be challenging. Nitrogen and methane are commonly used NCGs due to their low solubility in oil, but concentrations above 3 mol% typically decreases SAGP efficiency. To prevent NCG interference with steam chamber, an injection pressure of 0.95–1.1 times reservoir pressure and an NCG injection between 0 and 0.6 times total production period are recommended. Numerical simulations showed that injecting NCG after 0.125, 0.25, and 0.375 times total 8-year production period increased cumulative oil production by 18.6%, 163.7%, and 218.6% respectively, compared to injection from the start. Sensitivity ranges for reservoir parameters include thief zone thickness of aquifer (0–0.5 times reservoir thickness), ratio of vertical to horizontal permeability for sandstone (0.3–0.65), and oil viscosity based on major oil sands regions in Canada (2,000,000 cp. for Athabasca, 200,000 cp. for Peace River, and 60,000 cp. for Cold Lake at 12 °C). Thicker thief zones increase heat loss and higher vertical permeability accelerates steam chamber rise, requiring earlier NCG injection. Additionally, lower oil viscosity regions are more suitable for SAGP. Field application of Suncor Firebag project that NCG reduced cumulative steam-oil ratio from 3.14 to 2.76, demonstrating SAGP’s effectiveness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".