Energy (Heat) Distribution and Transformation in the SAGP Process
Bibliographic record
Abstract
Abstract The Steam and Gas Push (SAGP) process was developed to improve the thermal efficiency of SAGD process. In SAGP, non-condensable gas is co-injected with steam into the reservoir. Ideally, the non-condensable gas accumulates at the top of the reservoir and provides insulation which reduces heat losses to the overburden. This means that lower SOR can be achieved at the same recovery factor. It remains unclear how energy is distributed and transformed within the chamber and its edges when non-condensable gas is added to the injected steam. In this work, we compare conduction and convection at edge of the steam chamber during SAGD and SAGP. The results show that both oil production rate and cumulative oil are reduced in SAGP compared to SAGD when 0.8 mole% NCG is co-injected with steam. This is because the injected NCG accumulates at the upper part of the leading edge of the steam chamber and slows down the growth of the steam chamber in that area, which results in lower cSOR but with a reduction of recovery factor. If 0.8 mole% NCG is co-injected at later periods of the operation, lower cSOR results without a significant reduction of oil production rates and cumulative oil production. In this case, the injected NCG migrates directly to the upper part of the reservoir and accumulates at the side edge of the steam chamber, since the steam chamber had already grown to the top of the reservoir. The added gas slows down lateral growth of the steam chamber in the upper part of the reservoir and forces steam chamber growth in the downward direction. From an analysis of energy transport in SAGP and SAGD operations, the results reveal the optimal timing for the onset of NCG co-injection with steam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".