Rich solvent - Steam assisted gravity drainage (RS-SAGD): An option for clean oil sands recovery processes
Bibliographic record
Abstract
The impact of steam generation on Steam-Assisted Gravity Drainage (SAGD) process economics and environmental impacts have led to various modifications of SAGD including solvent co-injection with steam. One example is the Expanding Solvent Steam-Assisted Gravity Drainage (ES-SAGD) process where 1–5 vol% solvent is injected with the steam into the reservoir. The solvent travels with the steam to the edge of depletion chamber dissolving into the oil thereby mobilizing the oil more than would have been achieved with heating alone. This means lower steam-to-oil ratio and thus, greater thermal efficiency, lower emissions intensity, and water consumption intensity. However, processes where the majority of the fluid injected is steam are still overly emissive and expensive to operate. There is a need to develop carbon-neutral bitumen production processes. Rich Solvent-SAGD (RS-SAGD) aims at injecting mostly solvent into the reservoir with small amounts of steam to achieve radically lower emissions and water consumption intensities than that of SAGD. Here, a RS-SAGD process is investigated where the solvent content is >60 vol% by using detailed thermal-solvent reservoir simulation in an Athabasca oil sands reservoir. The results reveal that RS-SAGD yields higher oil rate than that of SAGD and the process has significantly higher energy efficiency, up to 96% lower emissions and water consumption intensities than that of SAGD. Given the performance of RS-SAGD processes, oil sands operators should consider such processes for future operations.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".