Phased Heating and Solvent Injection to Enhance Recovery of Heavy Oil and Bitumen
Bibliographic record
Abstract
Abstract The main challenges faced by oil sands operators are the cost of operations and the environmental intensity of the recovery processes. The Athabasca oil sands deposit contains bitumen with viscosity typically over 1 million cP. To lower the viscosity of the bitumen so that it can be drained from these reservoirs, it is heated with injected steam by using Steam-Assisted Gravity Drainage (SAGD). This process is effective and enables recovery factors over 60%. The major cost in the recovery process is steam generation and associated water treatment and handling. The combustion of natural gas to generate steam is the main origin of the carbon dioxide emissions associated with SAGD. An alternative to steam injection is the use of solvents co-injected with steam. Solvents dilute bitumen leading to an oil phase with reduced viscosity. Also, there is potential to recycle solvent for re-injection. Thus, solvent added to steam can improve the steam-to-oil ratio and as a consequence can lower the carbon dioxide emissions per unit volume oil produced. In this extended abstract, we describe a phased solvent and heat process that yields improved performance beyond that of SAGD and current solvent-aided SAGD processes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".