Multicomponent Solvent Co-injection with Steam in Heavy and Extra-Heavy Oil Reservoirs
Bibliographic record
Abstract
Expanding solvent steam assisted gravity drainage (ES-SAGD) is a hybrid steam–solvent oil recovery process that can be used to extract oil from heavy oil and bitumen reservoirs. It is a variation of the SAGD process in which only steam is used. In ES-SAGD, the mobilization of highly viscous oil is enhanced through a combination of heat and mass transfer processes, which results in significantly reduced volumes of water and natural gas needed to generate the injected steam, making ES-SAGD more energy efficient and environmentally sustainable relative to SAGD. Both SAGD and ES-SAGD use the same well configuration, and solvent co-injection in existing SAGD projects often requires limited facility modifications. This study investigates different aspects of ES-SAGD experimentally, based on typical Long Lake reservoir properties and operating conditions, using different concentrations of gas condensate. Furthermore, this study provides phase behavior insights to govern the selection of appropriate solvents for use in ES-SAGD. The performance of the gas condensate ES-SAGD cases in this study exceeded that of the baseline SAGD case in terms of oil production rates, energy efficiency, and postproduction water handling. These findings were instrumental in the design and implementation of a field pilot project by Nexen Energy ULC in the Athabasca Oil Sands that began in September 2014.
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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 | 0.001 |
| 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".