Laboratory Assessment of Residual Oil Saturation Under Multi-Component Solvent SAGD Coinjection
Bibliographic record
Abstract
Solvent-assisted steam-assisted gravity drainage (SA-SAGD) is an advanced hybrid oil recovery technique designed to enhance the extraction of heavy oil and bitumen. Unlike the conventional SAGD process, which relies solely on thermal energy from injected steam, SA-SAGD incorporates a coinjected solvent phase to improve oil mobility through the combined action of heat and mass transfer. This synergistic mechanism significantly reduces the demand for water and natural gas used in steam generation, thereby improving the energy efficiency and environmental sustainability of the process. Importantly, SA-SAGD retains the same well pair configuration as SAGD, meaning that its implementation often requires minimal modifications to existing infrastructure. This study explores the residual oil saturation following multi-component solvent coinjection in SA-SAGD using a linear sand pack model designed to emulate the properties and operational parameters of the Long Lake reservoir. Experiments were conducted with varying constant concentrations of cracked naphtha and gas condensate to assess their effectiveness in enhancing bitumen recovery. The results reveal that the injection of 15 vol% cracked naphtha achieved the lowest residual oil saturation and the highest rate of oil recovery, indicating superior solvent performance. Notably, gas condensate at just 5 vol% concentration outperformed 10 vol% cracked naphtha, demonstrating its effectiveness even at lower concentrations. These findings provide valuable insight into the phase behaviour and recovery dynamics of solvent–steam coinjection systems. The results strongly support the strategic selection of solvent type and concentration to optimise recovery efficiency while minimising steam consumption. Furthermore, the outcomes offer a robust basis for calibrating reservoir simulation models to improve the design and field-scale application of SA-SAGD, particularly in pilot operations such as those conducted by Nexen Energy ULC in the Athabasca Oil Sands.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".