Co-Injection of Foam and Steam in SAGD Using a Modified Well Configuration-A Simulation Study
Bibliographic record
Abstract
Abstract This study investigates the technical viability of combining foam injection with steam in Steam-Assisted Gravity Drainage (SAGD) operations through modified well designs, employing both computational modeling and experimental analysis. The research utilizes a foam system comprising water, non-condensable gas, and surfactants, specifically designed to control gas mobility and minimize residual oil saturation. The foam creates an enhanced thermal barrier beneath the formation cap, effectively reducing heat transfer to overburden strata and improving cumulative Steam-to-Oil Ratio (cSOR). The investigation employs vertical injection wells to position the foam directly under cap rock formations. Numerical simulations were conducted using CMG STARS thermal reservoir simulator, with Long Lake Pad 16 serving as the field prototype. All simulations incorporated no-flow boundaries, with producer wells constrained by maximum vapor injection rates and minimum bottomhole pressure thresholds. A baseline scenario modeling conventional SAGD operations from 2017 to 2027 projected 75,000 m3 of cumulative oil production with a cSOR of 6.19. Comparative analysis revealed that foam co-injection significantly enhanced both oil recovery and thermal efficiency. The foam mechanism effectively moderated gas mobility while reinforcing the insulating gas-saturation layer at the chamber apex, concurrently increasing trapped gas content and decreasing residual oil. Optimal configurations demonstrated substantial cSOR improvements: a three-vertical-injector arrangement achieved 4.3 at 2,500 kPa, while a field-adaptable four-well system at 2,500 kPa (within Long Lake's 2,600 kPa limit) yielded a cSOR of 4.25. These findings confirm foam-assisted SAGD's potential to enhance heavy oil recovery while optimizing thermal efficiency through engineered well architectures.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".