Injection of Non‐Condensable Gas in SAGD Using Modified Well Configurations ‐ A Simulation Study
Bibliographic record
Abstract
The objective of this research is to examine the feasibility of NCG co-injection with steam in SAGD using modified well configurations using numerical simulation. The NCG used in this project is methane. The aim is to form a more stable insulating layer just below the top of formation, which results in lowered overburden heat loss and cSOR. To place the NCG directly below the overburden rock, vertical steam injectors with dual completions are implemented in this study. Simulation runs are created using CMG STARS (Thermal and Advanced Process Simulator). These simulation results demonstrated substantial improvement in cSOR by injecting NCG separately from steam using the dual completed vertical injectors. The producer operational constraint is a combination of maximum live steam rate and minimum bottom-hole pressure (BHP). The base case covers production from May 2011 to January 2020 in steam only SAGD operation. The base case of non-flowing boundary condition showed approximately 157,000 m3 oil production in about 10 years, with a cumulative Steam Oil Ratio (cSOR) of 9.0. The simulation results show that injecting the non-condensable gas improved cSOR but maintained similar cumulative oil production compared to conventional SAGD process. As expected, non-condensable gas reduces the gas mobility and stabilizes the insulating blanket formed by high gas saturation at the top of the steam chamber. The optimized simulation case with three vertical wells enabled the cSOR to be as low as 2.27.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".