Co-Injection of Noncondensable Gas Improves ES-SAGD Performance in Shallow Oil Sands Reservoirs With a Small Top Water Zone
Bibliographic record
Abstract
Abstract The majority of oil sands are too deep for surface mining extraction; hence, in-situ techniques such as Steam-Assisted Gravity Drainage (SAGD) must be used. However, SAGD in low-pressure, top water, reservoirs shows relatively poor performance. To improve SAGD, solvent can be co-injected with steam, as in Expanding Solvent SAGD (ES-SAGD) leading to enhanced recovery, rates, and efficiency. Like most pressurized processes, a competent caprock is needed to prevent steam losses to maintain good efficiency and rates. There exist significant oil sand resources that are considered inaccessible because they are shallow (low-pressure) with little or no caprock with top water zones. Solvent addition allows reduced operating pressure, which makes it amenable for low pressure, shallow reservoirs. This research examines ES-SAGD, with non-condensable gas co-injection, in reservoirs with top water that has the potential to quench the chamber and stagnate oil drainage. The results reveal complex dynamics between the depletion chamber and overlying water zone and operating strategies that extend the life of the chamber thus raising the recovery factor. Low-pressure ES-SAGD operating strategies can be used to efficiently recover bitumen from shallow reservoirs with top water. A key finding from this study is that addition of non-condensable gas to ES-SAGD can significantly improve recovery, rate, and efficiency. Given the volume of shallow oil sands reservoirs with top water, development of processes to unlock this type of resource is important and critical to further growth of in situ oil sands recovery in Alberta. The results provide a technical basis to construct feasible low-pressure ES-SAGD processes for this type of reservoir.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".