Towards the Development of Bitumen Carbonates: An Integrated Analysis of Grosmont Steam Pilots
Bibliographic record
Abstract
The Grosmont Formation in Alberta, Canada is a highly fractured, karstified and vuggy bitumen-rich carbonate reservoir located west of and below the Athabasca oil sands deposit. The bitumen carbonate platform extends about 500 km in length and up to 150 km in width and contains an estimated 64.5 billion m3 (406.5 billion barrels) of oil. The Grosmont Formation is larger than the combined total of all other known carbonate bitumen deposits in the world. Here, we analyze early Grosmont steam pilots to improve the design of future pilots and commercial development of this massive bitumen deposit. In agreement with the conclusions of earlier analysis of these Grosmont pilots, they were reasonably successful considering the heterogeneous nature of the Grosmont Formation. Operational factors such as poor steam quality, non-optimized high injection pressures and completion issues appear to have heavily impacted recovery performances. Clearly, steam-based recovery operations have good potential for Grosmont, especially considering that it is mature commercial technology. Following an integrated analysis of early Grosmont pilots, we posit that Cyclic Steam Stimulation (CSS) using horizontal wells exhibits greater potential for the development of Grosmont carbonate, compared with Steam-Assisted Gravity Drainage technology (SAGD).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".