Comparison of CSS and SAGD Performance in the Clearwater Formation at Cold Lake
Bibliographic record
Abstract
Abstract Data are available for four SAGD projects and two CSS projects operating in the Clearwater formation at Cold Lake. This paper uses these data to compare the energy efficiency and recovery performance of SAGD and CSS. For the conditions outlined in this paper, field data demonstrate that: Bitumen recovery using SAGD is generally uneconomic in the Clearwater formation.Bitumen recovery using CSS in the Clearwater formation:Produces as much as 50% or more bitumen/m3 external gas consumed than SAGD; andWill result in significantly higher overall bitumen recoveries (as a percentage of OBIP) than SAGD. These observations are consistent with industry experience in non-Clearwater SAGD and CSS operations. In addition, operating and design data for commercial SAGD and CSS projects is used to demonstrate that: Due to differences in steam quality, SOR is not an appropriate indicator of energy efficiency. Energy efficiency comparisons are more appropriately based on the quantity of external gas required to produce 1-m3 of bitumen.To convert SOR data to an external gas requirement, the following conversion factors are proposed: 1-m3 wet (80% quality) steam requires approximately 60-m3 gas.1-m3 dry (100% quality) steam requires approximately 75-m3 gas.The use of project specific EBIP, versus OBIP, in calculating recovery makes meaningful comparisons difficult.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".