Bibliographic record
Abstract
Abstract I presented a paper "Is There Life After SAGD?" at the 1996 Western Regional Meeting1. The paper was published and elicited some 20 discussions. Today, SAGD - Steam-Assisted Gravity Drainage - is a commercially successful recovery method in Canada, if the price of oil is right. The subject of this paper is what we have learned in the past 20 years, and what is the future of SAGD in oil (tar) sands and heavy oil exploitation. At last count there were 18 SAGD projects in Alberta, and none anywhere. Bitumen production by SAGD is about 800,000 B/D. There have been failures also - in Alberta, California, Venezuela, etc. The oil recovery factor can be as high as 60%, depending on how one defines it, and as low as 10-20%. The steam-oil ratio varies from 1.6 Bbl steam/Bbl oil, to 8 Bbl/Bbl. (Note that these are based on 95% downhole steam quality and are to be multiplied by ~1.3 to compare with typical steam injection SOR's). There are large variations even within the same project. The heat production can be as much as 50% of that injected. The desired downhole steam quality is 100%. Thus SAGD is a unique recovery method, but its application requires great caution. Some of the assertions of 20 years ago still apply. A few new aspects of SAGD have come to the fore. Many variations of the basic process have been proposed and tested. Many have failed. In some of the less successful projects, solvents and other additives are being injected with steam to improve performance. The attractive features of SAGD are described, and application guidelines are offered, based on failures. SAGD is here to stay, but what are the limits to application? This is the focus of the paper.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".