Bibliographic record
Abstract
Abstract Steam-Assisted Gravity Drainage (SAGD) is widely used in Alberta for recovering bitumen from oil (tar) sands. A variation of the same has had some success in heavy oils as well. It is a high-risk recovery method and requires careful planning and design. This paper outlines the success criteria for SAGD, and a design methodology. First of all, applicability of other lower risk recovery processes, such as steamflood variants, is considered to determine if SAGD is a good choice. SAGD has been successful in oil sands of Alberta under rather restricted conditions. Geology is the most important factor, in particular vertical permeability, oil saturation, and initial mobility of water. Where the minimum criteria are not satisfied, there have been failures, discussed also. The author has developed new equations for the entire SAGD process, not just the stabilized oil flow regime, given previously, and has corrected the errors in the same. These are discussed in detail, with examples. The application of SAGD variations in conventional heavy oils is also considered. The current experience in Alberta, and elsewhere, is described, and reasons for success/failure are outlined. Given that background, the desirability of SAGD vis-à-vis other thermal processes is discussed for California heavy oils. The variation of SAGD being employed in Saskatchewan heavy oils is also discussed, showing that it is not SAGD in the strict sense, rather a modified steamflood, using horizontal wells. It is concluded that SAGD has a high oil recovery potential if the right combination of rock-fluid properties is present. The application of SAGD to conventional heavy oils is equally problematic, in view of a very different mechanism. The novelty of the paper lies in (1) a comprehensive treatment of SAGD, from the rising chamber to plateau to decline phases, (2) assessment of SAGD compared to other recovery methods for different types of reservoirs, and (3) application of SAGD variants to conventional heavy oils.
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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".