Bibliographic record
Abstract
Abstract In steam assisted gravity drainage (SAGD) process, accumulation of non-condensable gases at the edges of the steam chamber creates a resistance to heat transfer between hot steam and cold bitumen, thus slowing down growth of the steam chamber. Efficient removal of these gases from the steam chamber can substantially accelerate the recovery process. Typical practice in SAGD is to use steam splitters and strive for a relatively uniform pressure in the horizontal part of the well, which allows for even distribution of injected steam into the reservoir. In convective SAGD process, a significant pressure gradient is deliberately created along the horizontal length of the injector well by tailoring the well completion design. Enhanced flow resistances can be fashioned by using finned tubes, static mixers, flow constrictions or simply using tube sections with varying diameters. A complementary placement of resistances in the producer well prevents short-circuiting of steam. Pressure gradients created in the injector well are translated on to the reservoir, thus allowing for a sweep of non-condensable gasses from the steam chamber. In reservoir simulation studies, a novel convective SAGD process has shown significant improvement in oil production, with 20% higher peak production rates as compared to traditional SAGD while facilitating removal of non-condensable gases in excess of 90% from the steam chamber over the life of the well. Since fluids in the reservoir can now also move along the length of the wells, convective SAGD demonstrates a distinct advantage over traditional SAGD in heterogeneous reservoirs as horizontal barriers to flow can now be overcome with time. Currently, a field pilot is being pursued at Foster Creek to test the validity of convective SAGD process.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".