Impact of Different SAGD Well Configurations (Dover SAGD Phase B Case Study)
Bibliographic record
Abstract
Summary The volume of heavy oil and bitumen in the oil-sands deposits in western Canada is similar to that of conventional crude oil in the Middle East. This resource is immense but is difficult and energy intensive to extract because the viscosity of the oil is high, typically greater than 100,000 to 1,000,000+ cp at original reservoir conditions. Current commercial thermal-recovery processes used include steam-assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS). These methods are energy intensive, emit significant amounts of CO2 into the atmosphere, and use large volumes of water to recover the oil. In this work, the focus is on SAGD processes. It has been demonstrated that operating strategy can be altered to improve SAGD performance, but it is not clear how well configuration can be changed to improve recovery, energy intensity, thermal efficiency, water use, and flue-gas emissions. This paper examines the impact of position and geometry of steam injectors on the performance of steam-based gravity-drainage processes in a heterogeneous reservoir. Different injection-well configurations including single horizontal (typical SAGD), offset SAGD, and vertical/horizontal well combinations have been evaluated by using a detailed, 3D, geostatistically populated, large-scale thermal reservoir-simulation model derived from core-data examinations of the Dover pilot site. The research reveals how injection-well configuration impacts energy delivery to the reservoir, how it affects thermal efficiency, and how it changes the evolution of the steam-conformance zone and oil-flow dynamics in the reservoir. The results suggest that several vertical injectors have the potential to deliver steam more efficiently than a single horizontal injector.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".