Heavy Oil Production with Energy Effective Steam-Assisted Gravity Drainage
Bibliographic record
Abstract
In reservoirs with extra heavy oil and bitumen, thermal methods are used to reduce the viscosity, in order to extract the oil. Steam-assisted gravity drainage (SAGD) is a thermal method where continuous steam injection is used. In this method, two horizontal wells are placed in parallel. The upper well injects steam and the lower well produces oil and condensed water. The continuous steam injection creates a chamber with uniform temperature. Heavy oil and bitumen reserves in Western Canada, which exceed 175 billion barrels, are becoming increasingly important petroleum sources due to the technical success of the SAGD processes. This study includes Computational fluid dynamics (CFD) modelling and simulations of a horizontal oil well with SAGD. The simulations are performed with inflow control devices (ICD) and autonomous inflow control valves (AICV) completion. In the SAGD processes, it is important that the residence time for steam in the reservoir is high enough to ensure that all the injected steam condenses in the reservoir to reduce the amount of steam injection and thereby making the SAGD process more energy effective. The simulations are carried out with ICD completion to delay the steam breakthrough and with AICV completion to prevent breakthrough of steam and water to the well. The numerical results showed that a most of the steam was produced together with the oil when ICD completion was used. AICV was able to close for steam and water, and the steam was thereby forced to condense in the reservoir, resulting in better utilization of the condensation energy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".