Design and Optimization of Orifice based Flow Control Devices in Steam Assisted Gravity Drainage: A Case Study
Bibliographic record
Abstract
The classical SAGD (Steam Assisted Gravity Drainage) involves drilling wells in parallel horizontal pairs. Steam is injected into the upper well (injector) to heat the reservoir and mobilize bitumen/heavy oil so that it drains to the lower well (producer) and can be lifted to the surface. In this process, steam distribution in the injector and a sustainable liquid level above the producer are key to achieve steam chamber conformance. The completion designs of these wells are critical in order to achieve optimal bitumen/heavy oil recovery and steam chamber development1. Orifice based Flow Control Devices (FCDs) are being used in the SAGD wells. The FCDs in the injector (Steam Splitters) are used to customize steam distribution along the well. The FCDs in the producer (Inflow Control Devices) are used to develop a uniform inflow along the horizontal wellbore. In this paper, a method will be presented for determining the size and position of Steam Splitters and Inflow Control Devices. This method can be used for both simple and complex reservoirs containing geological heterogeneity, hydraulic barriers and baffles. The validation of the design by field data in a Case Study will also be presented. It is shown that using FCDs in the wellbores helps to improve conformance and performance of SAGD well pairs significantly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".