Screen-Inflow-Design Considerations with Inflow Control Devices in Heavy Oil
Bibliographic record
Abstract
Abstract Inflow control devices (ICDs) improve oil recovery because of their capability to delay water and gas breakthrough. ICDs also can be applied to heavy oil reservoirs to help overcome the higher mobility of water, and therefore, are now frequently used in Canada's Steam-Assisted Gravity Drainage (SAGD) heavy oil market to improve steam/oil ratio. Unfortunately, upstream flow behavior of ICDs is often ignored; however, flow through the screen-basepipe annulus can induce higher-than-expected pressure losses, and since these losses (not experienced with water) also reduce efficiency and lower performance, special attention should be given to flow-path characteristics. This paper examines pressure drops through the screen basepipe annulus of a direct wrap-on-pipe-type screen before entering the ICD. Using Computational Fluid Dynamics (CFD) simulations, the axial flow through the screen-basepipe annulus of a direct-wrap screen was simulated for three fluids. In addition, several flow rates and multiple-rib-wire-height wire types were used to cover a broad range of operations. The results of the simulations and analyses support the hypothesis that taller ribs would result in a lower pressure drop through the annulus. These simulations and analyses will show that increasing the rib height will cause the overall pressure drop in the annulus to decrease, often by as high a factor as four. These results will help determine optimum screen/base-pipe annulus spacing during screen design.
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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 | 0.001 |
| 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 | 0.001 | 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".