Single and Multi-Phase Flow Loop Testing for Characterization and Optimization of Flow Control Devices Used in SAGD: The Effect of Viscosity and Gas-to-Liquid Ratio on Tool Performance
Bibliographic record
Abstract
ABSTRACT The design of Flow Control Devices (FCDs) requires performance data of an FCD’s internal nozzle under a wide range of flow scenarios. The current study specifically considers the effect of nozzle diameter and wall profile on the induced pressure loss, and subsequently the recovery performance of an FCD. For this study, a flow measurement facility is developed to test the performance of different orifice/nozzle geometries. The flow of single- and two-phase fluid at various flow rates and mass fractions, is experimented. The pressure drop data from the experiments is used to produce performance curves that characterize pressure loss across the geometries. The pressure loss for two-phase flows are compared to their single-phase counterparts to characterize the performance of the tested geometries in the two scenarios. A detailed protocol for performance testing of FCDs is followed as per Advanced Well Equipment Standard (AWES: recommended practice3362). The testing protocol was utilized to characterize the performance of different FCDs geometries under single- and two-phase flow conditions. The results showed the pressure loss characteristic obtained from the flow loop experiments match the corresponding theories. The study has thus provided promising results for the successful application of direct flow loop testing to obtain reliable data which can be used in FCD design, performance investigation, and reservoir simulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".