A New Three-Layer Model for Gravel Packing Applications in Horizontal Wells
Bibliographic record
Abstract
Abstract In [1] we have developed a modified three-layer model for solid-liquid flow in horizontal pipes, which overcomes the limitations of previous mechanistic models. The steady-state model predicts the pressure loss, critical velocity, concentration profile in the heterogeneous layer, mean heterogeneous layer and moving bed layer velocities, and bed layer heights for each set of parameters. The steady-state model predictions show very good agreement with experimentally measured results in the literature. In this paper we extend the steady-state three-layer model to annular geometries and apply it to the design of open-hole gravel packing operations, in the typical parameter ranges of gravel packing operations for alpha wave placements. Alpha wave design is a key factor for successful gravel packing, and the models typically used are either based on small-scale experiments or are not specifically developed for gravel packing, e.g. cuttings transport models. In gravel packing the hydraulic configuration is slightly different. We explain how bed height is selected via coupling between the inner and outer annuli and from the outer annulus hydraulics. We investigate the effects of important parameters such as the slurry flow rate, mean solids concentration, wash pipe diameter, etc. on gravel packing operations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".