A New Method for the Design and Selection of Premium / Woven Sand Screens
Bibliographic record
Abstract
Abstract Woven metal mesh sand screens, commonly known as premium screens, have been extensively used by the industry. Sand retention testing is often done to evaluate the performance of these screens and establish empirical guidelines for screen size selection. However, these tests are tedious and the results are prone to artifacts and have been used, at best, to correlate trends in sand retention performance with select sand size distribution parameters. A new method incorporating results from numerical modeling in addition to experimental data is presented to estimate the mass and size distribution of the produced solids through premium screens. This method provides a fast, reliable correlation to estimate sand production through premium mesh screens when the size distribution of the formation sand is known. This paper presents results from a wide range of sand retention experiments. In these tests the mass of sand produced and its size distribution over time are measured. Results of three-dimensional, discrete element computer simulations of woven screen geometry placed in contact with granular sand packs of ~100, 000 particles are also presented. Based on both the simulations and the experiments a new method for screen selection is presented. This method is based on a correlation that allows us to use the entire sand size distribution of the formation sand and estimate the mass and size distribution of the produced sand. The method is validated by comparisons with experimental data. A new method and new correlations for estimating the mass and size distribution of produced solids through premium screens is presented. Key differences in sand retention mechanisms between premium and wire-wrapped screens have been identified. The method uses the entire formation sand size distribution (as opposed to a single design point) and has been validated with laboratory tests. The method also helps in screening anomalous test results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".