Advancements in Screen Testing, Interpretation and Modeling for Standalone Screen Applications
Bibliographic record
Abstract
Abstract Slurry type sand retention tests (SRT) that simulate gradual rock failure around the wellbore have been widely used in the industry to evaluate the performance of sand control screens for standalone screen (SAS) applications. Using the test results, screen selection is generally done based on the relative ranking of screen performances rather than absolute performance. A recent paper by Chanpura et al. (2011) highlighted the drawbacks of the current practices in slurry type SRT procedures and proposed a new testing and interpretation methodology. Another recent work by Mondal et al. (2010) proposed simulation methods and results that, to the best of our knowledge, modeled screen performance numerically for the first time and presented comparisons to physical experiments. However, the approach used by Mondal et al. considers cases where hole collapse occurs on wire wrap screens and simulates "prepack" testing as opposed to slurry type tests considered in this work. In this paper, we review the recent advancements in screen testing, interpretation and modeling for standalone screen applications, and present an analytical as well as a statistical (Monte Carlo) approach for prediction of sand production through sand screens with slot geometry. We show that the proposed methods can estimate both mass and size distribution of the produced solids in a slurry type SRT taking into account the full particle size distribution (PSD) of formation sand for wire wrap screens. Simulations show that once the slot opening is covered by particles bigger than the slot opening, sand production becomes negligible unless there is a true "fines" problem, which is characterized by a bimodal size distribution. The effect of slot size variation in screen coupons on sand production demonstrates the importance of proper quality control or at least accurate determination of slot sizes in these tests. The proposed methods can be used to estimate sand production in slurry type SRT for different screen sizes and thereby enable screen size selection based on defined acceptable level of sand production. Final screen selection can be confirmed through a sand retention test.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".