Design Optimization of Slotted Liner Completions in Cased and Perforated Wells: A Numerical Skin Model
Bibliographic record
Abstract
Abstract Several parameters affect the skin factor of the cased and perforated (C&P) wells completed with slotted liners. Existing skin factor models for slotted liners account for such factors as the flow convergence, pressure drop and partial production but neglect phenomena such as partial plugging of the screen or near-wellbore permeability alterations during the production. This paper discusses these factors and incorporates them into a skin model using a finite volume simulation. The finite volume analysis evaluates the skin factor as a result of pressure drop in the gap between the casing wall and the slotted liner. This skin model accounts for: 1) the perforation density and phasing, 2) slotted liner specifications, and 3) different amount of sand accumulation in the annular space between the casing and the sand screen. A semi-analytical pressure drop model is also linked to the numerical model to incorporate the skin factor due to flow convergence behind the perforations. The results of finite volume analysis reveal that a low perforation density would behave close to the open-hole completion for sand-free casing-liner annular space. Conversely, pressure drops were found to be significant for a partially or totally filled space. Additionally, it was found that the optimum completion design occurs if the slotted liner joints are in line with the casing joints. Besides, a partially perforated casing or a partially open sand screen increases the distance fluids have to travel in the annular space and intensifies the skin factor. This paper provides skin models derived for vertical and perforated wells completed with slotted liner sand screens using the finite volume simulations. Each part of the model has been verified against existing numerical models in the literature. The model improves the understanding of flow performance of the sand screens and skin factor, which in turn leads to a better design of sand control completions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".