A new assessment of perforation skin factor for vertical perforated wells in near-wellbore region
Bibliographic record
Abstract
Abstract The perforating technique is one of the well completion methods and a final stage that helps connect reservoir formation to wellbore during hydrocarbon production. The present work aimed to determine the effect of the perforated casing completion on the pressure gradient and perforation skin factor in the vertical near-wellbore region. This work presented a novel experimental approach for studying the effect of perforation parameters on hydrocarbon production by creating a prototype representing the near-wellbore region. The study conducted extensive laboratory testing to create two prototype artificial samples for a cylindrical near-wellbore region, open hole, and perforated casing sample. An experimental test was carried out using a geotechnical radial flow setup to measure the differential pressure in the two samples; the single-phase (water) was radially injected into the core sample within the same flow boundary conditions. Numerical simulation and statistical analysis were used to expand the investigation of the effect of the dimensions and distribution of perforations on the perforation skin factor and the pressure gradient in the cylindrical near-wellbore region. The results showed a clear view of the effect of the perforations’ parameters on the pressure gradient in the vertical near-wellbore region. In addition, two novel correlations were produced from statistical analysis that simplified the estimation of the perforation skin factor in the perforated casing completion. This study will help to clarify and understand the effect of perforation parameters on well productivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".