Enhancement of the Completion Efficiency of Perforation Tunnels in Petroleum Wells
Bibliographic record
Abstract
The objective of perforating is to maximize well productivity by establishing good connectivity between the wellbore and formation. The conventional method of perforation — perforation by shooting (PS) — cannot achieve expected wellbore productivity due to a region of reduced permeability around the perforation tunnel. In this study, it has been established that permeability is decreased in the range of 30%–75% due to the implementation of the PS technique compared to the openhole completion. As a result, a new perforation technique — perforation by drilling (PD) — has been proposed in this paper. To simulate a perforated completion, cylindrical sand samples (0.0572 in OD) consolidated with cement with varying porosity were prepared. These samples were perforated (0.0136 m ED) by the PS, PD and Casting techniques. Perforations created by the Casting techniques are considered the ideal, openhole perforation tunnel. Fluid flow rates and differential pressure across the perforated samples were measured for three different types of samples using “Geotechnical Digital System” triaxial testing set-up. Fluid flow rates with changing differential pressure and finally pressure build-up data with time indicates the PD technique can achieve better wellbore productivity compared to the PS technique. Results indicate that at 100 kPa differential pressure the PS, PD and Casting techniques can achieve 0.20 mL/s, 0.65 mL/s and 1.00 mL/s fluid flow rates respectively across a sample.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".