Numerical Simulation of Wellbore Dynamics During Underbalanced Perforation
Bibliographic record
Abstract
Abstract The main objective of perforating is to connect the wellbore to the reservoir zone by creating tunnels through casing and damaged zone into the undamaged rock. The process of detonating perforation charges generally causes damage due to crushed rock material around the perforation tunnels. There are different methods used to mitigate damage due to perforation, such as static and dynamic underbalanced perforation. In static underbalanced perforation, conventional high shot density perforating guns are run in underbalanced conditions in order to have clean-up after perforation. In dynamic underbalanced perforation, a specific gun design associated with gun puncher charges provides dynamic underbalanced conditions across the perforated interval immediately following charge detonation in order to efficiently clean up each single perforation tunnel from crashed rocks and debris. Using deep penetrating charges followed by a dynamic underbalanced can improve well productivity since it mitigates the damage related to perforation. Dynamic underbalanced perforation removes the crushed rocks and debris from the tunnels using a sudden pressure drawdown across the perforated interval immediately after detonating the charges. This study is intended to numerically simulate the dynamic underbalanced conditions after perforation in order to have a better understanding about the phenomenon during perforation process as well as perforation cleanup. The effect of wellbore fluid type on dynamic underbalanced behavior has been simulated to investigate efficiency of dynamic underbalanced in different wellbore conditions, and also three field examples are also shown to verify the simulation results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".