An optimal model for predicting the productivity of perforated vertical HTHP wells
Bibliographic record
Abstract
Abstract As perforation helps regulate inflow from the reservoir, the optimisation of the perforating parameters is critically important in increasing productivity. In this paper, a one‐dimensional reservoir/wellbore coupling model is presented to predict the productivity of perforated vertical high‐temperature‐high‐pressure (HTHP) wells. A comprehensive perforation skin factor is developed in the model, which takes into consideration many reservoir and perforation parameters, such as length, density, radius, anisotropy, phasing, partial penetration, perforation damage and formation damage. An optimisation strategy is established to investigate the effect of the perforation parameters on productivity based on the skin factor of a well. The model is applied to a 7100 m deep HTHP gas well to demonstrate its use in determining optimal perforation parameters. The simulation results demonstrate that an optimisation strategy improves the perforated well inflow performance. Then, a sensitivity analysis is conducted to investigate the effect of reservoir and perforation parameters on 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".