Extreme Overbalance Perforating and One-Trip Perforate and Gravel Pack-Combination of Two Techniques for Successful High Rate Gas Well Completions in the Ha'py Field
Bibliographic record
Abstract
The Extreme Overbalanced Perforating (EOB) technique has been successfully applied in a variety of locations around the world both in ‘hard rock’ and ‘soft rock’ formations. In certain applications, it offers definite advantages over other perforating techniques. One-Trip Perforating and Gravel Pack technology also has been successfully applied at various locations with definite advantages over other ‘soft rock’ cased hole completion techniques. Prior to the completion of three wells in the Ha'py field, these two technologies had never been applied concurrently.This paper describes the teamwork that was required between the operating company and the service providers to properly combine these methods and ensure their successful application in the field. The authors outline some of the obstacles encountered and describe how the tools and procedures were designed for success.During the three well completions in the Ha'py field, some problems were encountered that required modifications to operations. The lessons learned from these problems and how they were applied to subsequent operations are also addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".