Challenges in Planning, Installation, and Operation of Coiled-Tubing Drilling Equipment on a North Sea Platform
Bibliographic record
Abstract
Abstract Coiled-tubing drilling (CTD) is a well-established, common application in some areas, particularly Western Canada. However this practice has not grown to similar activity levels in the North Sea. Two CTD projects have been carried out on UK North Sea platforms since 1999 and it is over 4 years since the most recent operation. As a result of the sustained high oil price, North Sea operators are looking for methods of sidetracking existing wells to access isolated or stranded oil reserves. The condition of some North Sea drilling rigs is such that a significant financial investment is required if they are to return to conventional drilling. CTD is one option that enables operators to access these reserves, without the need to recommission the original drilling facility. The extremely low activity levels of CTD in the UK has led to the challenge faced by service companies of supplying suitable equipment and experienced personnel, to safely and effectively manage and perform a CTD operation. This paper discusses the challenges faced and some solutions developed for a recent CTD operation carried out on the Gannet ‘A’ platform, located in the Central North Sea. In addition to the challenges mentioned above, to deliver a successful well, several new technologies and equipment were incorporated into the application of CTD to the Gannet field.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".