Construction and Installation of X100 Pipelines
Bibliographic record
Abstract
The developments of gas fields are increasingly occurring in more remote locations and further from the prime gas demands. Pipeline activity continues to focus on arctic regions of both Canada and the United States. Cost effective solutions to these challenges can be found through innovative technology and the use of higher pressure and higher strength pipelines. TransCanada and its partners have been involved in a series of technology programs on high strength steels, particularly X100, that are focussed on its application for high pressure long distance pipelines. In order to evaluate this technology two field installations of X100 have been performed on the TransCanada system within Alberta. These installations have evaluated the summer and winter construction aspects of X100 pipelines. This paper will describe the work performed to enable the two projects to occur, and the results of the installation. The summer project occurred on the Westpath loop, and consisted of 1 km of NPS 48 by 14.3 mm X100 and was installed in September 2002. The winter project was on the Godin Lake Loop and consisted of 2 km of NPS 36 by 13.2 mm X100 and will be installed in February 2004. The paper will describe the approach taken to the pipe development and the properties required, the requirements for a strain-based design, the fracture control plan, and the welding requirements. Discussion will cover the installations and construction and the conclusions in terms of future projects. The role of code and regulatory bodies in the successful implementation will be covered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.009 | 0.002 |
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".