Inline Inspection Decisions and Results Using an Integrated Technology for a Baseline Inspection Program on a Large Diameter High Pressure Gas Transmission and Interconnect System
Bibliographic record
Abstract
The Alliance Pipeline System consists of 2664 Km of NPS 36 high pressure transmission pipeline and 339 Km of NPS 42 high pressure transmission pipeline. The mainline systems are connected by lateral and interconnect pipeline sections ranging in diameter from NPS 4 to NPS 24. The pipeline system extends from northeast British Columbia to Illinois. The Trans border nature of the pipeline means that it needs to satisfy both the Canadian and US regulatory requirements related to pipeline integrity management. Part of the approval process for the pipeline system was that it had to be inspected on a regular basis with a baseline inspection program to be initiated upon start-up of the pipeline system in 2000. This paper outlines some of the unique challenges the high pressure transmission pipeline presented to both the operator and the inline inspection (ILI) vendor in developing a successful in line inspection program. It discusses the vendor selection criteria used by the pipeline operator and the design process undertaken by the ILI Vendor to meet the requirements of this unique pipeline system. By the end of 2004, the mainline sections in Canada and the US will have been inspected as well as most of the smaller diameter interconnect and lateral system. Results are presented from the ILI inspection of both the high pressure system and the smaller diameter system. While the inspections have used Magnetic Flux leakage (MFL) Technology to detect metal loss features, the use of integrated technology in particular the inertial navigation system aboard the vendor’s inspections tools has allowed geometric features to be detected as well. Lessons learned from both the operator and the ILI Vendor will be presented on the execution of the inline inspection program as well as discussion on ways of ensuring that the ILI process goes smoothly and if not how to address these concerns.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".