Achieving North American Record for Longest Intelligent Inspection of a Natural Gas Pipeline
Bibliographic record
Abstract
TransCanada was faced with a significant challenge to inspect a 941 km NPS 48 pipeline. The options for the inline inspection (ILI) were multiple segments which would cause an increased cost with new pigging facilities required and a delay to the ILI schedule or attempt to pig the longest natural gas pipeline section in North America. The extraordinary proposal would require a massive 48″ combination Magnetic Flux Leakage (MFL) tool to traverse a high-speed gas pipeline 941km from Burstall, Saskatchewan to Ile des Chenes, Manitoba, Canada. Given the alternative of the installation of 3 additional launcher and receiver stations and the risk to overall project schedule from extended inspection operations, TransCanada took the bold decision to perform an MFL inspection in a single pass. However, as expected, this option created a new set of challenges to guarantee first run success in one of the harshest environments for an ILI tool and in a line where the cleanliness condition was unknown. This last factor, was a critical concern as the volumes of debris that could be collected with the highly aggressive MFL tool brushes, could easily and very quickly have led to very significant debris build up during inspection that at best would likely cause degraded data leading to an unwanted re-run and at worst the possibility of a stuck pig and subsequent retrieval program. From a project perspective either occurance was considered to be mission critical — if either occurred there was no easy solution to collecting the much needed condition data of the pipeline. In July 2017, a successful VECTRA HD GEMINI inspection was completed. This paper discusses the main program risks, mitigation steps taken over and above a standard ILI run. Key considerations and actions taken relating to additional engineering and tool modifications to various components of the inspection vehicle itself will be discussed. Lastly, insight will be given into an extensive smart cleaning program developed with the ILI vendor, using a combination of mechanical cleaning associated and debris level assessment, specifically designed and tailored for the project to ensure that the pipeline was both ready for ILI and that cleaning had reached optimum for ILI so that full, high quality MFL data would be collected the first time.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".