Reducing Pipeline Failures in a Complex System Using Statistical Methods
Bibliographic record
Abstract
Abstract In 2006, pipeline failures in the Swan Hill Unit #1 field, in Northern Alberta, Canada, had increased to a point where they were negatively impacting operating costs, production, and the company’s standing with the provincial regulator. The size and complexity of the field infrastructure, as well as the continuous reactive response to failures, made it difficult to focus efforts and prioritize work in the field. With over 600 pipeline segments, and 1000 km of pipe in the ground, a field specific assessment, inspection, and replacement strategy was necessary. The initial step in this process was a systematic review of the past 40 years of failure and operational data. Statistical methods were then used to determine which risk factors should be used to prioritize the pipelines for immediate and future attention. A field specific risk assessment process was developed which allowed for targeted inspections and pipeline replacements. This paper presents the results of the statistical analysis, how this information was fed into a risk assessment model, the ongoing efforts to improve the accuracy of the model, and the steps that were taken to reduce the number of failures to a third of their 2006 magnitude.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".