Reliability Assessment of Pipeline Third Party Damage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Pipeline failure statistics indicate that mechanical damage caused by third-party excavation represents the largest threat to the integrity of onshore oil and gas pipelines in North America. In 1999, PRCI developed a reliability model that quantifies the pipeline probability of failure due to the 3rd party damage threat. The model employs a fault tree approach comprised of four main elements: the probability of excavation occurring on the pipeline alignment, the effectiveness of damage preventive measures, the probability that the excavation depth exceeds the depth of cover, and the probability that the excavator force is sufficient to fully penetrate the pipe wall. The PRCI model has been implemented by numerous operating companies over the past two decades. Despite this large contribution, there has been a gap in quantitative assessment techniques regarding the effectiveness of the methods used to prevent mechanical damage, and the pipelines resistance to the impact loads applied to pipelines by excavation equipment. In 2020 Enbridge applied this model to its 25,000+ km liquid pipeline system. During implementation numerous learnings and areas for improvement were identified. Correspondingly, the model was expanded to improve consideration of four important 3rd party damage threats that are not currently included within the model: agricultural activity, vehicle crossings, pipeline exposures, and mitigation activities. The results of this updated model showed that the probability of failure’s due to 3rd party damage were generally increased at locations with high population density, agricultural land use, and road crossings, that exhibited shallow cover. It is expected that this updated model will assist in prioritizing the mitigation of various locations that are potentially susceptible to the 3rd party damage threat in alignment with operator expectations. This paper discusses the data gathering steps required for implementation, example probability of failure results, and provides the details of the model updates which may be incorporated by other operators.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it