Application of Risk and Reliability in Designing Facility Site Containment
Bibliographic record
Abstract
Abstract Enbridge is North America’s premier energy infrastructure company delivering the energy people need and want. Enbridge’s business value is asset intensive. With over 200 onshore liquids pipelines facility assets, operational safety and environmental protection are always top priorities. The embedment of risk management practices in business decisions is an effective way to appropriately optimize asset performance while avoiding catastrophic impacts to people and the environment. This includes understanding and managing these risk events and establishing barriers to prevent the impact. Facility site containment is an independent protection layer that mitigates the consequences of a spill. The United States Environmental Protection Agency and the National Fire Code of Canada provide requirements to contain overland flow of a spill from liquids pipelines facility assets. Although there are specific volumetric requirements for spill containment for facility tanks, there are no specific volumetric requirements for spill containment for pipeline facility assets such as pumps, valves, etc. Industry typically employs an index-based approach to determine the specific design volumes using catastrophic rupture volumes and facility location. This approach has several shortcomings, including design inadequacy, inconsistency, and challenges with scalability. A risk-based approach is appropriate in determining the required site containment volume based on oil spill history, facility assets, and environmental sensitivities. A probabilistic model can be created using historical facility oil spill data based on the Pipeline and Hazardous Materials Safety Administration’s (PHMSA’s) facility incident database to estimate the likelihood of a given size of release occurring. If available, company oil spill history can also be used or integrated with the PHMSA dataset. Combining the likelihood of the size of release occurring with the estimated consequence (by accounting for the volume of a release and the environmental sensitivity at the release location), it is possible to evaluate the risk of a release. This estimation of risk can then be leveraged to support facility site containment design to manage the risk to an acceptable level. By informing facility site containment with volumetric requirements using reliability and consequence models and risk management principles, an organization can prudently balance pipeline safety and capital constraints to comply with federal regulations. This paper demonstrates this approach and describes: • The value of available data and model development • Reliability modeling and consequence assessment • Risk-informed decision-making • Future model enhancements
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".