Development of an Index-Based Risk Model for All Liquid Hydrocarbon Facilities
Bibliographic record
Abstract
Enbridge Pipelines Inc. manages over 1200 liquid hydrocarbon facilities across Canada and the United States, including both mainline facilities and gathering system facilities. The range of facilities managed by Enbridge includes terminals, pump stations, valve sites, scraper trap sites, battery sites, and densitometer sites. Evaluation of risk at facilities is a requirement of 49CFR195 (2) for liquid mainline facilities located in the United States and a requirement of CSA Z662 (3) for NEB regulated mainline facilities in Canada. Enbridge has developed an index risk model that is modular in nature to evaluate risk at various liquid facilities. The index risk model provides a relative risk ranking of facility risk, so the risk values obtained are comparative numbers. Facility risk values may be compared with all facilities within the System, or between similar types of facilities, such as terminals. The modular nature of the facility risk model permits the evaluation of all applicable assets within a given facility. There are three likelihood modules and eight consequence modules within the facility risk model that combine to create a risk value using Equation 1: Risk=Likelihood×Consequence(1) The modular indexed risk model has proven to be an effective means of evaluating facility risk. The modular approach meets the requirements of 49CFR195 and the Onshore Pipeline Regulations (OPR 1999) that references CSA Z662 for the risk assessment of facilities associated with mainline pipe. The modular approach can be applied to all facilities regardless of regulatory requirements and can assist in the prioritization of risk control strategies at facilities. As this approach provides a whole risk view of facilities rather than focusing solely on leaks and spills, the risk model may identify systemic areas of opportunity within the organization. This paper provides a review of the development of a modular index facility risk model for liquid operations. Challenges and successes faced during the development and execution of the model are discussed.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".