Incorporating Risk-Informed Methodologies to Complement Deterministic Integrity Decision-Making in the Gas Industry
Bibliographic record
Abstract
Abstract This paper illustrates the practical application of risk-informed methodologies to complement existing deterministic methods for managing the integrity of gas-carrying assets. This approach aims to enhance the understanding of uncertainty and its role in decision-making while offering a supplementary lens for managing asset condition. It can be particularly relevant when managing the safety and reliability of hydrocarbon-carrying assets in an evolving energy landscape, where additional quantification and justification may become necessary for driving toward optimized remediation or replacement decisions. Historically, deterministic methods were generally sufficient to inform decisions when managing asset integrity threats. Though such approaches remain relevant, they can present limitations, especially when effective systemic prioritization is required for complex systems encompassing dissimilar assets, such as distribution and transmission piping, various facilities, and underground storage assets. More advanced data-driven models utilizing multiple risk assessment techniques can benefit operators in these cases, particularly as more industry codes and standards evolve accordingly. The authors of this paper highlight how they successfully adopted this approach in the Integrity Department at Enbridge Gas Inc. (EGI) in Ontario, Canada. They do so by describing the team’s comprehensive decision-making lifecycle and providing practical illustrations of how risk-based evaluations were layered over deterministic outcomes to better define asset condition and optimize mitigation strategies. In their first case study, they examine the overlay of the two approaches for an urban distribution network servicing tens of thousands of natural gas customers. In this illustration, incorporating and quantifying data uncertainty in probabilistic computations results in divergent conclusions between the two assessment streams, an outcome explored in the paper. In their second example, the authors describe a recent application of probabilistic risk evaluations following traditional deterministic analyses of axial and circumferential magnetic flux leakage (MFL-A and MFL-C) in-line inspection (ILI) data on a transmission pipeline. Finally, they present a third application to exemplify the interconnectedness between the deterministic and risk methodologies when prioritizing integrity decisions on underground gas storage assets. The advantages and disadvantages of each method are discussed to underline the criticality of using both approaches concurrently to optimize the comprehensiveness and effectiveness of decision-making.
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How this classification was reachedexpand
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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".