Probabilistic Performance Assessment of Fiber Optic Leak Detection Systems
Bibliographic record
Abstract
Leak detection systems (LDSs) play a major role in enhancing reliability and operability of oil and gas pipelines. They have the functional capabilities to detect, locate, and quantify leaks before they can cause devastating effects to the environment and operation. The performance of LDSs is typically influenced by three different types of failures that may have severe consequences: delayed detection, missed detection, and false detection of a leak. The consequences of these failures lead to extensive financial losses. For example, missed detection leads to oil spills and exposes operating companies to financial risk and destroyed image while false detection results in unnecessary deployment of personnel and equipment. To maintain safety of personnel and the environment and ensure operation continuity, LDSs should be assessed regularly. To fulfill this need, a probabilistic performance assessment scheme based on limit state approach for fiber optic LDS has been developed. The inherent uncertainties associated with leak detection and reporting capabilities are modeled to determine the LDS detection failure probability that combines two failure events: missed detection and delayed detection. Moreover, the probability of false detection is derived in terms of the lowest detectable change, the threshold. These three parameters establish the basis for an overall assessment scheme that can be used to predict the performance of the LDS. The proposed assessment scheme has been applied to a case study to demonstrate its usefulness and feasibility.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".