Maximizing MFL ILI Sizing Confidence and Accuracy Using High-Resolution Field Measurement Data
Bibliographic record
Abstract
Magnetic Flux Leakage inspection tools are generally calibrated on a series of manufactured defects. This has been shown to give good results on a wide range of defects in varying wall thicknesses, velocities and pipeline conditions. Significant improvements in sizing performance can be achieved if sizing algorithms can be optimized on high resolution field data with low uncertainty that more closely reflects the actual line specific corrosion dimensions and profiles. The effects of defect profile can be significant to the MFL signal response. In order to achieve this goal, very high resolution and accurate field measurement techniques are needed to map the combined profile of a significant number of corrosion defects. This paper discusses a process for developing high performance sizing algorithms that consistently better industry standards for MFL sizing performance in areas of high density or complex corrosion in both oil and gas pipelines through the incorporation of high resolution laser scan technology. Complex corrosion may be considered as an area wherein individual corrosions interact together such that they no longer behave as a single corrosion and the MFL response experiences a superposition of leakage signals. A review of the methodology will be discussed and the results demonstrated through case studies from both Enbridge Pipelines Inc. and TransCanada Pipelines Ltd. where high-resolution field data was used as the basis for sizing model optimization.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".