A Practical Application to Calculating Corrosion Growth Rates by Comparing Successive ILI Runs From Different ILI Vendors
Bibliographic record
Abstract
Corrosion growth rates are an essential input into an Integrity Management Program but they can often be the largest source of uncertainty and error. A relatively simple method to estimate a corrosion growth rate is to compare the size of a corrosion anomaly over time and the most practical way to do this for a whole pipeline system is via the use of In-Line Inspection (ILI). However, the reported depth of the anomaly following an ILI run contains measurement uncertainties, i.e., sizing tolerances that must be accounted for in defining the uncertainty, or error associated with the measured corrosion growth rate. When the same inspection vendor performs the inspections then proven methods exist that enable this growth error to be significantly reduced but these methods include the use of raw inspection data and, specialist software and analysis. Guidelines presently exist to estimate corrosion growth rates using inspection data from different ILI vendors. Although well documented, they are often only applicable to “simple” cases, pipelines containing isolated corrosion features with low feature density counts. As the feature density or the corrosion complexity increases then different reporting specifications, interaction rules, analysis procedures, sizing models, etc can become difficult to account for, ultimately leading to incorrect estimations or larger uncertainties regarding the growth error. This paper will address these issues through the experiences of a North American pipeline operator. Accurately quantifying the reliability of pipeline assets over time requires accurate corrosion growth rates and the case study will demonstrate how the growth error was significantly reduced over existing methodologies. Historical excavation and recoat information was utilized to identify static defects and quantify systemic bias between inspections. To reduce differences in reporting and the analyst interpretation of the recorded magnetic signals, novel analysis techniques were employed to normalize the data sets against each other. The resulting uncertainty of the corrosion growth rates was then further reduced by deriving, and applying a regression model to reduce the effect of the different sizing models and the identified systemic bias. The reduced uncertainty ultimately led to a better understanding of the corrosion activity on the pipeline and facilitated a better integrity management decision process.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".