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Record W2095307226 · doi:10.1115/ipc2012-90231

Managing the Threat of SCC in Gas Transmission Pipelines

2012· article· en· W2095307226 on OpenAlexaff
A. D. Batte, R. R. Fessler, James E. Marr, Steve Rapp

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsIntegrity managementPipeline transportHydrostatic testEngineeringWork (physics)Pipeline (software)Forensic engineeringRisk analysis (engineering)Petroleum engineeringOperations managementBusinessMechanical engineering

Abstract

fetched live from OpenAlex

A group of eight gas transmission pipeline operators, responsible collectively for operating over 160,000 miles of pipelines in North America, has participated in a Joint Industry Project (JIP) to examine the current status of Stress Corrosion Cracking (SCC) Threat Management. Many of these operators had previously participated in a JIP addressing the Integrity Management of SCC in High Consequence Areas. Completed in 2006, the JIP developed experience-based guidance for the conduct of hydrostatic testing and excavations, for the assessment of the severity of discovered defects, and for establishing the interval before the next assessment. The outcome was published in ASME STP-PT-011, and formed the basis for proposed revisions to ASME B31.8S. In this second phase of the work, the operational experiences and threat management experiences during the five years since 2006 have been reviewed. From an operational viewpoint, the situation has been very satisfactory; only three in-service failures (ruptures or leaks) due to SCC have been experienced during this period, a considerable reduction compared to the preceding years. However, there is still a legacy of SCC to be managed in older pipelines; for example, 80 near-critical cracks have been removed by hydrostatic testing, and around 100 cracks that would probably have failed a hydrostatic test have been discovered by crack detection ILI. From the threat management viewpoint, a consistent overall framework for addressing SCC is beginning to be established, within which the wide range of operational experience can be addressed using mitigation strategies that are appropriate, proportionate, and timely. Most operators, particularly those with a legacy of SCC in older pipelines, make use of hydrostatic testing. Several now make use of SCC Direct Assessment, following its acceptance as a formal process in around 2005, but mostly for addressing segments with low relative risk of SCC and/or no history of SCC. Many are exploring the application of crack detection ILI; among the JIP members around 45 runs totalling nearly 3000 miles have been completed using EMAT ILI vehicles, and more are scheduled. Almost all the JIP members are using two or more of these approaches in combination as part of their SCC Threat Management strategies. There are areas where the experiences of SCC Threat Management over the last five years point to opportunities for improvement. For SCC Direct Assessment, the use of feedback from excavations to refine the relative rankings for segment prioritisation and dig site selection will become an increasingly important aspect of process improvement. For crack detection ILI, the main issues are the accuracy and reliability of information determining the flaw size and shape for use in predictions of failure pressure and assessments of defect severity. As Threat Management moves from baseline assessment to regular re-assessment, issues that arise include determination of the re-assessment interval, particularly when using SCC Direct Assessment and crack detection ILI. There is also an issue about how best to actively monitor those segments where there is low relative risk and no experience of SCC.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.231
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2012
Admission routes1
Has abstractyes

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