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Record W4235390369 · doi:10.2523/97601-ms

Significant Reduction of Imperfection Depth Tolerance During Ultrasonic Inspection of Oil Country Tubular Goods

2005· article· en· W4235390369 on OpenAlexaboutno aff
Hilton Prejean, Dave Mason, Christy Von der Ahe, Randy McGill

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCitationDownloadComputer scienceLibrary scienceWorld Wide WebEngineering

Abstract

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Significant Reduction of Imperfection Depth Tolerance During Ultrasonic Inspection of Oil Country Tubular Goods Hilton Prejean; Hilton Prejean Tuboscope Search for other works by this author on: This Site Google Scholar Dave Mason; Dave Mason Shell Oil International E&P Search for other works by this author on: This Site Google Scholar Christy Von der Ahe; Christy Von der Ahe Shell Oil International E&P Search for other works by this author on: This Site Google Scholar Randy McGill Randy McGill Shell Oil International E&P Search for other works by this author on: This Site Google Scholar Paper presented at the SPE High Pressure/High Temperature Sour Well Design Applied Technology Workshop, The Woodlands, Texas, May 2005. Paper Number: SPE-97601-MS https://doi.org/10.2118/97601-MS Published: May 17 2005 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Prejean, Hilton, Mason, Dave, Von der Ahe, Christy, and Randy McGill. "Significant Reduction of Imperfection Depth Tolerance During Ultrasonic Inspection of Oil Country Tubular Goods." Paper presented at the SPE High Pressure/High Temperature Sour Well Design Applied Technology Workshop, The Woodlands, Texas, May 2005. doi: https://doi.org/10.2118/97601-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE High Pressure/High Temperature Sour Well Design Applied Technology Workshop Search Advanced Search AbstractThis paper summarizes the results of a series of demonstrations used to evaluate the effect of various conditions on conventional automated full-length ultrasonic inspection of heavy wall tubular goods using reference indicators with depths significantly less than 5% of the specified wall thickness.IntroductionNondestructive examinations are relied upon to know the quality of pipe used to perform great tasks. Ultrasonic inspections are the Industry's most reliable and critical examination currently being performed on Oil Country Tubular Goods. The minimum defects searched for are 5% of the specified wall thickness. For example 9 5/8? casing weighing 53.50 pounds per foot with a specified 0.545? wall thickness would be inspected with 0.027? (5%) deep reference notches normally 1? in length. When considering string designs for wells with high pressures and high temperatures, the wall thicknesses required are over 1.000?, which is double the typical API specified size. When using the API recommended notch depth of 5% for critical applications in thicker wall pipe known stress risers could be allowed. The past has shown failures could occur with flaw depths as little as 0.025? regardless of the specified wall thickness they propagate in. This does provide a greater inspection challenge for High Pressure High Temperature wells.BackgroundThis study was performed with an array of multiple transducers applying the ultrasonic inspection technique. Each tube is scanned full length on the outside diameter surface in the longitudinal and transverse orientations. Keywords: amplitude, pipe, upstream oil & gas, operator, wall thickness, society of petroleum engineers, notch, oil country tubular goods, screen height amplitude, imperfection depth tolerance This content is only available via PDF. 2005. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations0
Published2005
Admission routes1
Has abstractyes

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