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Record W2026095149 · doi:10.2118/60747-ms

Optimizing and Managing Coiled Tubing Frac Strings

2000· article· en· W2026095149 on OpenAlexaboutno aff
S. P. Kazakov, Keith Rispler

Bibliographic record

VenueSPE/ICoTA Coiled Tubing Roundtable · 2000
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsServiceability (structure)Service lifeString (physics)EngineeringService (business)Computer scienceStructural engineeringReliability engineeringMathematicsBusinessMarketing

Abstract

fetched live from OpenAlex

Abstract Multizonal coiled tubing fracturing (CT frac) services have been successfully performed in shallow gas fields in western Canada for more than I year. Properly managing and designing the serviceability of CT strings is critical to these operations. This paper discusses the practical and theoretical aspects of optimizing the frac-string design, changing the equipment geometry of the CT units, and using a CT fatigue model and fatigue-simulation software. The paper also discusses factors affecting the longevity and performance of frac strings, programs for continuously monitoring the service life of the strings, and actions considered for recovering and extending the fatigue life. With a properly designed and implemented comprehensive tubing management program, service operators in western Canada have helped ensure a high degree of personnel safety during fracturing while minimizing the risk of CT failure and maximizing pipe life.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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.

Study designSimulation or modeling
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

Citations8
Published2000
Admission routes1
Has abstractyes

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