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Record W4220776786 · doi:10.2118/209039-ms

Inhibition Testing with High Strength Quench and Tempered Coiled Tubing

2022· article· en· W4220776786 on OpenAlexaboutno aff
Kevin J. Elliott, Chris Rentas, Jamie Fenwick, Jason Hayworth, John D Albaugh, William Colt Ables, Woitas Tyler, Stang Ben

Bibliographic record

VenueSPE/ICoTA Well Intervention Conference and Exhibition · 2022
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCoiled tubingMicrostructureSour gasHomogeneousMaterials scienceMechanical engineeringEngineeringComposite materialWaste managementPhysicsNatural gasThermodynamics

Abstract

fetched live from OpenAlex

Abstract High strength quenched and tempered coiled tubing (CT) has been developed and commercialized as far back as the 1990s, however in recent years it has grown to be a substantial, if not the main, type of CT in many markets. Testing programs for sour environment compatibility using inhibition exist for similar strength levels of conventionally produced coiled tubing and has been used as justification for utilization of Q&T coiled tubing with existing CT equipment and operations in sour environments. Quenched and tempered coiled tubing is given final heat treatments which yield a more homogeneous microstructure than conventional products. The field performance of this homogenized product has generally shown that the quenched and tempered tubing performs better in sour environments. This paper then explores the existing body of work with respect to accepted operational guidelines and sour gas testing of coiled tubing. A test program for 140-grade quenched and tempered coiled tubing was developed to explore sour compatibility using an inhibition system. The development and test results of the testing program are described in this paper. The paper concludes with field history of high strength quenched and tempered coiled tubing in Western Canada operations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.879

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 head0.013
GPT teacher head0.197
Teacher spread0.184 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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