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Record W4382459953 · doi:10.3390/coatings13071155

Study on the Wear Performance of Polyethylene Inner Lining Pipe under Different Load and Mineralization Conditions

2023· article· en· W4382459953 on OpenAlexaff
Liqin Ding, Lei Wang, Jie Li, Suoping Qi, Wanli Zhang, Yuntao Xi, Keren Zhang, Shanna Xu, Haitao Liu, Lei Wen, Xinke Xiao, Jiangtao Ji

Bibliographic record

VenueCoatings · 2023
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Regina
FundersNatural Science Basic Research Program of Shaanxi ProvinceXi'an Shiyou UniversityState Key Laboratory of TribologyChina Scholarship CouncilNanyang Institute of TechnologyNational Natural Science Foundation of China
KeywordsPolyethyleneMaterials scienceScanning electron microscopeComposite materialCoatingService lifeMineral oilAlloyFriction coefficientMetallurgy

Abstract

fetched live from OpenAlex

This study conducted pin disc friction and wear performance tests on polyethylene-lined oil pipes and four types of centralizing materials (45# steel, nylon, polytetrafluoroethylene (PTFE), and surface alloy coating) in oil fields. The friction coefficient and wear rate were tested, and the wear mechanism was analyzed using scanning electron microscopy (SEM) and three-dimensional confocal microscopy. Using a combination of experimental testing analysis and theoretical research, a comprehensive evaluation of the current wellbore centering and anti-wear technology for oil was conducted. The experimental results indicate that the usage limit of polyethylene-lined oil pipes is 400 N, and compared to metal oil pipe materials, the wear rate of both stabilizing material and tubing material is lower, indicating that it has a certain service life. From the perspective of testing load, taking into account the factors of friction coefficient and wear rate, the recommended sequence of straightening material for polyethylene lined oil pipes is (1) surface alloy coating, (2) nylon, (3) PTFE, and (4) 45# steel.

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 score0.176
Threshold uncertainty score0.263

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 head0.020
GPT teacher head0.229
Teacher spread0.208 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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