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Record W2609037030 · doi:10.5006/c2013-02292

Environmental Stress Cracking of High Density Polyethylene Pipes in Alkali Surfactant Polymer Enhanced Oil Recovery Floods

2013· article· en· W2609037030 on OpenAlexaff
Li Zhong, Colin Dooley, Y. Frank Cheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsPetroleum Technology Alliance CanadaUniversity of Calgary
Fundersnot available
KeywordsEnvironmental stress crackingMaterials scienceCrackingPolymerPolyethylenePulmonary surfactantStress corrosion crackingStress (linguistics)Alkali metalHigh-density polyethyleneComposite materialPetroleum engineeringMetallurgyCorrosionChemical engineeringChemistryGeology

Abstract

fetched live from OpenAlex

Abstract With the increasing use of high density polyethylene (HDPE) pipes in enhanced oil recovery process, environmental stress cracking (ESC) poses a threat to the integrity of pipes in alkali surfactant polymer (ASP) flooding. In this work, the ESC susceptibility of three types of HDPE material was investigated by ASP soaking, tensile testing, pre-notched specimen stressing tests and surface characterization. The results demonstrate that the susceptibility of HDPE to ESC depends on the ASP concentration, stress and the type of materials. The ASP solution soaking decreases the elongation of the material, especially at an elevated temperature. Furthermore, the percentage of the cracked specimens over the total number of specimens in ESC tests increases with the ASP concentration. The PE 100+ has the highest resistance to ESC compared to two other HDPE materials. PE 100+ exhibits a higher crystallinity and disentanglement energy than 4710 and 3608. The failure specimens exhibit swelling phenomenon and the formation of crazes on the specimen surface.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.181
Teacher spread0.178 · 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 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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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