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Record W2476446836 · doi:10.5006/c2016-07892

Establishing Operating Envelops for Material Susceptibility to Environmentally Assisted Cracking in Thermal Oil Sands Operation

2016· article· en· W2476446836 on OpenAlexaff
Brendan Crozier, Tesfaalem Haile, Danielle Kiesman, Haralampos Tsaprailis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsAlberta Innovates
Fundersnot available
KeywordsCrackingMaterials sciencePetroleum engineeringThermalEnvironmentally friendlyWaste managementEnvironmental scienceComposite materialGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract The defined environmental and material limits specified in ANSI/NACE MR0175/IS015156:2009 restrict the use of austenitic stainless steel alloys under sour conditions typically encountered in thermal in-situ oil sand operations. In this study, C-ring testing was conducted to evaluate the susceptibility of an austenitic stainless steel to conditions that simulate a severe downhole environment; i.e., 230 °C, a pH of 3.5, and variable chloride concentrations and H2S partial pressures. The results indicated that after 720 hours of exposure, the material did not show any cracking when tested at 10,000 ppm Cl- and 1,000 kPa H2S, and at 50,000 ppm Cl- and 100 kPa H2S; conditions well outside of the environmental limitation imposed by ANSI/NACE MR0175/IS015156:2009. Cracking was observed on the specimens when the chloride concentration was increased to 30,000 ppm at 1,000 kPa H2S, and at 10,000 ppm chloride when the partial pressure of H2S was increased to 3,000 kPa. Increasing the partial pressure of H2S tended to produce deeper cracks in the specimens, while increasing the chloride content increased the number of cracks on the specimens. Increasing the chloride concentration was linked to higher corrosion rates in the specimens due to its scale destabilizing effects.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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