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Record W2428564949 · doi:10.5006/c2016-07888

Prediction of Corrosion Occurrence by Simulating Multi-phased Fluid Flow in Downhole Tubulars in SAGD Production. I. Vertical and Horizontal Segments

2016· article· en· W2428564949 on OpenAlexaff
Qiang Li, Haitao Hu, Y. Frank Cheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCorrosionPetroleum engineeringFlow (mathematics)Fluid dynamicsMaterials scienceGeologyEnvironmental scienceGeotechnical engineeringMechanicsMetallurgyPhysics

Abstract

fetched live from OpenAlex

Abstract Computational fluid dynamics (CFD) analysis of multi-phased fluid flow was conducted on vertical and horizontal segments of steam-assisted gravity drainage (SAGD)/ CO2 co-injection tubulars. The fraction of water volume and wall shear stress profiles were determined, and the correlation with corrosion occurrence was assessed. Results show that there is a larger likelihood for corrosion to occur on horizontal tubulars than vertical ones due to the favorable water condensation and a higher wall shear stress at the bottom of the horizontal tubular under equivalent operating conditions. A mechanistic model was developed based on the fluid flow simulation and electrochemical corrosion mechanism, enabling prediction of tubular corrosion as a function of various operating parameters, including temperature, total pressure, CO2 partial pressure, flow rates and tubular size. The results support basis of recommendations toward the parametric effects on corrosion of steel tubulars, and prediction of corrosion rate of tubulars under SAGD/ CO2 co-injection conditions.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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