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Record W2939847887 · doi:10.1080/10408436.2019.1588706

Unveiling Corrosion Behavior of Pipeline Steels in CO<sub>2</sub>-Containing Oilfield Produced Water: Towards Combating the Corrosion Curse

2019· article· en· W2939847887 on OpenAlexaff
Peter Adeniyi Alaba, Sunday Adedigba, Sunday Felix Olupinla, Oluranti Agboola, Samuel Eshorame Sanni

Bibliographic record

VenueCritical reviews in solid state and materials sciences/CRC critical reviews in solid state and materials sciences · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsMemorial University of Newfoundland
FundersUniversiti Malaya
KeywordsCorrosionBrineProduced waterSupercritical fluidPipeline transportMaterials scienceCarbon dioxideCarbon steelMetallurgyCarbonic acidNatural gasChemical engineeringPetroleum engineeringChemistryEnvironmental scienceEnvironmental engineeringGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

The presence of carbon dioxide (CO2) in oilfield produced water, which could be natural or intentionally injected component in oil and gas production, is extremely corrosive to pipelines and tubings. Oilfield produced water is mainly brine solution comprising organic acids, purely condensed water as well as aggressive ions like SO42−, Cl− and dissolved acid gases (like CO2). CO2 induced corrosion in this media is more detrimental than corrosion induced by HCl. This study critically unveiled the CO2 corrosion behavior of steel in oilfield produced water. The subjects covered include the chemistry of CO2 in water, supercritical CO2 condition, computational fluid dynamics (CFD) and corrosion control. Corrosion resistant alloy has been proved to exhibit remarkable corrosion resistance due to formation of strong protective film that can prevent localized corrosion. Moreover, the use of viable surface-active compounds, which are constituents of crude oil is a promising strategy for pipeline steel corrosion mitigation.

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)0.021
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.058
GPT teacher head0.383
Teacher spread0.324 · 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.

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

Citations26
Published2019
Admission routes1
Has abstractyes

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Same venueCritical reviews in solid state and materials sciences/CRC critical reviews in solid state and materials sciencesSame topicCorrosion Behavior and InhibitionFrench-language works237,207