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Record W2507614716 · doi:10.5006/c2013-02339

Aggressive Corrosion Associated with Salt Deposits in Low Water Content Sour Gas Pipelines

2013· article· en· W2507614716 on OpenAlexaff
Wes Litke, Joe Bojes, P. Blais, J. A. Lerbscher, Wellington Wamburi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsBaker Hughes (Canada)
Fundersnot available
KeywordsCorrosionSour gasPipeline transportSalt (chemistry)Produced waterSalt waterMetallurgyMaterials scienceEnvironmental scienceWaste managementChemistryNatural gasEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Low water volumes associated with gas production can lead to salt deposition under conditions that allow water evaporation to occur within a pipeline system. Salt deposits are capable of attracting water vapor from wet gas that has a water content well below the normal dew point. This phenomenon is related to the hygroscopic or deliquescent properties associated with individual salts present in the deposits. The relative humidity at which liquid water starts to accumulate at salt deposits is referred to as the Deliquescence Relative Humidity (DRH). Both MgCl2 and CaCl2 have strong abilities to attract water and are typical components in gas line salt deposits. This means those components will attract water from the gas, even if the water content is well below the theoretical dew point, and form concentrated salt solutions. These concentrated brines lead to aggressive, localized pitting corrosion. In order to better understand the contribution of deliquescence to the corrosion mechanism in gas pipelines where salt deposits can form, a novel laboratory test method was developed to simulate the deliquescence phenomenon in gas pipelines. This paper gives a brief explanation of DRH, reports findings of this laboratory study and concludes that deliquescence is an integral part of the corrosion mechanism.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.015
Threshold uncertainty score1.000

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)0.0020.001

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 head0.224
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2013
Admission routes1
Has abstractyes

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