MétaCan
Menu
Back to cohort
Record W2615042707 · doi:10.5006/c2014-3808

Corrosion Management and Cleaning of SAGD Produced Gas/H2S Scavenger Contactor

2014· article· en· W2615042707 on OpenAlexaff
Qiang Liu, Jack Whittaker, Roberto Allende-Garcia, Allan J. McIntyre, John S. Magyar, Kyle Tamminga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsCenovus Energy (Canada)
Fundersnot available
KeywordsContactorScavengerCorrosionWaste managementProduced waterMetallurgyChemistryMaterials sciencePetroleum engineeringEnvironmental scienceEngineeringOrganic chemistryRadical

Abstract

fetched live from OpenAlex

Abstract Produced gas (PG) from the Steam Assisted Gravity Drainage (SAGD) recovery process typically consists of approximately 6% mercaptans and 7,500 ppm H2S. This sour gas is sweetened using a triazine H2S scavenger in a carbon steel (CS) contactor. The reactions between the scavenger and H2S were studied for operation optimization. A corrosion management program was established in PG lines and contactor including water/gas chemistry study and coupon monitoring. To keep the high contact efficiency of H2S and the scavenger, the contactor required thorough cleaning to remove several years of built up solids. A commercial hydrogen peroxide (H2O2) incorporating a stabilizer to reduce its decomposition in the presence of metal ions is used for mercaptan removal and oxidizing other sulfur containing compounds. The first contactor cleaning with H2O2 at 60°C (140°F) resulted in the generation of hazardous oxygen from H2O2 decomposition and the release of H2S. New procedures developed with an optimized cleaning temperature of 30°C (86°F) and a pH of 8.0 ensured the effective removal of the mercaptans, a significant decrease in H2O2 decomposition, and the oxidation of polysulfide for corrosion mitigation. This paper discusses the successful application of these procedures as well as the results of H2S scavenger reactions and the corrosion monitoring program.

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 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 score0.258
Threshold uncertainty score0.343

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)0.0000.000

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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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.

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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicIndustrial Gas Emission ControlFrench-language works237,207