MétaCan
Menu
Back to cohort
Record W3193362264 · doi:10.5006/c2021-16503

Comparison of Laboratory Based H2S Scavenger Methodologies for Oil and Gas Production

2021· article· en· W3193362264 on OpenAlexaff
Jody Hoshowski, Alyn Jenkins, Øystein Birketveit, Rachael Cole, Aiman Kamaruzaman, Tore Nordvik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsScavengerProduction (economics)Petroleum engineeringProcess engineeringEnvironmental scienceMetallurgyMaterials scienceWaste managementChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT H2S scavenger test methodologies used for the evaluation of scavenger products in oil and gas production can vary in design and purpose. The benefits and limitations of three different methods have been determined using established scavenger chemistries. The capacity, kinetics, and efficiency of the scavengers were evaluated by employing a variation of temperature, concentration, and, in some cases, pressure. The effect of pH was studied to determine the contribution of neutralization compared to a reaction where the sulfide is covalently bound (reversible versus non-reversible reactions). Additional observations were made on thermal stability and secondary effects, such as, solids formation from reaction products and scaling. Industry requires information on the capability of H2S scavenger methods in order to select suitable products to implement in their production systems. The intent is to provide a perspective that helps them make technically informed decisions.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.086
GPT teacher head0.345
Teacher spread0.259 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicOil, Gas, and Environmental IssuesFrench-language works237,207