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Record W2326522189 · doi:10.2118/174486-ms

Experimental Evaluation of H2S Yields during Thermal Recovery Processes

2015· article· en· W2326522189 on OpenAlexaff
Riyi Lin, Duopei Song, Xinwei Wang, Daoyong Yang

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2015
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsUniversity of Regina
FundersPetroChina Company LimitedChina University of Petroleum, Beijing
KeywordsAlkalinityCarbon dioxideNitrogenAlkali metalPassivationChemistryGas analyzerInorganic chemistryAnalytical Chemistry (journal)Carbon fibersMaterials scienceChemical engineeringEnvironmental chemistryPhysical chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Experimental techniques have been developed to quantitatively evaluate in-situ H2S generation as a function of solution alkalinity, SO42− concentration, and carrier gas during thermal recovery processes. Experimentally, well-designed laboratory tests have been conducted to quantify the in-situ generation of H2S in a surface passivation reactor. The reaction gases were analyzed by using a gas analyzer, while the pH values were measured by using a glass electrode after adding anions or cations into the solution. Solution alkalinity is found to restrain the formation of H2S after subtracting the amount of H2S that neutralizes alkali in the solution. H2S production increases with increasing SO42− concentrations, more evidently at a high temperature. As temperature increases, using either carbon dioxide or nitrogen as carrier gas increases the H2S production; carbon dioxide as a carrier gas, however, yields more H2S up to 57.6% than nitrogen at temperature of 200-280°C. It is suggested that effect of pH of the formation water be taken into account while predicting the produced acid gas rates.

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)0.001
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 score0.144
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.063
GPT teacher head0.269
Teacher spread0.207 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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