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Record W2193763469 · doi:10.1139/cjc-2015-0425

Hydrogen sulfide formation in oil and gas

2015· article· en· W2193763469 on OpenAlexafffundvenue
Robert A. Marriott, Payman Pirzadeh, Juan J. Marrugo-Hernandez, Shaunak Raval

Bibliographic record

VenueCanadian Journal of Chemistry · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistrySulfateHydrogen sulfideSulfurHydrogenEnhanced oil recoveryPetroleumSour gasSulfideInorganic chemistryHydrogen productionChemical engineeringNatural gasOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrogen sulfide (H 2 S) can be a significant component of oil and gas upstream production, where H 2 S can be naturally generated in situ from reservoir biomass and from sulfate-containing minerals through microbial sulfate reduction and (or) thermochemical sulfate reduction. On the other hand, the technologies employed in oil and gas production, especially from unconventional resources, also can contribute to generation or delay of appearance of H 2 S. Steam-assisted gravity drainage and hydraulic fracturing used in production of oil sands and shale oil/gas, respectively, can potentially convert the sulfur content of the petroleum into H 2 S or contribute excess amounts of H 2 S during production. A brief overview of the different classes of chemical reactions involved in the in situ generation and release of H 2 S is provided in this work. Speciation calculations and reaction mechanisms are presented to explain why thermochemical sulfate reduction progresses at faster rates under low pH. New studies regarding the degradation of a hydraulic fracture fluid additive (sodium dodecly sulfate) are reported for T = 200 °C, p = 17 MPa, and high ionic strengths. The absence of an ionic strength effect on the reaction rate suggests that the rate-limiting step involves the reaction of neutral species, such as elemental sulfur. This is not the case with other thermochemical sulfate reduction studies at T > 300 °C. These two different kinetic regimes complicate the goal of extrapolating laboratory results for field-specific models for H 2 S production.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.279

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 head0.006
GPT teacher head0.170
Teacher spread0.163 · 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 designObservational
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

Citations113
Published2015
Admission routes3
Has abstractyes

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