MétaCan
Menu
Back to cohort
Record W2121680593 · doi:10.2516/ogst:2005017

Thermodynamic Analysis of Organic/Inorganic Reactions Involving Sulfur: Implications for the Sequestration of H2s in Carbonate Reservoirs

2005· article· en· W2121680593 on OpenAlexaboutno aff
Laurent Richard, Daniel R. Neuville, Jérôme Sterpenich, Erwan Perfetti, J. C. Lacharpagne

Bibliographic record

VenueOil & Gas Science and Technology – Revue d’IFP Energies nouvelles · 2005
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCarbonateDolomiteAnhydriteSulfurSulfatePyriteChemistryCalciteCarbonate mineralsGeologyMineralogyGypsumOrganic chemistry

Abstract

fetched live from OpenAlex

A thermodynamic analysis has been made of water-gas-rock-hydrocarbon interactions involving sulfur in carbonate reservoirs. Mineral-solution equilibria calculations indicate that formation waters produced from carbonate reservoirs at temperatures between 40°C and 160°C in the Paris Basin, Alberta Basin, and the North Sea are saturated with respect to calcite and dolomite, but undersaturated with respect to anhydrite. The calculations also suggest that increasing the activity of dissolved H2S (aH2S(aq)) in the reservoirs favors the formation of elemental sulfur, which constitutes a potential sequestration reaction for H2S. It is also shown that high partial pressures of H2S generated by thermochemical sulfate reduction in carbonate reservoirs may be controlled by metastable equilibrium states between hydrocarbons, organic sulfur compounds, elemental sulfur, and H2S at partial pressures which are 4 to 6 orders of magnitude higher than in clastic reservoirs where the H2S partial pressures are controlled by pyrite and iron carbonates. Depending on the H/C ratios of the hydrocarbons and organic sulfur compounds, H2S may be consumed or produced as a result of petroleum sulfurization reactions in carbonate reservoirs.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations8
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueOil & Gas Science and Technology – Revue d’IFP Energies nouvellesSame topicHydrocarbon exploration and reservoir analysisFrench-language works237,207