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Record W2772917829 · doi:10.1144/sp468.3

The utility of methane clumped isotopes to constrain the origins of methane in natural gas accumulations

2017· article· en· W2772917829 on OpenAlexaff
Daniel A. Stolper, Michael Lawson, Michael J Formolo, Cara L. Davis, Peter Douglas, John M. Eiler

Bibliographic record

VenueGeological Society London Special Publications · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsMcGill University
FundersPetrobrasCalifornia Institute of TechnologyNational Science Foundation
KeywordsMethaneGeologyNatural gasEarth scienceNatural (archaeology)IsotopeMethane emissionsGeochemistryMethane gasIsotopes of carbonPaleontologyChemistryNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Abstract Methane clumped-isotope compositions provide a new approach to understanding the formational conditions of methane from both biogenic and thermogenic sources. Under some conditions, these compositions can be used to reconstruct the formational temperatures of the gas, and this capability can be applied to common subsets of both biogenic and thermogenic systems. Additionally, there are examples in which clumped-isotope compositions do not reflect gas-formation temperatures but instead mixing effects and kinetic phenomena; such kinetic effects also occur in common and recognizable subtypes of biogenic and thermogenic gases. Here we review the use of methane clumped-isotope measurements for understanding the origin of methane in the subsurface. We review methane clumped-isotope measurements from numerous biogenic and thermogenic natural gas reservoirs. We then place these measurements in the context of common frameworks for identifying the formational conditions of methane including the use of methane δ 13 C and δD values and C 1 /C 2–3 ratios. Finally, we propose a framework for how methane clumped isotopes can be used to identify the origin of methane accumulations.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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 designTheoretical or conceptual
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

Citations75
Published2017
Admission routes1
Has abstractyes

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