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Record W2114198181 · doi:10.1190/tle31091076.1

Stochastic volume estimation and connectivity analysis at the Mallik gas hydrate field, Northwest Territories, Canada

2012· article· en· W2114198181 on OpenAlexaffabout
Camille Dubreuil-Boisclair, Erwan Gloaguen, Gilles Bellefleur, Denis Marcotte

Bibliographic record

VenueThe Leading Edge · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsPolytechnique MontréalGeological Survey of Canada
Fundersnot available
KeywordsClathrate hydrateGeologyPermafrostSubmarine pipelineNatural gas fieldNatural gasHydrateSaturation (graph theory)Well loggingSeismologyGeomorphologyPetroleum engineeringGeotechnical engineeringOceanography

Abstract

fetched live from OpenAlex

Gas hydrates located offshore and onshore beneath thick permafrost areas constitute one of the largest untapped natural gas resources. Yet, gas hydrate in place (GHIP) estimation at the scale of a field is not common in the scientific literature but is required to realistically assess the economical potential of specific accumulations. Progress in the last decade in Alaska and Canada has shown that gas hydrate accumulations beneath thick permafrost can be mapped at depth using conventional seismic attributes (Inks et al., 2009; Riedel et al. 2009). To evaluate the economic potential of gas hydrates in this environment, a test site at Mallik, Northwest Territories, Canada, was extensively surveyed (three-dimensional seismic, full set of logs in two wells, etc.) and a production test was realized in high gas-hydrate horizons. At Mallik, high P- and S-wave velocities, high acoustic impedances, and strong seismic amplitude reflections were all linked to sand-rich sediments with a high saturation of gas hydrates (Bellefleur et al. 2006; Riedel et al.). This relationship provides a strong basis for an integrated data characterization of this gas hydrate deposit.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.578

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)0.0010.000

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations2
Published2012
Admission routes2
Has abstractyes

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