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Record W2037719613 · doi:10.2118/149508-ms

Prediction Performance of Permeability Models in Gas Hydrate Bearing Sands

2011· article· en· W2037719613 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCanadian Unconventional Resources Conference · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Calgary
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Natural Resources Limited
KeywordsPermeability (electromagnetism)HydratePorous mediumClathrate hydrateSaturation (graph theory)Relative permeabilityPorosityPetroleum engineeringMaterials scienceGeologyGeotechnical engineeringChemistryMathematics

Abstract

fetched live from OpenAlex

Abstract A major factor affecting the hydrate formation and that of gas dissociation is the variation of permeability of the porous media in the presence of gas hydrates. In the absence of reliable experimental data, theoretical and empirical models have been proposed to establish the relationship between gas hydrate saturation and permeability. The effectiveness of a particular permeability model in fitting the measured data has largely been qualitative through graphical analysis. In contrast, this paper introduces a quantitative performance measure to evaluate the effectiveness of a specific model in predicting the measured permeability. Secondly, a novel hybrid model based on the weighted combination of pore-filling and grain coating model is proposed. Permeability measurements from laboratory and field data were used to assess the prediction performance of various permeability models and the proposed hybrid model.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.995

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)0.0060.000

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 head0.042
GPT teacher head0.194
Teacher spread0.152 · 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