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Record W1968616663 · doi:10.3997/2214-4609.20131988

Thermo-hydro-mechanical Sand Production Model in Hydrate-bearing Sediments

2013· article· en· W1968616663 on OpenAlexaboutno aff
Assaf Klar, Shun Uchida, Z. Charas, Koji Yamamoto

Bibliographic record

VenueProceedings · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsHydratePetroleum engineeringGeotechnical engineeringGeologyCabin pressurizationClathrate hydrateBearing (navigation)Extraction (chemistry)Environmental scienceMaterials scienceComposite materialChemistry

Abstract

fetched live from OpenAlex

A better understanding of the behavior of hydrate-bearing sediments during gas extraction is a vital step towards realization of commercially viable gas production for the future. In 2007, the world first trial of gas production from hydrate-bearing sediments by depressurization method was conducted at the Mallik gas hydrate site, located in the Mackenzie Delta of Northwest Territories, Canada. However, the operation encountered a large amount of sand migration into the well, a phenomenon known as sand production, and thus was terminated after 24 hours. This incident highlights the importance of development of hydro-mechanical sand production model within hydrate-bearing sediments and understanding of the behavior of hydrate-bearing sediments with the effect of sand production during gas extraction. This extended abstract provides a formulation for the sand production including grain flow and hydraulic dispersion effect. A formulation is fully-coupled such that the sand production affects fluid pressures, saturations and temperature. In addition, the effective stress reduction due to grain detachment is incorporated. This results in further deformation of hydrate-bearing sediments, which may need to be considered for stability of the wellbore.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.213
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 source (direct Gemma or distilled Codex), 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

Citations15
Published2013
Admission routes1
Has abstractyes

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