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Gas Production from Methane Hydrate: A Laboratory Simulation of the Multistage Depressurization Test in Mallik, Northwest Territories, Canada

2016· article· en· W2469437904 on OpenAlexaboutno aff
Katja U. Heeschen, Sven Abendroth, Mike Priegnitz, Erik Spangenberg, Jan Thaler, Judith M. Schicks

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
FundersBundesministerium für Wirtschaft und Technologie
KeywordsCabin pressurizationHydrateMethanePetroleum engineeringSaturation (graph theory)Natural gasClathrate hydrateMultiphase flowEnvironmental sciencePermafrostGeologyChemistryMechanicsMaterials science

Abstract

fetched live from OpenAlex

Gas hydrate production is still in the test phase. It is only now that numerical models are being developed to describe data and production scenarios. Laboratory experiments are carried out to test the rationale of the conceptual models and deliver input data. Major experimental challenges include (I) the simulation of a natural three-phase system of sand–hydrate–liquid with known and high hydrate saturations and (II) the simulation of transport behavior as deduced from field data. The large-scale reservoir simulator (LARS; 210 L sample) at the GFZ has met these challenges and allowed for the first simulation of the gas production test from permafrost hydrates at the Mallik drill site (Canada) via multistage depressurization. At the starting position, hydrate saturation was as high as 90%, formed from dissolved methane only. Whereas gas hydrate dissociation determined the flow patterns in the early pressure stages, the importance of different transport behaviors increased at lower pressure stages and increasing water content. Gas flow patterns as observed in Mallik were recorded. While the conceptual model for the experimental data does agree with the model proposed for Mallik at moderate and low gas production, it is different at high gas production rates.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.388

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)0.0000.000

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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 designBench or experimental
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

Citations114
Published2016
Admission routes1
Has abstractyes

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