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Record W4391956149 · doi:10.1063/5.0177369

On the transport behavior of shale gas in nanochannels with fractal roughness

2024· article· en· W4391956149 on OpenAlexaff
Liqun Lou, Peijian Chen, Juan Peng, Jiaming Zhu, Guannan Liu

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeomechanica (Canada)
FundersNational Key Research and Development Program of ChinaNanjing UniversityState Key Laboratory of Solid LubricationNanjing University of Aeronautics and AstronauticsState Key Laboratory of Mechanics and Control of Mechanical StructuresNational Natural Science Foundation of China
KeywordsFractalOil shaleSurface finishShale gasMethanePetroleum engineeringFractal dimensionPhysicsMaterials scienceGeologyComposite materialChemistry

Abstract

fetched live from OpenAlex

As an efficient and environmentally friendly source of energy, shale gas is abundantly available and continues to contribute to the economy growth because of its huge potential for production. However, accurately predicting the transport behavior of shale gas is still challenging due to the small scale and complexity of nanochannels, which impedes the efficiency of recovery. In this paper, the transport behavior of shale gas in nanochannels with fractal roughness is studied by molecular dynamics simulation and theoretical analysis. It is found that the present work functions well to predict the transport behavior of shale gas in nanochannels with roughness. The introduction of fractal roughness hinders the transport of shale gas and leads to a complex trajectory of methane molecules in nanochannels. Furthermore, it is interesting to find the average gas viscosity increases, while the gas flux decreases with the increase in the inclined angle due to the impediment effect after the deflection. These results are helpful for understanding the migration of shale gas in nanochannels with roughness and guiding the improvement of shale gas recovery in practical applications.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.223

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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 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

Citations13
Published2024
Admission routes1
Has abstractyes

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