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Record W2165286423 · doi:10.1002/aic.14791

A model for gas transport in microfractures of shale and tight gas reservoirs

2015· article· en· W2165286423 on OpenAlexafffund
Keliu Wu, Xiangfang Li, Chenchen Wang, Zhangxin Chen, Wei Yu

Bibliographic record

VenueAIChE Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersNational Science and Technology Major ProjectNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaAlberta Innovates - Technology FuturesCMG Reservoir Simulation Foundation
KeywordsKnudsen diffusionKnudsen numberShale gasChemistrySlip (aerodynamics)MechanicsDiffusionPetroleum engineeringGaseous diffusionFlow (mathematics)CollisionOil shaleGas oil ratioGeologyThermodynamicsPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

A model for gas transport in microfractures of shale and tight gas reservoirs is established. Slip flow and Knudsen diffusion are coupled together to describe general gas transport mechanisms, which include continuous flow, slip flow, transitional flow, and Knudsen diffusion. The ratios of the intermolecular collision frequency and the molecule‐wall collision frequency to the total collision frequency are defined as the weight coefficients of slip flow and Knudsen diffusion, respectively. The model is validated by molecular simulation results. The results show that: (1) the model can reasonably describe the process of the mass transform of different gas transport mechanisms; (2) fracture geometry significantly impacts gas transport. Under the same fracture aperture, the higher the aspect ratio is, the stronger the gas transport capacity, and this phenomenon is more pronounced in the cases with higher gas pressure and larger fracture aperture. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2079–2088, 2015

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)0.001
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 score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.255
Teacher spread0.221 · 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

Citations155
Published2015
Admission routes2
Has abstractyes

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