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Record W2588622022 · doi:10.1002/aic.15678

Real gas transport in tapered noncircular nanopores of shale rocks

2017· article· en· W2588622022 on OpenAlexafffund
Jinze Xu, Keliu Wu, Sheng Yang, Jili Cao, Zhangxin Chen, Yi Pan, Bicheng Yan

Bibliographic record

VenueAIChE Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesCMG Reservoir Simulation Foundation
KeywordsNanoporeShale gasAdsorptionConductanceChemistryMaterials scienceNanotechnologyOil shaleMechanicsChemical physicsPhysicsEngineeringPhysical chemistryWaste managementCondensed matter physics

Abstract

fetched live from OpenAlex

A model for gas transport in tapered noncircular nanopores of shale rocks with integrating real gas effect, molecular kinetic, and transport behavior was presented. The proposed model is well validated with experimental and simulation data, including six kinds of gases, under different pressures, and temperatures. Results show that neglect of real gas effect results in the misleading transport conductance. The adsorbed gas transport ratio and the ratio of area occupied by adsorbed gas increase along the length of nanopore. Pore proximity induces the faster gas transport and omitting pore proximity leads to the enlargement of the adsorbed gas‐dominated region. Increasing taper ratio (ratio of inlet size to outlet size) and aspect ratio weakens real gas effect and lowers free gas transport. Moreover, it lowers the total transport capacity of the nanopore, and the tapered circular nanopore owns the greatest transport capacity, followed by tapered square, elliptical, and rectangular nanopores. © 2017 American Institute of Chemical Engineers AIChE J , 63: 3224–3242, 2017

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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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

Citations51
Published2017
Admission routes2
Has abstractyes

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