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Record W2529315067 · doi:10.1080/10916466.2016.1209683

The effect of capillary condensation on the phase behavior of hydrocarbon mixtures in the organic nanopores

2016· article· en· W2529315067 on OpenAlexaff
Xiaohu Dong, Huiqing Liu, Zhangxin Chen

Bibliographic record

VenuePetroleum Science and Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKelvin equationCapillary condensationAdsorptionEquation of stateMethaneButaneThermodynamicsWettingChemistryCondensationNanoporeHydrocarbonCapillary pressurePentanePhase (matter)Chemical engineeringHydrocarbon mixturesLangmuir adsorption modelCapillary actionMaterials scienceOrganic chemistryPorous mediumNanotechnologyPorosity

Abstract

fetched live from OpenAlex

In nanoscale pores, the adsorption of gas is a multilayer adsorption process, and the conventional Langmuir model is no longer valid. In particular for the unconventional gas condensate reservoir, the adsorbed gas will become condensate once the pressure is above the critical condensate pressure at pore scale. In this study, considering the effect of adsorption (wetting) film, the multicomponent Kelvin equation is modified to computing the isotherm of capillary condensation. Then it is coupled with the multispace adsorption model and Peng-Robinson equation of state to investigate and represent the phase behavior of hydrocarbons in organic nanopores. Then, a prediction process for the behavior of a four-component mixture of methane, n-butane, n-pentane, n-hexane are performed. The actual Marcellus shale gas is also used to examine the performance of this model.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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 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

Citations3
Published2016
Admission routes1
Has abstractyes

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