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Record W2885595229 · doi:10.1021/acs.langmuir.8b01819

Nanoscale Phase Measurement for the Shale Challenge: Multicomponent Fluids in Multiscale Volumes

2018· article· en· W2885595229 on OpenAlexafffund
Junjie Zhong, Yinuo Zhao, Chang Lu, Yi Xu, Zhehui Jin, Farshid Mostowfi, David Sinton

Bibliographic record

VenueLangmuir · 2018
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of TorontoUniversity of AlbertaUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAlberta InnovatesE.W.R. Steacie Memorial FundWestern Canada Research GridCompute Canada
KeywordsOil shaleDew pointHydrocarbonNanoporousCondensationPropaneMethanePhase (matter)Hydrocarbon mixturesNanoporeAdsorptionBubble pointChemical physicsThermodynamicsMaterials scienceNanotechnologyChemistryBubbleMechanicsGeologyPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrocarbon recovery from shale reservoirs provides an increasing share of world energy. These resources are multicomponent fluid mixtures within multiscale geometries, and understanding their associated phase-change thermodynamics presents an array of challenges for experimentalists, theorists, operators, and policy makers. Here, we quantify hydrocarbon mixture phase behavior via direct imaging of connected channels spanning 4 orders of magnitude (10 nm to 10 μm) with supporting density functional theory. The methane/propane mixture dew point shifts, with early condensation of heavy components in nanopores because of a combination of capillarity and competitive surface adsorption. The bubble point in nanoconfinement is found to be deeply suppressed (∼3-fold), to below the bulk dew point of the original mixture, because of the exchange of mixture components with larger connected volumes. The trapping of the heaviest components of hydrocarbon mixtures within the smallest connected pores has implications for shale operations, reserve estimation, and ultimately energy security.

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 score0.882
Threshold uncertainty score0.323

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 head0.041
GPT teacher head0.276
Teacher spread0.235 · 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

Citations67
Published2018
Admission routes2
Has abstractyes

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