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Record W2029249345 · doi:10.1103/physreve.76.066309

Lattice-Boltzmann simulation of solvent diffusion into oil-saturated porous media

2007· article· en· W2029249345 on OpenAlexafffund
Can Hatiboglu, Tayfun Babadagli

Bibliographic record

VenuePhysical Review E · 2007
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLattice Boltzmann methodsPorous mediumSolventDiffusionMaterials scienceStatistical physicsPorosityLattice (music)ThermodynamicsMechanicsPhysicsChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

We simulated the diffusion process into oil-saturated porous media using the modified diffusion-limited aggregation and the lattice Boltzmann method algorithms. The results were matched to visual experiments for cocurrent (two sides of the model open to flow) and countercurrent (only one side of the model open to flow) diffusion for horizontally and vertically positioned samples. The model saturated with oil was exposed to pentane in order for a miscible interaction to take place. These experiments mimic the transfer between the rock matrix and fracture during gas or liquid solvent injection for enhanced oil recovery, underground waste disposal, groundwater contamination, and CO2 sequestration in naturally fractured reservoirs. Finger development at the early stages of the process was controlled by oil viscosity and the interaction type dictated by the boundary conditions. The convective transport driven by the buoyancy that was experimentally observed on vertically oriented samples and transfer driven by diffusion on the horizontal ones were captured in the LBM simulation of the process.

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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.314
Teacher spread0.290 · 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

Citations27
Published2007
Admission routes2
Has abstractyes

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Same venuePhysical Review ESame topicLattice Boltzmann Simulation StudiesFrench-language works237,207