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Record W2060585303 · doi:10.1115/fedsm-icnmm2010-30309

Pore Scale Simulation of Two-Phase Fluid Flow in Berea Sandstone Core Using Lattice Boltzmann Method

2010· article· en· W2060585303 on OpenAlexaff
Akshay C. Gunde, Sushanta K. Mitra, Tayfun Babadagli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLattice Boltzmann methodsMicroscale chemistryPorous mediumFluid dynamicsMultiphase flowMechanicsPorosityFlow (mathematics)GeologyMaterials scienceGeometryGeotechnical engineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

Study of flow through porous media has been an area of major interest due to its application in diverse areas like Enhanced Oil Recovery. In order to gain a better understanding of the physical processes taking place inside a porous structure, a large number of attempts have been made to computationally simulate multiphase fluid flow at pore-scale. Recently, application of Lattice Boltzmann Method has gained popularity for this very purpose, considering its relative superiority in dealing with complex boundaries and multiphase flow. However, in order that such a numerical analysis is successful, a proper understanding of the geometry of the pore structure at the microscale is required. This paper uses a Micro-CT scan image of a Berea Sandstone core, which displays a two dimensional representation of pore network inside the scanned sample. The processed image has been imported and simulation of an immiscible two-phase flow has been carried out by using a Lattice Boltzmann program. The resident fluid (oil) has been displaced by the invading fluid (water) due to application of a pressure gradient. The pore surfaces have been treated as solid boundaries and bounce back scheme has been implemented on them to account for the no-slip condition. The ability of the code to import an arbitrary porous geometry and perform numerical analysis of fluid flow has been demonstrated.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.901

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 head0.042
GPT teacher head0.364
Teacher spread0.321 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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