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Macroscopic modeling of acoustically-assisted miscible displacements in porous media

2025· article· en· W4411224733 on OpenAlexafffund
Saeid Khasi, Apostolos Kantzas

Bibliographic record

VenueInternational Journal of Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundMitacsAlberta InnovatesPetroleum Technology Research CentreChevronEnergi SimulationCommission Géologique du CanadaAlberta Energy Regulator
KeywordsPorous mediumMaterials scienceMechanicsPorosityComposite materialPhysics

Abstract

fetched live from OpenAlex

Pore-scale investigations confirm that acoustic waves can enhance mass transfer during miscible displacements in porous media. However, these beneficial effects are less understood at the macro-scale, where large-scale heterogeneities could amplify dispersivities by orders of magnitude. In this study, we develop a continuum multiphysics model to simulate flow and transport in porous media under mechanical wave stimulation. Our model couples a conservative advection-dispersion description of species transport with a macroscopic flow equation, while accounting for time-harmonic forces in a poroelastic medium. This two-way coupling accounts for both the pore pressure changes resulting from rock strain and the stress alterations induced by applied pressures. Simulation results reveal that while wave-induced deformations are reversible, they can lead to permanent enhancements in both spreading and mixing at larger scales. We conduct a detailed analysis of how waveform characteristics, hydrodynamic parameters, and poroelastic properties affect these processes, and we explore scale dependencies through identified governing dimensionless numbers. Under field-applicable conditions, represented by a relative excitation amplitude of 200, a frequency of 50 Hz, and a viscosity log ratio of 3, the simulation results predict up to a 12 % increase in in-place fluid recovery. An energy trade-off analysis, comparing electricity used and fuel produced, further suggests that these enhancements can be economically viable at large scales and high excitation amplitudes. Moreover, our findings indicate that higher frequencies, greater heterogeneities in rock and fluid properties, softer media, and transverse wave propagation relative to the flow direction are favorable for the acoustically assisted process. This study proposes an innovative multi-physics solvent-based method that can reduce both the required solvent amounts and greenhouse gas emissions.

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 score0.007
Threshold uncertainty score0.014

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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