Macroscopic modeling of acoustically-assisted miscible displacements in porous media
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".