Effect of Asphaltene Precipitation and Deposition on Miscible Viscous Fingering in Porous Media — High-Resolution Nonlinear Numerical Simulations
Bibliographic record
Abstract
Abstract This work focuses on modeling the miscible viscous fingering in porous media accounting for asphaltene precipitation and deposition. The mass balance equations for solvent and asphaltene are defined, and the highly nonlinear system of equations is solved numerically through hybridization of compact finite difference and pseudo-spectral methods. We explain how asphaltene precipitation and the resulting formation damage influence the growth of viscous fingers. To conduct our analysis, we use the experimental data for the amount of asphaltene precipitation at different solvent mass fractions and also oil viscosity at various asphaltene and solvent contents. This data is measured in our lab and is used as input for the nonlinear numerical simulations. For these simulations, the conventional finite difference schemes cannot be applied as they suffer from the excessive computational time and most importantly, numerical dispersion. Therefore, we employ hybrid techniques to benefit from the high accuracy of spectral methods and capture the nonlinear dynamics of fingerings on very fine grids. Hydrocarbons such as light n-alkanes are widely used as diluents in the production and upgrading of heavy oils. The addition of a diluent to heavy oil or bitumen alters the chemical forces acting within the mixture, leading to the precipitation of asphaltenes. It is hypothesized that precipitation of asphaltene from oil changes the viscosity behavior of the mixture, influences the dynamics of viscous fingering, and therefore affects the oil recovery. Moreover, asphaltene deposition alters the porosity and permeability of the porous media and might modify the flow paths, leading to possible formation damage. Our results show that asphaltene precipitates are mostly accumulated in the contact interface between the solvent and oil. The major asphaltene deposition occurs along the growing fingers leading to permeability reductions up to 30% in the studied cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".