Modeling Miscible Injection in Fractured Porous Media using Non-classical Simulation Approaches
Bibliographic record
Abstract
Abstract The objective of this paper is to introduce an adaptation of a non-classical simulation method (random walk, RW) for simulation of fully miscible displacement in fractured porous media, and to validate this method using production and visual data obtained from an experimental work. First, the limitations of classical (continuum models) modeling approach in the simulation of miscible displacement in fractured media were identified by matching the numerical and experimental results obtained earlier. Classical simulation yielded reasonable matches for low viscosity oil but failed to capture the flow patterns of heavy oil displacement, especially in the cases of vertical displacement. This was attributed to two reasons: (1) Numerical dispersion and grid size limitations and (2) the random nature of the phenomenon (mainly the viscous fingering process). Beyond that, the classical modeling scheme required the intensive use of "matrix-fracture pseudo transfer parameters" to achieve experimental matching. To overcome these problems, a non-classical modeling approach, the Random Walk (RW) model was adapted. This technique deals with particles (walkers), each of which moves randomly, but the probability of the movement is defined considering the physics of the process. By tracing a large number of particles, one can model the process and have an idea about the transport of injected and displaced fluid in complex systems. The RW technique allows capturing micro heterogeneities, the random nature of the diffusion process and viscous fingering. It also requires less computational time compared to classical simulation methods. The RW model introduced was validated using experimental -visual- data for different oil types, displacement directions (horizontal and vertical), and injection rates. This exercise showed that the model presented here captures the physics of the process and hence, can be extended and used for larger (field) scale processes of miscible displacement in complex fracture networks, which would not be possible with classical finite-difference models.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".