Numerical simulation of miscible fluid flows in \nporous media
Bibliographic record
Abstract
The study of miscible \now in porous media is an important topic in many disciplines \nof science and engineering, especially in the field of petroleum engineering. \nFor example, Carbon dioxide (CO₂) may be injected into an oil reservoir in order \nto improve the oil recovery rates, which is called enhanced oil recovery (EOR). This \nthesis focuses on the study of a miscible displacement of two \nfluids, such as CO₂ and \noil, in a porous medium. An upscaling methodology for modeling multiscale features \nof the \now and the porous medium has been studied, where the overall pressure drag \nand skin friction exerted on the porous medium has been modelled by combining the \nDarcy's law with a statistical mechanical theory of viscosity, which is an important \ncontribution of this thesis. \nA numerical methodology for capturing the multiphysics and multiscale nature \nof the governing motion has been studied. The temporal discretization employs the \nsecond order Crank-Nicolson scheme for viscous and diffusive phenomena, and an \nexplicit method for all other terms. The nonlinear advection terms in the momentum \nequation has been treated with an Euler explicit \nflux form central finite difference \nmethod; however, the advection of the CO2 mass \nflux has been treated with a streamline \nbased Lagrangian method. In order to implement the Marker-and-Cell (MAC) \nscheme for resolving the incompressibility, a staggered arrangement of the velocity \nand pressure has been presented on a collocated grid. This approach enhances the \nimplementation of a multigrid solver, and is a novel computational model for simulating \nmiscible displacement processes. The performance of the Lagrangian method \nhas been assessed with respect to an equivalent \nflux form upwind method. \nThe results indicate that the viscous forces play a signicant role compared to the \neffect of permeability on miscible displacement of CO₂ and oil, where the injected CO₂ displaces the residual oil without being distorted, thereby enhancing the recovery \nof hydrocarbon. Although the present results with an idealized model lacks from \nverifications with field measurements, findings of this thesis provide useful feedback \nto further investigations on CO₂ based EOR techniques.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".