Numerical investigation of two phase flow in micromodel porous media: Effects of wettability, heterogeneity, and viscosity
Bibliographic record
Abstract
Abstract The aim of the present work is to assess the effects of wettability, heterogeneity, and viscosity differences on water‐oil displacement process in micromodel porous media through numerical modelling. The two‐phase flow was simulated by Cahn‐Hilliard phase field method (PFM) using a finite element package. The micromodel was initially saturated with oil (wetting phase) and oil was produced through invasion of the displacing phase into the matrix. The computed oil and water saturations were in good agreement with those obtained by the visual flooding experiment. Using the validated model, sensitivity analysis was performed to investigate the effects of different wettability states, heterogeneity, and viscosity on the displacement process. The obtained results showed that the final oil saturation of the homogeneous pattern is 0.37 which is 13 % less than that of the heterogeneous one. For the highly oil‐wet medium ( ), the capillary forces prevented water to invade more pore bodies and resulted in 0.62 ultimate oil saturation; however, the final oil saturation in the neutral wet ( ) was ∼0.5. Increasing the viscosity of displacing agent formed lower channelling and fingering which led to higher oil recovery due to the favourable mobility ratio. The present study demonstrates that PFM can be a reliable approach to capture micro‐ and macro‐scale mechanisms in the simulation of immiscible two‐phase flow in micromodel porous media with a reasonable computational time.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".