Comparison of Four Competing Invasion Percolation Models for Gas Flow in Porous Media
Bibliographic record
Abstract
Numerous variations of Invasion-Percolation (IP) models can simulate multiphase flow in porous media across various scales (pore-scale IP to macroscopic IP); here, we are interested in gas flow in water-saturated porous media. This flow occurs either as continuous or discontinuous flow, depending on the flow rate and the porous medium’s nature. Literature suggests that IP models are well suited for the discontinuous gas flow regime; other flow regimes have not been explored. Our research compares four existing macroscopic IP models and ranks their performance in these “other” flow regimes. We test the models on a range of gas-injection in water-saturated sand experiments from transitional and continuous gas flow regimes. Using the light transmission technique, the experimental data is obtained as a time series of images in a 2-dimensional setup. To represent pore-scale heterogeneities, we ran each model version on several random realizations of the initial entry pressure field. We use a diffused version of the so-called Jaccard coefficient to rank the models against the experimental data. We average the Jaccard coefficient over all realizations per model version to evaluate each model and calibrate specific model parameters. Depending on the application domain, we observe that some macroscopic IP model versions are suitable in these previously unexplored flow regimes. Also, we identify that the initial entry pressure fields strongly affect the performance of these models. Our comparison method is not limited to gas-water systems in porous media but generalizes to any modelling situation accompanied by spatially and temporally highly resolved data.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".