Overcoming the Model‐Data‐Fit Problem in Porous Media: A Quantitative Method to Compare Invasion‐Percolation Models to High‐Resolution Data
Bibliographic record
Abstract
Abstract Invasion percolation (IP) models offer a computationally inexpensive way to simulate multiphase flow in porous media, but only very few studies have compared their results to actual laboratory experimental image data. One reason might be the difficulty in quantitative assessment: IP models do not have a notion of experimental time but only have an integer counter for simulation steps that imply a time order. Previous experiments‐to‐model comparison studies have either used perceptual similarity or spatial moments as measures of comparison. In this work, we present an objective and quantitative comparison method that overcomes the limitations of the traditional approaches. First, we perform a volume‐based time matching between real‐time experiments and IP model results. Then, we evaluate the quality of fit using a diffused version of the so‐called Jaccard coefficient, which is known from image recognition. We demonstrate our method's applicability on a laboratory‐scale experimental video of gas injection in homogeneous, saturated sand, comparing it to a Macro‐IP model's simulation results. We consider random realizations of the initial entry pressure field to capture the sand's inherent pore‐scale heterogeneity. We find that our proposed method is reliable and intuitive in identifying realistic model realizations. The “strictness”of the method can be adjusted to relevant scales of interest via the blurring (diffusion) radius of the compared images. Beyond the application presented here, our comparison method can be used to compare any high‐resolution space‐time model output to experimental data given as raster images, thus providing valuable insights for model development in many research areas.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".