Generalizable Permeability Prediction of Digital Porous Media via a Novel Multi‐Scale 3D Convolutional Neural Network
Bibliographic record
Abstract
Abstract Subsurface characterization is critical in understanding and controlling many natural and industrial processes including groundwater movement, oil extraction, and geological carbon dioxide sequestration. While recent advances in three‐dimensional (3D) imaging of core samples have enabled digital subsurface characterization, the exorbitant computational cost associated with direct numerical simulation in 3D remains a persistent challenge. In contrast, machine learning models are much more efficient, though their use in subsurface characterization is still in its infancy. Here, we introduce a novel 3D convolutional neural network (CNN) for end‐to‐end prediction of permeability, which is a fundamental characteristic of subsurface porous media. We show that increasing the dataset size and diversity, utilizing multi‐scale feature aggregation, and optimizing the network architecture elevate the model accuracy beyond that of existing state‐of‐the‐art 3D CNN models for permeability prediction. We demonstrate that the model is generalizable, and it is capable of predicting the permeability of previously unseen samples with an excellent accuracy.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".