Quantitative characterization of organic and inorganic pores in shale based on deep learning
Bibliographic record
Abstract
ABSTRACT Organic matter (OM) maturity is closely related to organic pores in shales. Quantitative characterization of organic and inorganic pores in shale is crucial for rock-physics modeling and reservoir porosity and permeability evaluation. Focused ion beam-scanning electron microscopy (FIB-SEM) can capture high-precision three-dimensional (3D) images and directly describe the types, shapes, and spatial distribution of pores in shale gas reservoirs. However, due to the high scanning cost, wide 3D view field, and complex microstructure of FIB-SEM, more efficient segmentation for the FIB-SEM images is required. For this purpose, a multiphase segmentation workflow in conjunction with a U-net is developed to segment pores from the matrix and distinguish organic pores from inorganic pores simultaneously in the entire 3D image stack. The workflow is repeated for FIB-SEM data sets of 17 organic-rich shales with various characteristics. The analysis focuses on improving the efficiency and relevance of the workflow, that is, quantifying the minimum number of training slices while ensuring accuracy and further combining the fractal dimension (FD) and lacunarity to study a simple and objective method of selection. Meanwhile, the computational efficiency, accuracy, and robustness to noise of the 2D U-net model are discussed. The intersection over the union of automatic segmentation can amount to 80%–95% in all data sets with manual labels as ground truth. In addition, calculated by the FIB-SEM multiphase segmentation, the organic porosity is used to quantitatively evaluate the OM decomposition level. Deep-learning-based segmentation shows great potential for characterizing shale pore structures and quantifying OM maturity.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".