Estimating Petrophysical Properties of Shale Rock Using Conventional Neural Networks CNN
Bibliographic record
Abstract
Abstract Digital Rock Physics (DRP) serves as a powerful computational tool for analyzing the petrophysical properties of rock. Obtaining the properties of a fine-grained sample, such as shale is very challenging due to its highly variable and complex nature. Capturing the micro-features of this structure requires advanced microscopy techniques such as SEM (scanning electron microscopy) and FIB-SEM (Focused ion beam- scanning electron microscopy). However, performing advanced microscopy techniques to capture the heterogeneity of the sample is quite difficult; the slow speeds of data collection and analysis are two critical problems that limit more extensive use of this technology. In this study, an alternative approach is proposed to quantify the physical properties of the rock sample. This study aims to accelerate the process of SEM image analysis and reduce the computational cost by using machine learning. A deep learning-based method, Convolutional Neural Networks (CNN), is utilized to predict the properties from the 2D grayscale SEM images of Marcellus shale. The image data set is segmented by applying watershed segmentation to extract the pore network of the sample. Porosity and average pore size are the two properties computed for this study. The SEM images are down sampled to low-resolution images which are fed as an input, and the computed properties are used for training and validation of the CNN network. A detailed description of the image segmentation process, CNN architecture and the predicted results are discussed in this work.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".