Segmenting identified fracture families from 3D fracture networks in Montney rock using a deep learning-based method
Bibliographic record
Abstract
Fractures are critical to subsurface activities such as oil and gas extraction, geothermal energy production, and carbon storage. Hydraulic fracturing, a technique that enhances fluid production, creates complex fracture networks within rock formations containing natural discontinuities. Accurately distinguishing between hydraulically induced fractures and pre-existing discontinuities is essential for understanding hydraulic fracture mechanisms. However, this remains challenging due to the interconnected nature of fractures in three-dimensional (3D) space. Manual segmentation, while adaptive, is both labor-intensive and subjective, making it impractical for large-scale 3D datasets. This study introduces a deep learning-based progressive cross-sectional segmentation method to automate the classification of 3D fracture volumes. The proposed method was applied to a 3D hydraulic fracture network in a Montney cube sample, successfully segmenting natural fractures, parted bedding planes, and hydraulic fractures with minimal user intervention. The automated approach achieves a 99.6% reduction in manual image processing workload while maintaining high segmentation accuracy, with test accuracy exceeding 98% and F 1 -score over 84%. This approach generalizes well to Brazilian disc samples with different fracture patterns, achieving consistently high accuracy in distinguishing between bedding and non-bedding fractures. This automated fracture segmentation method offers an effective tool for enhanced quantitative characterization of fracture networks, which would contribute to a deeper understanding of hydraulic fracturing processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".