Generating a labeled data set to train machine learning algorithms for lithologic classification of drill cuttings
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Bibliographic record
Abstract
Abstract Despite significant developments in the past few years in the application of machine learning algorithms for the lithologic classification of rock samples, publicly available labeled data sets are very scarce. We open source a fully labeled data set containing more than 16,000 scanning electron microscopy (SEM) images of drill cutting samples—mounted on thin sections—from a low-permeability reservoir in western Canada. We develop a simplified image processing workflow to segment and isolate the rock chips into individual SEM images, which in turn are used to identify, classify, and quantify rock types based on textural characteristics. In addition, using this data set, we explore the use of convolutional neural networks (CNNs) as a baseline tool for acceleration and automatization of rock-type classification. Without significant modifications to popular CNN models, we obtain an accuracy of approximately 90% for the test set. Results demonstrate the potential of CNN as a fast approach for lithologic classification in low-permeability siltstone reservoirs. In addition to making the data set publicly available, we believe our workflow to segment and isolate drill cutting samples in individual images of rock chips will facilitate future research of drill cuttings properties (e.g., lithology, porosity, and particle size) using machine learning algorithms.
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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) | 0.000 | 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) | 0.000 | 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 it