Exploring Convolutional Neural Networks and Machine Learning for Oil Sands Drill Core Image Analysis
Bibliographic record
Abstract
Summary Permeability is one of the key reservoir rock properties that can substantially affect the performance of the oil sand reservoirs. Standard methods for estimating permeability do not work well in oil sands. However, permeability can be estimated from mean grain size (MGS) obtained from particle size distribution (PSD). This paper investigates the use of the convolutional neural network (CNN) and machine learning (ML) to estimate MGS from drill core photos from oil sand reservoirs. Three approaches are explored for classifying core photos based on facies analysis, including (1) transfer learning on the pre-trained VGG-16 CNN model, (2) fine-tuning the top layers of VGG-16, and (3) the combination of VGG-16 and ML algorithms. These approaches are further utilized to estimate MGS from core photos to investigate their accuracy in predicting the facies. The results demonstrate that MGS can be accurately estimated from core photos using a random forest model trained on the features extracted from the last convolutional layer block of the VGG-16 CNN model. This work is one of the first research on the application of ML and CNN techniques for characterizing drill cores using digital images.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".