Machine Learning Approach to Predict the Illite Weight Percent of Unconventional Reservoirs from Well-Log Data: An Example from Montney Formation, NE British Columbia, Canada
Bibliographic record
Abstract
Shale mineralogy is critical for the proper design and execution of hydraulic fracturing operations and for evaluating production potential. There has been relatively little research into using artificial intelligence for mineralogical prediction for the Montney Formation. This study aims to predict the Montney Formation illite wt.% using readily available conventional logs, where illite is one of the constituents of shale and can aid in analyzing the brittle and ductile zones within the shale formation. The wt.% of illite is often determined by examining core samples or cuttings using XRD or QEMSCAN; both techniques are time-consuming, costly, and cannot be performed without physical samples. Based on conventional log readings, this study uses artificial neural networks (ANNs) and principal component analysis (PCA) to construct an accurate prediction model for illite wt.%. The feed-forward neural network (FFNN) obtained good overall performance in illite wt.% prediction (R2 = 92%) utilizing the backpropagation algorithm and the B.R. technique for eight input parameters. The ANN model was tested by randomly selecting three wells from the same log dataset excluded from the core training and testing phases. Overall, R2 = 88.5% was found in the tests, which is encouraging. This work demonstrates the viability of employing the ANN in applications involving evaluating mineralogical components of a target source rock using traditional logs, especially when geochemical data are missing or inadequate.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".