Tree-based, boosting, and stacked models for accurate prediction of total organic carbon from conventional well logs
Bibliographic record
Abstract
Rock-Eval pyrolysis provides accurate total organic carbon (TOC) measurements but are expensive, time-intensive, and reliant on the availability and quality of rock samples. The Passey method, a well log-based approach, serves as an alternative; however, it often underestimates TOC compared to laboratory analyses. To address these limitations, this study introduces several innovative machine learning (ML) frameworks for TOC estimation, such as ExtraTrees (ET), Gradient Boosting (GB), and XGBoost (XGB), along with three stacked hybrid models (HM1, HM2, and HM3) that integrate ET, GB, and XGB in different combinations, resulting in a total of six distinct models. Except for XGBoost, these models have not been previously applied to TOC prediction, underscoring the novelty of this study. Among the models, ET achieves the maximum prediction accuracy, with a correlation coefficient (R²) of 0.9825 and root mean square error (RMSE) of 0.2483, followed closely by GB, which yields similar performance. A feature importance analysis, conducted using the best-performing ET model through sequential parameter exclusion, identifies gamma ray as the most influential predictor, while resistivity has the least impact on TOC estimation. The Passey method exhibits significantly lower predictive accuracy (R² = 0.621, RMSE = 1.018), further demonstrating the superiority of ML models. The evaluation of TOC is conducted here utilizing 125 core samples and well log data from the Shahejie Formation in the Dongying Depression, Bohai Bay, China. Additionally, the proposed methodology has been evaluated in an entirely new region located in Alberta, Canada, to improve the generalizability of this work. • Six machine learning models have been developed for TOC prediction. • ExtraTrees performed best among the proposed models. • Traditional Passey method exhibited significantly lower predictive accuracy. • Acoustic log is the most significant feature, while neutron is the least. • Validation of the proposed methodology in an entirely new field is performed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".