Prediction of Lithofacies in Heterogeneous Shale Reservoirs Based on a Robust Stacking Machine Learning Model DOI Open Access
Sizhong Peng, Congjun Feng, Zhen Qiu

et al.

Minerals, Journal Year: 2025, Volume and Issue: 15(3), P. 240 - 240

Published: Feb. 26, 2025

The lithofacies of a reservoir contain key information such as rock lithology, sedimentary structures, and mineral composition. Accurate prediction shale is crucial for identifying sweet spots oil gas development. However, obtaining through core sampling during drilling challenging, the accuracy traditional logging curve intersection methods insufficient. To efficiently accurately predict lithofacies, this study proposes hybrid model called Stacking, which combines four classifiers: Random Forest, HistGradient Boosting, Extreme Gradient Categorical Boosting. employs Grid Search Method to automatically search optimal hyperparameters, using classifiers base learners. predictions from these learners are then used new features, Logistic Regression serves final meta-classifier prediction. A total 3323 data points were collected six wells train test model, with performance evaluated on two blind that not involved in training process. results indicate stacking predicts achieving an Accuracy, Recall, Precision, F1 Score 0.9587, 0.959, respectively, set. This achievement provides technical support evaluation spot exploration.

Language: Английский

State of Health Estimation of Lithium-Ion Batteries Based on Feature Optimization and Data-Driven Models DOI

G. G. Mu,

Qingguo Wei,

Yonghong Xu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134578 - 134578

Published: Jan. 1, 2025

Language: Английский

Citations

3
A novel dual-stage grey-box stacking method for significantly improving the extrapolation performance of ship fuel consumption prediction models DOI
Zhang Ruan, Lianzhong Huang,

Daize Li

et al.

Energy, Journal Year: 2025, Volume and Issue: 318, P. 134927 - 134927

Published: Feb. 7, 2025

Language: Английский

Citations

0
Prior task aware-augmented meta learning for early state-of-health estimation of lithium-ion batteries DOI
Jing Yang,

Minglan Zhang,

Xiaomin Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135648 - 135648

Published: March 1, 2025

Language: Английский

Citations

0
Prediction of Lithofacies in Heterogeneous Shale Reservoirs Based on a Robust Stacking Machine Learning Model DOI Open Access
Sizhong Peng, Congjun Feng, Zhen Qiu

et al.

Minerals, Journal Year: 2025, Volume and Issue: 15(3), P. 240 - 240

Published: Feb. 26, 2025

The lithofacies of a reservoir contain key information such as rock lithology, sedimentary structures, and mineral composition. Accurate prediction shale is crucial for identifying sweet spots oil gas development. However, obtaining through core sampling during drilling challenging, the accuracy traditional logging curve intersection methods insufficient. To efficiently accurately predict lithofacies, this study proposes hybrid model called Stacking, which combines four classifiers: Random Forest, HistGradient Boosting, Extreme Gradient Categorical Boosting. employs Grid Search Method to automatically search optimal hyperparameters, using classifiers base learners. predictions from these learners are then used new features, Logistic Regression serves final meta-classifier prediction. A total 3323 data points were collected six wells train test model, with performance evaluated on two blind that not involved in training process. results indicate stacking predicts achieving an Accuracy, Recall, Precision, F1 Score 0.9587, 0.959, respectively, set. This achievement provides technical support evaluation spot exploration.

Language: Английский

Citations

0