Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models

Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models

To address the limitations of traditional methods in modeling complex nonlinear relationships in horizontal in situ stress prediction for shale reservoirs, this study proposes an integrated framework that combines well logging interpretation with machine learning to accurately predict horizontal in...

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Bibliographic Details
Main Authors: Wenxuan Yu, Xizhe Li, Wei Guo, Hongming Zhan, Xuefeng Yang, Yongyang Liu, Xiangyang Pei, Weikang He, Longyi Wang, Yaoqiang Lin
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
horizontal in situ stress prediction
XGBoost
well logging interpretation
machine learning
complex nonlinearity
Bayesian optimization
Online Access:https://www.mdpi.com/2076-3417/15/12/6868
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