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...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/12/6868 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849433070332018688 |
|---|---|
| author | Wenxuan Yu Xizhe Li Wei Guo Hongming Zhan Xuefeng Yang Yongyang Liu Xiangyang Pei Weikang He Longyi Wang Yaoqiang Lin |
| author_facet | Wenxuan Yu Xizhe Li Wei Guo Hongming Zhan Xuefeng Yang Yongyang Liu Xiangyang Pei Weikang He Longyi Wang Yaoqiang Lin |
| author_sort | Wenxuan Yu |
| collection | DOAJ |
| description | 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 situ stress in shale reservoirs. Based on the logging data from five wells in the Luzhou Block of the Sichuan Basin (16,000 samples), Recursive Feature Elimination (RF-RFE) was used to identify nine key factors, including Stoneley wave slowness and caliper, from 30 feature parameters. Bayesian optimization was employed to fine-tune the hyperparameters of the XGBoost model globally. Results indicate that the XGBoost model performs optimally in predicting maximum horizontal principal stress (SHmax) and minimum horizontal principal stress (SHmin). It achieves R<sup>2</sup> values of 0.978 and 0.959, respectively, on the test set. The error metrics (MAE, MSE, RMSE) of the XGBoost model are significantly lower than those of SVM and Random Forest, demonstrating its precise capture of the nonlinear relationships between logging parameters and in situ stress. This framework enhances the model’s adaptability to complex geological conditions through multi-well data training and eliminating redundant features, providing a reliable tool for hydraulic fracturing design and wellbore stability assessment in shale gas development. |
| format | Article |
| id | doaj-art-325a081645bd476aae48c91ec83d3f20 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-325a081645bd476aae48c91ec83d3f202025-08-20T03:27:11ZengMDPI AGApplied Sciences2076-34172025-06-011512686810.3390/app15126868Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning ModelsWenxuan Yu0Xizhe Li1Wei Guo2Hongming Zhan3Xuefeng Yang4Yongyang Liu5Xiangyang Pei6Weikang He7Longyi Wang8Yaoqiang Lin9Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaShale Gas Research Institute of Southwest Oil & Gas Field Branch, Chengdu 610051, ChinaShale Gas Research Institute of Southwest Oil & Gas Field Branch, Chengdu 610051, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaTo 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 situ stress in shale reservoirs. Based on the logging data from five wells in the Luzhou Block of the Sichuan Basin (16,000 samples), Recursive Feature Elimination (RF-RFE) was used to identify nine key factors, including Stoneley wave slowness and caliper, from 30 feature parameters. Bayesian optimization was employed to fine-tune the hyperparameters of the XGBoost model globally. Results indicate that the XGBoost model performs optimally in predicting maximum horizontal principal stress (SHmax) and minimum horizontal principal stress (SHmin). It achieves R<sup>2</sup> values of 0.978 and 0.959, respectively, on the test set. The error metrics (MAE, MSE, RMSE) of the XGBoost model are significantly lower than those of SVM and Random Forest, demonstrating its precise capture of the nonlinear relationships between logging parameters and in situ stress. This framework enhances the model’s adaptability to complex geological conditions through multi-well data training and eliminating redundant features, providing a reliable tool for hydraulic fracturing design and wellbore stability assessment in shale gas development.https://www.mdpi.com/2076-3417/15/12/6868horizontal in situ stress predictionXGBoostwell logging interpretationmachine learningcomplex nonlinearityBayesian optimization |
| spellingShingle | Wenxuan Yu Xizhe Li Wei Guo Hongming Zhan Xuefeng Yang Yongyang Liu Xiangyang Pei Weikang He Longyi Wang Yaoqiang Lin Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models Applied Sciences horizontal in situ stress prediction XGBoost well logging interpretation machine learning complex nonlinearity Bayesian optimization |
| title | Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models |
| title_full | Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models |
| title_fullStr | Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models |
| title_full_unstemmed | Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models |
| title_short | Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models |
| title_sort | prediction of horizontal in situ stress in shale reservoirs based on machine learning models |
| topic | horizontal in situ stress prediction XGBoost well logging interpretation machine learning complex nonlinearity Bayesian optimization |
| url | https://www.mdpi.com/2076-3417/15/12/6868 |
| work_keys_str_mv | AT wenxuanyu predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels AT xizheli predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels AT weiguo predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels AT hongmingzhan predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels AT xuefengyang predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels AT yongyangliu predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels AT xiangyangpei predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels AT weikanghe predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels AT longyiwang predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels AT yaoqianglin predictionofhorizontalinsitustressinshalereservoirsbasedonmachinelearningmodels |