Interpretable machine learning model for shear wave estimation in a carbonate reservoir using LightGBM and SHAP: a case study in the Amu Darya right bank

Frontiers in Earth Science

Earth Science

Hui Chai¹  Tongcui Guo²  Liangjie Zhang²  Tianze Zhang²  Wenqi Zhang²  Hongjun Wang² 

[1] China National Petroleum Corporation International Ltd., Ashgabat, Turkmenistan;China National Petroleum Corporation, Research Institute of Petroleum Exploration and Development, Beijing, China;

关键词: carbonate reservoir;    S-wave velocity estimation;    machine learning;    lightgbm;    shap;   

DOI  :  10.3389/feart.2023.1217384

 received in 2023-05-05, accepted in 2023-09-26,  发布年份 2023

来源: Frontiers

【 摘 要 】

The shear wave velocity (Vs) is significant for quantitative seismic interpretation. Although numerous studies have proved the effectiveness of the machine learning method in estimating the Vs using well-logging parameters, the real-world application is still hindered because of the black-box nature of machine learning models. With the rapid development of the interpretable machine learning (ML) technique, the drawback of ML can be overcome by various interpretation methods. This study applies the Light Gradient Boosting Machine (LightGBM) to predict the Vs of a carbonate reservoir and uses the Shapley Additive Explanations (SHAP) to interpret the model. The application of ML in Vs estimation normally involves using conventional well-log data that are highly correlated with Vs to train the model. To expand the model’s applicability in wells that lack essential logs, such as the density and neutron logs, we introduce three geologically important features, temperature, pressure, and formation, into the model. The LightGBM model is tuned by the automatic hyperparameter optimization framework; the result is compared with the Xu-Payne rock physics model and four machine learning models tuned with the same process. The results show that the LightGBM model can fit the training data and provide accurate predictions in the test well. The model outperforms the rock physics model and other ML models in both accuracy and training time. The SHAP analysis provides a detailed explanation of the contribution of each input variable to the model and demonstrates the variation of feature contribution in different reservoir conditions. Moreover, the validity of the LightGBM model is further proved by the consistency of the deduced information from feature dependency with the geological understanding of the carbonate formation. The study demonstrates that the newly added features can effectively improve model performance, and the importance of the input feature is not necessarily related to its correlation with Vs

【 授权许可】

Unknown   
Copyright © 2023 Zhang, Chai, Wang, Guo, Zhang and Zhang.