| Citation: | LI Zihao，JIANG Shu. Reconstructing and interpreting analysis of sonic logging curves based on machine learning and SHAP algorithm[J]. Bulletin of Geological Science and Technology，2025，44（1）：321-331 doi: 10.19509/j.cnki.dzkq.tb20230504
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Well logging techniques is cruicial for determining subsurface lithological characteristics and geological structures, which plays a pivotal role in the petroleum exploration industry. However, issues such as instrument damage and wellbore conditions frequently lead to data gapping or incomplete curves of well logs. Traditional multivariate linear regression or empirical formula fail to construct a reasonable relationship model among well logging curves, resulting in a relatively low reconstruction accuracy. Although machine learning algorithms are able to find the most appropriate mapping relationship between a large amount of data to improve the model accuracy, the black-box characteristics cannot be well explained.
In this work, support vector regression (SVR), random forest (RF), and eXtreme gradient boosting (XGBoost) are compared with traditional multiple linear regression (LR) to reconstruct the acoustic logging curve of the NDR well 22-30b-11, and the XGBoost model is interpreted based on shapley additive explanations (SHAP) algorithm.
Results demonstrate that XGBoost outperforms SVR and RF on the test set, achieving
Results show that the machine learning algorithm is significantly better than the traditional multiple linear regression for logging curve reconstruction, indicating the feasibility of the SHAP algorithm in the interpretation of machine learning models for logging curve reconstruction, which provides a new idea for the subsequent development of machine learning in logging techniques.
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