Explainable Prediction Model of Logging Lithology Classification Based on XGBoost and SHAP
Author:
Affiliation:
1. China Research Institute of Radio Wave Propagation,Xinxiang,China
2. China National Logging Corporation International Company,Beijing,China
Funder
National Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10403657/10403658/10403708.pdf?arnumber=10403708
Reference20 articles.
1. Well log interpretation model for the determination of lithology and fluid;Akinyokun;The Pacific Journal of Science and Technology,2009
2. Lithological identification of volcanic rocks from SVM well logging data: case study in the eastern depression of Liaohe Basin;MOU;Chinese Journal of Geophysics,2015
3. Lithology identification using an optimized KNN clustering method based on entropy-weighed cosine distance in Mesozoic strata of Gaoqing field, Jiyang depression
4. An improved method for lithology identification based on a hidden Markov model and random forests
5. Automatic Lithology Identification by Applying LSTM to Logging Data: A Case Study in X Tight Rock Reservoirs
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