AI-in-well-logging
人工智能在石油测井上的应用包括采用机器学习，深度学习等相关方法进行岩性识别与相关测井曲线的回归。The application of artificial intelligence in well logging includes the use of machine learning, deep learning and other related methods for lithology identification and regression of related well logging data.
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최종 버전 다운로드 (.zip)- <Concise code>SVM Random Forests GBDT XGBoost facies classification.ipynb
- catboost美国油田_延安油田_Catboost U.S. Oilfield_Yan'an Oilfield.ipynb
- catboost美国油田_延安油田_gpu.ipynb
- Conv1d_PE回归_regression_analysis.ipynb
- Facies_Classification_SVM.ipynb
- Informer原始代码.ipynb
- LICENSE
- LSTM_simple_RNN_Bi_LSTM实现PE回归_regression_analysis.ipynb
- README.md
- resnet_无步长_延安油田.ipynb
- RNN_DNN岩性分类Lithology classification.ipynb
- SECURITY.md
- “02_Map_View_ipynb”的副本.ipynb
- 使用matplotlib_可视化_csv测井数据实现ui控制的测井曲线生成.ipynb
- 岩性分类_SVM.ipynb
- 延安油田,KNN,gbdt,随机森林,xgboost.ipynb
- 延安油田_岩性分类.ipynb
- 延安油田SMOTE,KNN,gbdt,随机森林,xgboost.ipynb
- 延安油田决策树.ipynb
- 延安油田标签传播算法半监督_Yan'an Oilfield Label Propagation Algorithm Semi-supervised.ipynb
- 测井曲线画图总结.ipynb
- 电阻率线性回归.ipynb
- 神经网络分类.ipynb
- 美国油田GMM_smote_knn_gbdt,随机森林，xgboost.ipynb
- 美国油田朴素贝叶斯.ipynb
- 超参数选择.ipynb
- 采用PCA_KPCA_LDA_做数据降维以美国油田为例.ipynb
- 采用PCA_KPCA_LDA不采用归一化_做数据降维以美国油田为例.ipynb
- 采用多元线性,岭回归,SVR,GBDT实现孔隙度回归_Use multiple linear, ridge regression, SVR, GBDT to achieve porosity regression.ipynb
// repository documentation
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