Coalbed methane content prediction based on joint optimization of log interpretation model and mean impact value method
In order to further improve the prediction effect of Coalbed Methane(CBM)content by using the geophysical well logging technology,this study introduced the coal reservoir parameters calculated by Log Interpretation Model(LIM)into the construction process of the CBM content prediction model.And the input parameters of Support Vector Machine(SVM)were optimized based on the Mean Impact Value(MIV)method.Finally,a LIM-MIV-SVM model for predicting CBM content was constructed using grid search method.And the CBM content prediction effects of the LIM-MIV-SVM model were compared with multiple regression model,conventional logging SVM model and LIM-SVM model by using the actual logging data from Huainan coalfield.The application results show that the proposed LIM-MIV-SVM model has the highest prediction accuracy,followed by the LIM-SVM model and the conventional logging SVM model,and the multiple regression model has the lowest prediction accuracy.This indicates that machine learning method have advantages over traditional logging interpretation method,and introducing gas content parameters calculated by LIM reasonably is effective for improving the prediction accuracy of CBM content.The LIM-MIV-SVM model is jointly optimized through multi-source logging data fusion and input dataset selection,which can provide technical support for CBM resource exploration and reservoir evaluation.Moreover,this research method and modeling strategy can be suitable for other machine learning modeling research fields.
万方数据
维普
