A Prediction Method for Heterogeneous Reservoir Parameters Based on Transfer Learning
To address the issue of traditional methods neglecting flow mechanisms and parameter correlations,a pre-diction model of reservoir parametert hat integrates seepage theory with transfer learning is proposed.By using the oversampling algorithm of SMOTE,the issue of imbalanced samples is effectively addressed.The discriminative mod-el of lithology andseepage capacity is established by using random forest,which provides the information of seepage mechanism for reservoir parameter predicting.Combined with parameter correlation,the technology of transfer learn-ingis used to build a reservoir parameter prediction model.The results show that the correlation analysis between res-ervoir parameters can be conducted by introducing lithology and seepage capacity discrimination technology,which can effectively improve the prediction accuracy of reservoir parameters.The prediction error of the model in porosity and permeability parameters is 3.51％and 15.17％,respectively,and the prediction accuracy is significantly im-proved.This method can effectively address the issues of parameters prediction in heterogeneous reservoir,and pro-vide reference for the research combining artificial intelligence technology with physical models.
万方数据
维普
