Due to the poor performance of pore permeability statistical regression and logging interpretation method in predicting parameters of tight sandstone reservoirs,artificial intelligence methods are widely used in predicting parameters of tight sandstone reservoirs.However,the available core data is difficult to meet the requirements of artificial intelligence for learning a large number of samples.Therefore,a virtual sample generation method based on Gaussian mixture model is proposed to solve the problem of lacking training samples.This algorithm generates virtual samples by fitting the distribution of the original samples,filling in the information gaps between small sample data.Through standard function testing,this method can ef-fectively generate training data.Comparative experiments on predicting porosity and permeability in actual work areas show that after expanding the dataset with virtual samples,the prediction accuracy of the model has increased by 9.7％and 18.6％,respectively.This indicates that the proposed method can effectively im-prove the prediction accuracy of the model under small sample condition.
维普
万方数据