基于小波变换和CNN-Transformer模型的测井储层流体识别OA北大核心CSTPCD

Low-porosity low-permeability reservoirs have complex storage spaces and strong heterogeneity,and the characteristics of conventional logging responses are not clear enough,which makes it difficult to effectively identify reservoir fluids using traditional in-terpretation methods.For this purpose,a hybrid model combining wavelet transform and CNN-Transformer is proposed for reservoir fluid identification.Firstly,the logging signal is extended from time domain to time-frequency domain using wavelet transform,and a time-fre-quency spectrum is generated to enhance the features of the logging signal.Then,the sliding sampling is carried out along the depth di-rection of the logging curve by sliding time window to obtain a spectral feature map representing the formation information at interpreta-tion depth.Finally,the information in the spectral feature map is deeply mined by training the CNN transformer model to achieve the i-dentification of reservoir fluid.The model was applied to the logging curves of 22 wells in Dagang Oilfield,and its fluid identification re-sults were compared with those of multiple models such as CNN and BiLSTM.The results showed that the recognition performance of this model was significantly better than other models,with a recognition accuracy of 92.7％on the test set.The research results indicate that this method can be an effective means of identifying reservoir fluids using conventional logging data in low porosity and permeability reservoirs,providing new ideas for fluid evaluation.