西安石油大学学报（自然科学版）2024，Vol.39Issue(4)：108-116,9.DOI:10.3969/j.issn.1673-064X.2024.04.015
基于小波变换和CNN-Transformer模型的测井储层流体识别
Reservoir Fluid Identification Model Based on Wavelet Transform and CNN-Transformer
摘要
Abstract
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.关键词
流体识别/测井曲线/小波变换/CNN-TransformerKey words
fluid recognition/logging curve/wavelet transform/CNN-transformer分类
能源科技引用本文复制引用
龚安,张恒..基于小波变换和CNN-Transformer模型的测井储层流体识别[J].西安石油大学学报（自然科学版）,2024,39(4):108-116,9.基金项目
中石油重大科技项目"多级压裂水平井产能评价规律及应用"(ZD2019-183-004) （ZD2019-183-004）
中央高校基本科研业务费专项资金"人工智能非常规油藏储层评价算法研究"(20CX05019A) （20CX05019A）
