A method for fracability evaluation of continental shale oil reservoir based on deep learning
Considering the excellent performance of traditional convolutional neural network(CNN)in extracting local features,the ability of bidirectional long and short-term memory neural network(BiLSTM)to better capture bidirectional dependencies and geological varia-tions in logging data,as well as the attentional mechanism to enhance the perceptual ability of important information,the advantages of all three are combined,and a deep hybrid neural network(CBiLSTM-AT)is proposed.The experimental results show that the fracability e-valuation method based on uniform manifold approximation and projection(UMAP)is more suitable for continental shale oil reservoirs than the traditional method.CBiLSTM-AT outper-forms the traditional neural network model and achieves higher prediction accuracy.The proposed method is more efficient and accurate,and provides effective technical support for reservoir stimulation.
NETL
NSTL
万方数据
维普
