Abstract: Abstract: In order to improve the accuracy of lithofacies identification， this paper developed a heterogeneous reservoir lithofacies intelligent identification model based on residual connection long short-term memory network （LSTM_res）. Firstly， a sequence feature module is constructed based on long short-term memory neural network to obtain key logging features. The multi-layer stacking of this module further enhances the model’s ability to extract key feature information. Secondly， the residual connection technology is introduced on the basis of the sequence feature module to realize the extraction and fusion of the feature information between different layers of the network， which can effectively solve the degradation problem of the deep neural network. Finally， taking the logging data in the shallow sea area of the North Sea near Norway as the research object， six logging parameters （RMED， RHOB， GR， NPHI， PEF and SP） are selected through sensitivity analysis of logging parameters to realize intelligent identification of reservoir lithofacies. Compared with LSTM， CNN_res and CNN models under the same conditions， the experimental results show that the lithofacies identification accuracy of LSTM_res model is improved by 2， 4 and 6 porcentage points， respectively. It provides fast and effective data support for reservoir modeling and geological research.

Key words: Key words: long short-term memory neural network, residual connection, lithofacies identification, logging data, sensitivity analysis of logging parameters