Abstract: Shear wave information plays an important role in oil and gas reservoir exploration and development, but is often lacking in actual logging data.There is a relationship between reservoir parameters and shear wave velocity, but it is too complex to obtain analytical solutions.Considering the correlation between the shear wave velocity and other reservoir parameters, shear wave velocity prediction can be realized by deep learning.In this study, we propose a shear wave velocity prediction method based on the attention mechanism and bidirectional long short-term memory network (AT-BLSTM).First, the weight of logs is automatically assigned by the attention mechanism, and the logs that contribute the most to shear wave velocity prediction become the focus, whereas the logs with low sensitivity are ignored.In other words, human supervision can be avoided by using the proposed method when selecting features for shear wave prediction.Next, the influence of the upper and lower formation parameters on the shear velocity and the sequence characteristics of the longitudinal log data were fully considered to obtain the learning model that relates the shear velocity to the other parameters.Lastly, the optimal reservoir parameters can serve as the input in the learning model, and the prediction results of the shear wave velocity can thus be obtained.The proposed method was applied to the actual well logging data of the Volve oilfield on the Norwegian continental shelf and a land area in southwest China, and the results were compared with those obtained using the conventional gate recurrent unit neural network, bidirectional long short-term memory network (LSTN), and the conventional prediction method based on empirical formulas.The results showed that the error between the results predicted by the model and the measured shear wave velocity was smaller and the correlation coefficient was higher, demonstrating that the proposed method can effectively reduce the influence of manual characteristic curve selection and improve the shear wave prediction accuracy.