Logging while drilling（LWD）technologies are employed in many oilfields to reduce the impact of mud intrusion on logging curves，which require the prediction of logging curves for undrilled formations as it is of great guiding significance to LWD. Therefore，a method based on the gated recurrent unit（GRU）neural network was applied to predict the logging curves of undrilled formations. The model combines the input gate and forget gate of long short-term memory（LSTM）into an update gate and turns the input gate into a reset gate，which makes its structure simple and not prone to overfitting.Meanwhile，it retains the long-term memory function of the LSTM model and can effectively alleviate the problem of gradient vanishing or explosion. Taking real logging data from vertical wells in Xinjiang Oilfield and LWD data in western South China Sea Oilfield as examples，this study selected the five logging curves of drilled formations and adjoining wells，namely，the curves of the natural gamma ray，deep induction resistivity，acoustic time difference，density，and well diameter，as training samples and input into the LSTM model and GRU model for learning training. The trained models were then used to predict the logging curves of undrilled formations. The application results indicate that the average correlation coefficients of predicted logging curves for Xinjiang Oilfield and western South China Sea Oilfield by the GRU model are 13.78% and 12.13% higher than that of the LSTM model，and the mean root mean square errors are decreased by 27.08% and 42.17%，respectively. The GRU model can accurately predict the variation trend of logging curves for undrilled formations.