Deep learning has been widely used in various aspects of oil and gas field development. Nevertheless，purely data-driven deep learning models suffer from large amounts of required data，unstable prediction ability，and weak generalization ability，and such models fail to consider the physical laws underlying the data. For dynamic reservoir pressure prediction，a deep neural network model for pressure field prediction driven jointly by reservoir seepage physics and data was constructed，and the mathematical seepage model of heterogeneous reservoirs was added to the loss function through regularization，which enabled the model to conform to both the results of data training and the constraint of the seepage physics equations. The results show that the jointly driven deep neural network model can achieve efficient learning and accurate prediction of pressure field data. Compared with the purely data-driven deep neural network model，the jointly driven model reduces the error（L2）between predicted and reference values by 93.1% and increases the decision coefficient（R2）by 20.3%.In the case of noisy observed data，the jointly driven model can still maintain high stability with strong noise resistance.