The application of agent models based on deep learning is a new direction of oil and gas reservoir simulation.Given the huge time cost of high-precision full-order oil and gas reservoir simulation，this paper adopts an Embed to Control（E2C）model to construct a deep learning network through the architecture of“encoder+linear converter+decoder”. Data of the pressure field and saturation field at the original moment are integrated with well control constraints to generate the field data at a new moment. The PY35-1 Gas Field in the east of the South China Sea is discussed as an example to test the differences between the simulation results of the E2C model and those of the traditional numerical simulator. The test results show that the E2C model has smaller errors，with a relative error in the saturation field of less than 5% and an average relative error in the pressure field of 8%. Under the same CPU，the E2C model takes 16 s to run 100 cases，which is 375 times faster than the traditional numerical simulator（the time cost is 6 000 s）. In conclusion，the E2C model can greatly reduce the time cost under the condition of ensuring simulation precision.