Efficient coupling of high-fidelity simulation models and history-monitored data to predict future migration behavior of CO2plume is crucial for leakage risk management and assessment during the process of geological CO2sequestration (GCS). However, the conventional model-based inverse modeling and forecasting procedure generally presents intensive computation-cost due to numerous high-fidelity model simulations and thus hinders its practical applications. We present a novel data-driven direct forecast and uncertainty quantification framework that hybridizes deep convolutional autoencoder and deep residual neural network to represent spatial distribution of subsurface fluid by latent space features and achieve predictions of future fluid dynamic behavior given history-monitored data, respectively. The linear-type data-space inversion approaches based on Bayesian theorem are generalized to non-linear model applications through deep-learning, i.e., referred to as DSI-DL. Compared to the conventional learning-based direct forecast approach (LDFA) that numerous training samples are required to guarantee predictability of the supervised machine-learning models, our proposed DSI-DL employs a data-augmentation strategy to automatically generate sufficient training samples from a relatively small ensemble of high-fidelity model simulations. The performance DSI-DL method is demonstrated on a synthetic 2D models and a practical 3D model for deep saline aquifer GCS applications. The deep-learning surrogate directly predicts the latent features of future spatial CO2plumes given history-monitored CO2plumes. Comparisons between LDFA and DSI-DL confirm that the proposed DSI-DL method obtains more robust and better results in predicting future CO2plume migration, especially when solely a few number of large-scale high-fidelity model simulations is available at hand and large observation errors exist. The proposed deep-learning-based direct forecast approach contributes to an efficient computation framework to manage uncertainty of the long term process of GCS.