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This work focuses on a 3D supervised deep machine learning method for predicting reservoir elastic properties from partial-angle stack seismic data. Using well-log data from several well locations from Troll field in the North sea and 3D interval velocity volume, we train a deep convolutional neural network to estimate elastic properties such as P and S waves velocities and density from seismic partial stacks. Furthermore, we have also make a comparison between the deep learning predictions with the model-based seismic inversion methods including Bayesian linearized inversion and stochastic nonlinear inversion schemes. The applications tests on well logs and seismic field data demonstrate that deep learning method can effectively predict property volumes with good lateral continuity as compared to traditional model based inversion techniques. Estimation of these elastic properties allows obtaining other rock properties such as porosity, saturation, and shale volume, which are important for reservoir rock characterization.