In this study, the neural network is used to estimate the amount of water stored in a porous reservoir from seismic data. To generate the training data for the neural network, a coupled poroviscoelastic-viscoelastic wave propagation model is solved using a three-dimensional (3D) discontinuous Galerkin method coupled with an Adams-Bashforth time stepping scheme. In addition, the effect of the unknown source wavelet is normalized using a deconvolution-based approach. Results indicate that the proposed neural network approach is applicable to estimate the wave content of a porous reservoir with a variety of noise amplitudes while uninteresting parameters can be successfully ignored.