Most of the current 3D reservoir porosity estimation methods are based on analyzing the elastic parameters inverted from seismic data. It is well-known that elastic parameters vary with the pore structure parameters such as the pore aspect ratio, consolidation coefficient, and critical porosity. Thus, we may obtain inaccurate 3D porosity estimation if the chosen rock-physics model fails to properly address the effects of the pore structure parameters on the elastic parameters. However, most of the current rock-physics models only consider one pore structure parameter such as the pore aspect ratio or the consolidation coefficient. To consider the effect of multiple pore structure parameters on the elastic parameters, we have developed a comprehensive pore structure (CPS) parameter set that is generalized from current popular rock-physics models. The new CPS set is based on the first-order approximation of current rock-physics models that consider the effect of the pore aspect ratio on the elastic parameters. The new CPS set can accurately simulate the behavior of current rock-physics models that consider the effect of pore structure parameters on the elastic parameters. To demonstrate the effectiveness of the proposed parameters in porosity estimation, we use a theoretical model to demonstrate that our CPS parameter set properly addresses the effect of pore aspect ratio on elastic parameters such as velocity and porosity. Then, we obtain a 3D porosity estimation for a tight sand reservoir by applying it seismic data. We also predict the porosity of the tight sand reservoir using a neural network algorithm and a rock-physics model that is commonly used in porosity estimation. The comparison demonstrates that the predicted porosity has a higher correlation with the porosity logs at blind well locations.