In this study, a new workflow that integrates well data, multi-seismic attributes and machine learning models has been developed in the WDDM of Egypt. This workflow is used to interpret seismic data and identify channels and gas zones, as well as distinguish gas chimneys from normal seismic reflectors. The gas zones of the studied channels are identified using supervised neural network analysis, which was supported by multi-seismic attributes. This analysis allowed for the prediction and classification of both gas zones and chimneys. The elastic characteristics of the studied reservoir and their relationship to porosity and pore fluids are predicted using the fluid substitution method in corporation with machine learning techniques.