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SUMMARY Porosity has been estimated in the gas hydrate bearing sediments in Krishna-Godavari (K-G) basin at site National Gas Hydrate Programme (NGHP) 01-10 using multilayer feed forward neural network (MLFN). A series of elastic parameters namely P-wave velocity (Vp), S-wave velocity (Vs), density (Dn), Vp/Vs, P impedance (Zp) and S impedance (Zs) have been derived from seismic data using pre-stack inversion. The porosity (O_D) estimated from the density log for the depth interval 1024-1224m is ranging from 51.6 to 82.6 %. The estimated porosity has been treated as target log and Zp, Zs, Vp/Vs and Dn have been used as input parameters during the training of MLFN. The trained network is then used to train the network to generate subsurface porosity along a 2D multi channel seismic section (line-16) at site NGHP01-10 within the time interval of 1.433 – 1.631s corresponding to the depth interval of 1024-1224m. The predicted porosity is decreasing in gas hydrate stability zone (GHSZ) and is varying laterally. Porosity is increased in the free gas bearing zone below the base of GHSZ

Application of Multilayer Feed Forward Neural Network - Porosity Mapping in Gas Hydrate Sediment of K-G Basin, India