Abstract

Brittleness coefficient and in-situ stress have been estimated from well log data linking post-stack seismic data in the unconventional Raghavapuram Shale reservoir of Krishna-Godavari basin, India. Inverted acoustic impedance, shear impedance, density and compressional to shear wave velocity ratio (Vp/Vs) have been used to train the multilayer feedforward neural network (MLFN) model for mapping pore pressure and vertical stress obtained from well logs. Minimum horizontal stress is computed for anisotropic shale medium. Brittleness coefficient estimated from static Young