S-wave prediction is considered as an important step in seismic and petrophysical modelling. Seismic and petrophysical modelling is the key step adopted by the hydrocarbon exploration and production industry to carry out effective strategy for the exploration of the hydrocarbons. Gassmann’s equation can be used for the prediction of elastic velocity at low frequencies in the sediments having high water saturation by incorporating the dry rock moduli. The prediction of S wave velocities is quite easy if the estimation of dry rock moduli was carried out by using the P wave velocities. Compaction constant can be used to relate the shear modulus with the dry rock bulk modulus. Several researches reveal that if the differential pressure is greater than 5 MPa the prediction S wave velocity is quite accurate in consolidated and unconsolidated sediments. In this method the prediction of S wave velocity is only controlled by the elastic logs like Density and P-wave. It is a simple method and does not involves the impact of other advanced parameters like pore aspect ratio etc.
The Vp and Vs are very important for the estimation of mechanical properties of rock formations. This aids the hydrocarbon industry in planning and execution of effective exploration and production strategy. Generally, the shear wave logs are not available in the well log data of oil well. Because the acquisition of S wave data requires high cost. So, to adopt alternative strategy for obtaining the S wave logs was being searched. This involved the correlation among the Vs with the other petrophysical logs to finds out the best suited relationship to predict the S wave velocity. The machine learning approach can be used as an efficient tool for the prediction of S wave velocity.