Evaluation of shear wave velocity prediction by rock physics and artificial neural networks, in one of the south Iranian oil reservoirs

Based on the extensive studies, undoubtedly, the role of the shear wave data in hydrocarbon reservoir evaluation is vital. Using shear wave along with P-wave data often allows us to identify the seismic signatures of lithology, pore fluid type and pore pressure, efficiently. Unfortunately shear wave data is not available in all reservoirs and it is necessary to predict it. This study has done on a well in one of the oil reservoirs in south of Iran that has shear wave velocity measurements. In this study shear wave velocity had predicted by rock physics relations and neural networks and then the results compared with real shear wave velocity measurements. Regression between predicted shear wave velocity by using rock physics relations and measured shear wave velocity is about 0.91, whereas it is about 0.95 by using neural networks. Results confirm that both methods are suitable for predicting shear wave velocity in other wells in this reservoir.