Hybrid wavelet transform with artificial neural network for forecasting of shear wave velocity from wireline log data: a case study

Shear wave velocity (Vs) is an important variable for performing geomechanical and geophysical modeling and reservoir studies. Field tests to measure this variable directly are high costs and time consuming. Due to the operational difficulties mentioned above, it is more convenient estimating Vs without direct measurements from conventional log data. In this research, the hybrid of wavelet transform with artificial neural network is utilized to estimate the Vs. To input variables (log gamma, log compressional wave velocity, and log bulk density), preprocessing is done using wavelet transform and then variables are entered to artificial neural network model. The estimation abilities of the hybrid artificial neural network with wavelet transform were substantiated using field data achieved from Marun reservoir, Iran. The results obtained in this study show a positive effect of input parameters’ preprocessing using wavelet transform in the estimation of Vs, and it has led to noticeable increase in the accuracy of model calculations.