Today, geomechanics and accurate estimation of geomechanical parameters have played a significant role in various stages of petroleum studies. The aim of this study is to estimate geomechanical parameters using log data and MLP algorithm in one of the hydrocarbon field wells in southwest Iran. In order to estimate geomechanical parameters, one of the important parameters is shear wave velocity, which is estimated in this article using multilayer perceptron (MLP) neural network algorithm and experimental relationships. Considering the better estimation of MLP algorithm in training, test and blind data, its output has been used to estimate subsequent studies. The value of error (MSE) and coefficient of determination (R2) of the blind data are 0.0013 and 0.8875 respectively. Next, Young's modulus and Poisson's ratio were calculated and dynamic brittleness index was calculated using these two parameters. In the next step, the uniaxial compressive strength, tensile strength were calculated and then the static brittleness index was calculated and the relationship between the dynamic brittleness index and the static brittleness index was investigated. The brittleness index was then calculated using the volume percentage of minerals and compared with the dynamic and static brittleness index values. The results show a good relation between the dynamic and static brittleness index obtained using the predicted shear wave velocity from MLP algorithm and the brittleness index obtained from the volume percentage of minerals.