Prediction of S-wave velocity based on GRU neural network

There is a relationship between reservoir parameters and shear wave velocity.But it is too complex to get analytic solutions.This paper proposes a GRU (gated recurrent unit) neural network which includes building a neural network,data preprocessing,samples training and data predication.After approximating the relationship between S-wave velocity and reservoir parameters by training a neural network,S-wave velocity can be predicted directly from P-wave velocity,density,gamma ray,porosity and logarithm of resistivity.The neural network was trained and tested by logging data from 30 wells in Block D.The results show that: ①The P-wave velocity,density and logarithm of resistivity are positively related to the S-wave velocity,while the gamma ray and porosity are negatively related to the S-wave velocity; ②In the case of being trained by more wells and tested by less wells,the relative error between the S-wave velocity and the real one is about 3.00%,and the correlation coefficient is 0.9837 for training data,while they are 3.19% and 0.9805 for tested data.In the case of being trained by less wells and tested by more wellsg,the relative error is 2.49% and the correlation coefficient is 0.9867 for training data,while they are 3.92% and 0.9686 for testing data.The new method has a high prediction accuracy and generalization ability. © 2020, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.