A novel method for the inversion of the virtual well-log interval transit time

By analyzing the relation between well-log data and seismic data, a novel method for predicting the virtual well-log interval transit time based on particle swarm optimization and support vector machine (PSO-SVM) is proposed. A prediction model for the virtual well-log interval transit time is established using the data of seismic of the un-drilled well, seismic and well-log of the drilled well by training the SVM, which is optimized by PSO algorithm. The proposed method is applied to the well YONG of Junggar Basin and the experimental results show it has higher prediction accuracy, faster convergence speed and better generalization than BP neural network approach.