Porosity inversion by Caianiello neural networks with Levenberg-Marquardt optimization

Abstract

Caianiello neural networks (CNNs) incorporated with the Robinson seismic convolutional model are modified by the Levenberg-Marquardt algorithm to improve convergence. CNNs are extended to the multiattribute domain for reservoir property inversion, with time-varying signal processing by a frequency-domain block implementation using fast Fourier transforms. Optimal inversion can be achieved by applying the Levenberg-Marquardt optimization to multiattribute domain CNNs for convergency improvement due to its ability to swing between the steepest-descent and Gauss-Newton algorithms. The methodology is applied to porosity estimation in an oilfield with six wells in the Bohai Basin of China. Cross-validation results indicate significant correlation between actual porosity logs and predicted porosity logs. Compared with a traditional method, our technique is robust.