Estimation of Fracture Properties From Azimuthal Seismic Data Using Convolution Neural Network

Fracture weaknesses represent two critical elastic parameters utilized for characterizing fracture properties in naturally fractured reservoirs. Given the intricate seismic attributes associated with amplitude variation with angles of incidence and azimuth (AVAZ) in fractured reservoirs, accurately delineating the mapping relationships between azimuthal seismic data and fracture weaknesses in analytic form poses a significant challenge. Leveraging neural networks offers a nonlinear mechanism to bridge this gap. Initially, we establish a forward model by employing convolution operations between the azimuthal PP-wave (incident and reflected P-wave) reflection coefficient equation in transversely isotropic (HTI) media with a horizontal axis of symmetry and seismic statistical wavelets. This foundation enables the synthesis of azimuth-dependent seismic data. Subsequently, a convolution neural network (CNN) is constructed to predict subsurface rock fracture properties from azimuthal pre-stack seismic data. To quantify the uncertainty associated with neural network estimation, we employ the approximate Bayesian computation (ABC) method to determine the posterior distribution of model parameters. Finally, we present the application of both synthetic and filed data. Our results indicate a correlation of 90% and 86.8% between the synthetic model and the blind well, respectively. Furthermore, the estimated posterior distribution serves to validate the constraint capability of the proposed method, thereby furnishing comprehensive evidence supporting the feasibility and robustness of our approach.