Parameter interpretations of wave dispersion and attenuation in rock physics based on deep neural network

Acoustic wave features, including velocity dispersion and attenuation, induced by fluid flow in porous media have attracted significant attention in reservoir exploration. To enhance the quantitative understanding of these features, various wave propagation mechanisms have been developed. It has been discovered that wave dispersion and attenuation are associated with multiple reservoir parameters, each with different sensitivity. It is difficult to distinguish the effects of individual physical parameters on acoustic features by traditional wave equations. Considering the ability of deep neural networks (DNNs) in establishing the relationships between two datasets, a fully connected DNN has been employed as a surrogate rock physics model, and the Shapley additive explanations model (SHAP) based on this DNN has been introduced to evaluate the contributions of different parameters. In this study, the classic White model is used to generate datasets for training the DNN. Datasets include seven parameters (bulk modulus, shear modulus, and density of the solid matrix, frequency, porosity, fluid saturation, and permeability), along with velocity dispersion and attenuation. By embedding SHAP into the trained DNN, the presented ShaRock algorithm allows for a clear quantification of the contributions of various reservoir parameters to acoustic features. Furthermore, we analyze the underlying interactions between two parameters by using their combined quantified contributions to the features. The application of this proposed algorithm, which is based on wave propagation mechanisms, demonstrates its potential in providing valuable insights for parameter inversions in hydrocarbon exploration.