Prediction of reservoir porosity, permeability, and saturation based on a gated recurrent unit neural network

Porosity, permeability, and saturation are important parameters to characterize and evaluate reservoirs.The interpretation of well logging data can be used to evaluate the porosity, permeability, and saturation parameters of the rock, and thus discover reservoirs.A method for predicting reservoir porosity, permeability, and saturation based on a gated recurrent unit(GRU) neural network was proposed in this study.The GRU neural network is a novel deep learning algorithm suitable for solving nonlinear and time-dependent problems.As such, it may also be able to identify the non-linear mapping relationship between porosity, permeability, and saturation parameters and logging data, as well as the correlations among historical data at different depths.First, a correlation measurement method based on a copula function was employed in this work to select the logging parameters that best correlate with the porosity, permeability, and saturation parameters.Then, the GRU neural network was used to identify the non-linear mapping relationship between logging data and porosity, permeability, and saturation parameters.The results of the application to an exploration area in the Sichuan basin showed that the proposed method was superior to multiple regression analysis and recurrent neural network methods with respect to two evaluation indexes, namely the root mean square error and the correlation coefficient.