Gated recurrent unit neural network (GRU) based on quantile regression (QR) predicts reservoir parameters through well logging data

Abstract

The prediction of reservoir parameters is the most important part of reservoir evaluation, and porosity is very important among many reservoir parameters. In order to accurately measure the porosity of the core, it is necessary to take cores for indoor experiments, which is tedious and difficult. To solve this problem, this paper introduces machine learning models to estimate porosity through logging parameters. In this paper, gated recurrent unit neural network based on quantile regression method is introduced to predict porosity. Porosity measurement is implemented by taking cores for indoor experiments. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity parameters measured in the laboratory are used as the output parameters. Experimental results show that the quantile regression method improves the accuracy of the gated recurrent unit neural network, and the RMSE (Root Mean Square Error) of the unoptimized GRU neural network is 0.1774, after optimization, the RMSE is 0.1061. By comparing with the most widely used BP neural network, the accuracy of the method proposed in this paper is much higher than that of BP neural network. This shows that the gated recurrent neural network method based on quantile regression is excellent in predicting reservoir parameters.