On a Deep Learning Method of Estimating Reservoir Porosity

Abstract

Porosity is an important parameter for the oil and gas storage, which reflects the geological characteristics of different historical periods. The logging parameters obtained from deep to shallow strata show the stratigraphic sedimentary characteristics in different geological periods, so there is a strong nonlinear mapping relationship between porosity and logging parameters. It is very important to make full use of logging parameters to predict the shale content and porosity of the reservoir for precise reservoir description. Deep neural network technology has strong data structure mining ability and has been applied to shale content prediction in recent years. In fact, the gated recurrent unit (GRU) neural network has further advantage in processing serialized data. Therefore, this study proposes a method to predict porosity by combining multiple logging parameters based on the GRU neural network. Firstly, the correlation measurement method based on Copula function is used to select the logging parameters most relevant to porosity parameters. Then, the GRU neural network is used to identify the nonlinear mapping relationship between logging data and porosity parameters. The application results in an exploration area of the Ordos basin show that this method is superior to multiple regression analysis and recurrent neural network method, which indicates that the GRU neural network is more effective in predicting a series of reservoir parameters such as porosity.