On a Deep Learning Method of Estimating Reservoir Porosity

Zhenhua Zhang,Pan Wang,Yanbin Wang,Rossana Dimitri

doi:10.1155/2021/6641678

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.

Highlights

Porosity is an important physical property parameter reflecting the reservoir capacity
Various concepts related to statistics, including correlation coefficient (R), mean absolute error (MAE), variance accounted for (VAF), and root mean square error (RMSE) are used to compare performance prediction
By comparing the prediction precision of the three types deep learning models (RNN, Gated recurrent unit (GRU), and CAGRU) established in this study, it can be shown that the GRU and correlation analysis (CA)-GRU models were superior to Recurrent neural network (RNN) model in prediction precision, among which CA-GRU took the best precision due to the highest R of 0.9423 and VAF of 88.7578, and the lowest MAE of 0.2101 and RMSE of 1.1412

Summary

Introduction

Porosity is an important physical property parameter reflecting the reservoir capacity. Accurate calculation of reservoir porosity is the key work in geological interpretation and oil exploration and development. Bakhorji et al believe that the porosity parameter obtained by petrophysical analysis through core sampling is the most accurate [3], since researchers have done a lot of relevant research, and Tao et al [4] realized the reconstruction of the pore-fracture system of different marcolithotypes. The quantitative calculation of porosity usually adopts the theoretical porosity model of density, acoustic time difference, and compensated neutron logging, or establishes the regional empirical porosity model combined with core analysis [7,8,9]. From the point of view of the interpretation model, parameter selection, and mathematical processing method, it is difficult to establish a good mapping relationship between

Methods

Results

Conclusion