Reservoir property prediction using the dynamic radial basis function network
Lei Li, Wei Xiong, Zhan Shifan, Wan Zhonghong · 2011
The nonlinear relationship between seismic attributes and reservoir property can be estimated by many statistical methods, such as machine learning and neural networks. However, an adaptive process for parameters learning cannot be implemented in most statistical methods. In this paper, we propose a dynamic radial basis function (D-RBF) network method to predict the reservoir property from seismic attributes. The main feature of this method is that the nonlinear relationship between the reservoir property and seismic attributes can be determined adaptively without interpreter intervention. In fact, if the current RBF network cannot reduce the prediction errors in the system, the proposed method will increase or decrease the hidden units in the RBF network to re-estimate the relationship between the reservoir property and the seismic attributes. We illustrate the proposed method using two real 3D seismic data sets. The results show that models based on D-RBF networks can predict reservoir property with high accuracy.