A Hybrid MPSO-BP-RBFN Model for Reservoir Lateral Prediction

Authors:
Shiwei Yu;Kejun Zhu;Xiufu Guo;Jing Wang
Affiliations:
School of Economics and Management, China Universiyt of Geosciences, Wuhan, Hubei, P.R. China 430074;School of Economics and Management, China Universiyt of Geosciences, Wuhan, Hubei, P.R. China 430074;School of Economics and Management, China Universiyt of Geosciences, Wuhan, Hubei, P.R. China 430074;School of Economics and Management, China Universiyt of Geosciences, Wuhan, Hubei, P.R. China 430074
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

Citing 5
Cited 0

Swarm intelligence

Swarm intelligence
Corrective action planning using RBF neural network

Applied Soft Computing
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the efficiency of the orthogonal least squares training method for radial basis function networks

IEEE Transactions on Neural Networks
Optimal adaptive k-means algorithm with dynamic adjustment of learning rate

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The degree of success of many oil and gas drilling, completion, and production activities depends on the accuracy of the models used in the reservoir lateral prediction and description. In this paper, a hybrid MPSO-BP-RBFN model for predicting reservoir from seismic attributes is proposed. The model in which every particle consists of binary and real parts is able to simultaneously search for optimal network topology (the number of hidden nodes) and parameters, as it proceeds. The model has been used to reservoir lateral prediction of a reservoir zone and proved the model's applicability.