TY - GEN

T1 - Higher order neural networks for well log data inversion

AU - Huang, Kou-Yuan

AU - Shen, Liang Chi

AU - Chen, Chun Yu

PY - 2008/11/24

Y1 - 2008/11/24

N2 - Multilayer perceptron is adopted for well log data inversion. The input of the neural network is the apparent resistivity (Ra) of the well log and the desired output is the true formation resistivity (Rt). The higher order of the input features and the original features are the network input for training. Gradient descent method is used in the back propagation learning rule. From our experimental results, we find the expanding input features can get fast convergence in training and decrease the mean absolute error between the desired output and the actual output. The multilayer perceptron network with 10 input features, the expanding input features to the third order, 8 hidden nodes, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. And then the system is applied on the real well log data.

AB - Multilayer perceptron is adopted for well log data inversion. The input of the neural network is the apparent resistivity (Ra) of the well log and the desired output is the true formation resistivity (Rt). The higher order of the input features and the original features are the network input for training. Gradient descent method is used in the back propagation learning rule. From our experimental results, we find the expanding input features can get fast convergence in training and decrease the mean absolute error between the desired output and the actual output. The multilayer perceptron network with 10 input features, the expanding input features to the third order, 8 hidden nodes, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. And then the system is applied on the real well log data.

UR - http://www.scopus.com/inward/record.url?scp=56349093473&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2008.4634154

DO - 10.1109/IJCNN.2008.4634154

M3 - Conference contribution

AN - SCOPUS:56349093473

SN - 9781424418213

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 2545

EP - 2550

BT - 2008 International Joint Conference on Neural Networks, IJCNN 2008

Y2 - 1 June 2008 through 8 June 2008

ER -