TY - GEN
T1 - Multilayer perceptron learning with particle swarm optimization for well log data inversion
AU - Huang, Kou-Yuan
AU - Shen, Liang Chi
AU - Chen, Kai Ju
AU - Huang, Ming Che
PY - 2012
Y1 - 2012
N2 - Well log data inversion is important for the inversion of true formation. There exists a nonlinear mapping between the measured apparent conductivity (C a) and the true formation conductivity (C t). We adopt the multilayer perceptron (MLP) to approximate the nonlinear input-output mapping and propose the use of particle swarm optimization with mutation (MPSO) algorithm to adjust the weights in MLP. In the supervised training step, the input of the network is the measured C a and the desired output is the C t. MLP with optimal size 10-9-10 is chosen as the model. We have experiment in simulation and real data application. In simulation, there are 31 sets of simulated well log data, where 25 sets are used for training, and 6 sets are used for testing. After training the MLP network, input Ca, then C t' can be inverted in testing process. Also we apply it to the inversion of real field well log data. The result is acceptable. It shows that the proposed MPSO algorithm in MLP weight adjustments can work on the well log data inversion.
AB - Well log data inversion is important for the inversion of true formation. There exists a nonlinear mapping between the measured apparent conductivity (C a) and the true formation conductivity (C t). We adopt the multilayer perceptron (MLP) to approximate the nonlinear input-output mapping and propose the use of particle swarm optimization with mutation (MPSO) algorithm to adjust the weights in MLP. In the supervised training step, the input of the network is the measured C a and the desired output is the C t. MLP with optimal size 10-9-10 is chosen as the model. We have experiment in simulation and real data application. In simulation, there are 31 sets of simulated well log data, where 25 sets are used for training, and 6 sets are used for testing. After training the MLP network, input Ca, then C t' can be inverted in testing process. Also we apply it to the inversion of real field well log data. The result is acceptable. It shows that the proposed MPSO algorithm in MLP weight adjustments can work on the well log data inversion.
KW - apparent conductivity (C )
KW - multilayer perceptron (MLP)
KW - particle swarm optimization with mutation (MPSO)
KW - true formation conductivity (C )
UR - http://www.scopus.com/inward/record.url?scp=84865069423&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2012.6252707
DO - 10.1109/IJCNN.2012.6252707
M3 - Conference contribution
AN - SCOPUS:84865069423
SN - 9781467314909
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012
T2 - 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Y2 - 10 June 2012 through 15 June 2012
ER -