Browsing by Author "Huang, Ming-Che"

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Multilayer Perceptron Learning with Particle Swarm Optimization for Well Log Data Inversion
(2012)
Huang, Kou-Yuan
;
Shen, Liang-Chi
;
Chen, Kai-Ju
;
Huang, Ming-Che
;
資訊工程學系
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.
MULTILAYER PERCEPTRON WITH GENETIC ALGORITHM FOR WELL LOG DATA INVERSION
(2013-01-01)
Huang, Kou-Yuan
;
Shen, Liang-Chi
;
Chen, Kai-Ju
;
Huang, Ming-Che
;
資訊工程學系
Two-layer multilayer perceptron (MLP) learning by genetic algorithm (GA) is used to approximate the nonlinear mapping between the input and the desired output. The GA is a global optimization method that can avoid the local minimum during the training in MLP and is implemented in binary and real number calculations. We have experiments on 31 simulated well log data and real data application. In the supervised training step, the input of the network is the apparent conductivity (Ca) and the desired output is the true formation conductivity (Ct). The best size of two-layer MLP is chosen as 10-9-10 by theorem and experiments. And we get the best parameters of binary GA and real number GA by sequential method. After getting the best MLP network in training, the corresponding true formation conductivity can be inverted for each input Ca pattern in testing process. From comparison of errors in experiments of simulated data, the real number GA has less error than that of binary GA. That is because the bit string in binary GA limits the range of weighting coefficient and has higher error. We also apply the best 10-9-10 MLP model to the inversion of real field well log data. It shows that this method can work on well log data inversion and is feasible.
MULTILAYER PERCEPTRON WITH PARTICLE SWARM OPTIMIZATION FOR WELL LOG DATA INVERSION
(2012)
Huang, Kou-Yuan
;
Chen, Kai-Ju
;
Huang, Ming-Che
;
Shen, Liang-Chi
;
資訊工程學系
A nonlinear mapping exists 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 Ca and the desired output is the C-t. MLP with optimal size 10-9-10 is chosen as the model. We have experiments 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 C-a, then C-t' can be inverted in testing process. Compared with radial basis function (RBF) networks and particle swarm optimization (PSO) method, the error of MPSO is the smallest. 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 perform the well log data inversion.