Multilayer perceptron with genetic algorithm for well log data inversion

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.