Multilayer perceptron with particle swarm optimization for well log data inversion

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 C_a 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.