عنوان مقاله [English]
Multiple networks systems have been proposed for the purpose of decreasing the error and increasing the accuracy of the results of artificial neural network (ANN) method. In these systems, the results of several single ANN’s, which are trained solely and separately, are combined using a suitable method. In this work, the effective porosity in one of hydrocarbon reservoirs of giant Southern Pars field is estimated using multiple networks systems. Single ANN’s trained using early stopping back propagation (BP) method are used as the components of multiple networks systems. Well logging data acquired from 4 wells in the field at the depth interval corresponding to Kangan formation are used. Acoustic, density, gamma ray, and neutron porosity well log data are considered as the inputs of the networks and the effective porosity data are assigned as the output of the networks. The ensemble combination of networks, which have a parallel structure, are applied for making multiple networks systems. The results show that suitable ensemble combinations improve the results of the ANN’s trained using early stopping BP method. The best obtained ensemble combination is a three-network combination compared to the best obtained single ANN, which reduces the mean of squares of errors (MSE) of porosity prediction in the training and test steps by 14.7% and 12.5% respectively.