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Knowledge of permeability is critical to developing an effective reservoir description. Permeability data can be obtained directly from well tests and cores or indirectly from logs. Normally, using well log data to derive estimates of permeability is the lowest cost method. This article proposes a structured multiple neural network that is constructed according to the structure of IF-THEN fuzzy inference rules and the fuzzy c-means clustering method, which combines the advantages of neural network and fuzzy systems. This multiple neural network shows that the predictive performance of the proposed model is superior to that of both the fuzzy logic and neural network. [ABSTRACT FROM PUBLISHER]

A key parameter for reservoir characterization is permeability distribution. Most well log data and core permeability values are corrupted by noise (such as uncertain depth-matching, core testing conditions, and thin beddings). In this work, the authors first used wavelet as a new powerful tool for de-noising data points and then they investigated how the integration of back propagation with group based symbiotic evolution improves the reliability and prediction capability of neuro-fuzzy systems for predicting permeability of real reservoir data. [ABSTRACT FROM AUTHOR]

In this work we investigate how the integration of back-propagation (BP) with particle swarm optimization (PSO) improves the reliability and prediction capability of PSO. This strategy is applied to predict permeability in Mansuri Bangestan reservoir located in Ahwaz, Iran, utilizing available geophysical well log data. Our methodology utilizes a hybrid PSO–BP. The particle swarm optimization algorithm was shown to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergence speed around global optimum and with greater accuracy. The proposed algorithm combines the local search ability of the gradient-based BP strategy with the global search ability of particle swarm optimization. PSO is used to decide the initial weights of the gradient decent methods so that all of the initial weights can be searched intelligently. The experimental results show that the proposed hybrid PSO–BP algorithm is better than the PSO algorithm in convergence speed and accuracy. [ABSTRACT FROM PUBLISHER]

In this work we investigate how the integration of back-propagation with a genetic algorithm (GA) improves the learning and prediction capability of the genetic algorithm. This strategy is applied to predict permeability of Mansuri Bangestan reservoir located in Ahwaz, Iran, utilizing available geophysical well log data. Our methodology utilizes a hybrid genetic algorithm–back propagation strategy (GA-BP). The proposed algorithm combines the local searching ability of the gradient-based back-propagation (BP) strategy with the global searching ability of genetic algorithms. Genetic algorithms are used to decide the initial weights of the gradient descent methods so that all of the initial weights can be searched intelligently. The genetic operators and parameters are carefully designed and set, avoiding premature convergence and permutation problems. For evaluation purposes, the performance and generalization capabilities of the GA-BP are compared with those of neural networks trained with GA (GA-ANN). The results demonstrate that a carefully designed hybrid GA-BP outperforms the genetic algorithm–based neural network in convergence speed and accuracy. [ABSTRACT FROM AUTHOR]