Extracting fuzzy rules based on fusion of soft computing in oil exploration management
This paper proposed a self-learning, self-adapting algorithm (ANN-GA-Cascades) for extracting fuzzy rules, which is based oil fusion of soft computing. We could use it to attain the fuzzy rules of oiliness in oil exploration: firstly, supervised learning of training sample is performed by using neural networks, with the inputs being the simplest well-logging attribute Set which is relevant to the oiliness attributes. and the outputs being the corresponding oiliness partition CA (dry layer, water layer, inferiority layer and oil layer). When the neural network attained precision or the maximum iteration steps, the kill output node of neural network will be the corresponding partition of decision character, with the output function being psi(k) = f(x(i),(WG1)(ij), (WG2)(jk)), in which (WG1)(ij) are the connection weights between input layer and hidden layer. (WG2)(jk) are the connection weights between hidden layer and output layer. Then, the genetic algorithm (GA) was used to randomly assemble the input character and psi(k) as the fitness function. In this way, the optimal chromosome will be the fuzzy rule of partition CA. Finally, the empirical study application of this Agorithm oil oil well oilsk81 and oilsk83 of Jianglian oilfield in China has proved to be satisfactory. (C) 2007 Elsevier Ltd. All rights reserved.
fuzzy rules soft computing well-logging genetic algorithm artificial neural networks reservoir