Application of dynamic fuzzy neural networks based on EBF to multifactorial flooding index prediction

Multifactorial flooding index is an important parameter to flooding reservoir analysis. It is very necessary to consider the weight of each flooding strength indicator in calculation of multifactorial flooding index by using of logging data. Therefore, a fuzzy neural network prediction system of multifactorial flooding index based on Ellipse Basis Function was established on the basis of the analysis of a variety of static and dynamic data of Gasikule oil field N1-N2¹ reservior. This prediction system can create or delete fuzzy rules by analyzing samples and take the dynamic weight values of the input variables into consideration. The information contained in the log data is enormous. By using this prediction system with self-learning mechanism, the extraction and utilization of information is more effective. Practical application shows that the accuracy of identification is high. Especially for complex reservoirs, the application of this Fuzzy Neural Networks to reservoir characteristic parameters prediction improves the precision of prediction results and reduces the dependency on prior informations.