Predicting Reservoir Water Saturation Based on Fuzzy Neural Networks
Xiaoyan Huang · Acta Simulata Systematica Sinica · 2003
A kind of improved Compensation Fuzzy Neural Networks can impersonate the advantages of fuzzy system with ability to be prone to express knowledge and the advantages of neural networks with fairly strong self-adaptive ability. Then, a mechanism that can dynamically adjust the learning step is presented. So the sway phenomenon can be minimized and the learning step can be quickly speeded. Finally, the system is applied to predicting reservoir water saturation in logging interpretation of oil field. The result of experiment is satisfying. Compared to conventional neural networks, the convergence speed and the error precision are improved a lot. Practice has proved that the method is worth further extending.