Fracture detection is a key step in wellbore stability and fractured reservoir fluid flow simulation. While different methods have been proposed for fractured zones detection, each of them is associated with certain shortcomings that prevent their full use in different related engineering applications. In this paper, a novel combined method is proposed for fractured zone detection, using processing of petrophysical logs with wavelet, classification and data fusion techniques. Image and petrophysical logs from Asmari reservoir in eight wells of an oilfield in southwestern Iran were used to investigate the accuracy and applicability of the proposed method. Initially, an energy matching strategy was utilized to select the optimum mother wavelets for de-noising and decomposition of petrophysical logs. Parzen and Bayesian classifiers were applied to raw, de-noised and various frequency bands of logs after decomposition in order to detect fractured zones. Results show that the low-frequency bands (approximation 2, a₂) of de-noised logs are the best data for fractured zones detection. These classifiers considered one well as test well and the other seven wells as train wells. Majority voting, optimistic OWA (ordered weighted averaging) and pessimistic OWA methods were used to fuse the results obtained from seven train wells. Results confirmed that Parzen and optimistic OWA are the best combined methods to detect fractured zones. The generalization of method is confirmed with an average accuracy of about 72%.