Fracture identification is one of the key issues and difficulties in exploration and development of fractured reservoirs.Based on response characteristics of conventional logging data to fractures

we proposed an identification method called PSO-SVM that integrates the Particle Swarm Optimization(PSO) with the Support Vector Machine(SVM).By taking the reservoirs in the Yan'an and Yanchang formations in the western Mahuang-shan block for examples

we identified conventional logging parameters that can respond well to fractures through a crossplot analysis

and performed an overall optimal selection of model parameters by using the PSO.Based on these works

we built a model for identification of fractures in the study area.The model was applied to single well in the study area for identification of fractures and a synthetic column map was produced by comparing the model outputs with core photos and logging curves.The application of the method shows that the modeling result matches well with the geological reality and can truthfully reflect the growth of fractures.