The Oilfield M in the Middle East is a dominated by dual-medium carbonate reservoir with well-developed fractures and strong heterogeneity. However，the conventional single method cannot finely characterize the distribution of fractures. The multi-information fusion modeling technology for fractures based on the neural network firstly depends on the neural network to predict the fracture density in a single well without imaging logging data. Secondly，the nonlinear fusion of multiple pre-stack seismic attributes including P-wave azimuthal anisotropy and seismic discontinuity detection is performed on the basis of the neural network to predict the development probability of 3D fracture density. The fracture density of a single well is taken as hard data，and the fracture density model is constructed within the dual constraints of strict variogram analysis and fracture density probability volume. Finally，the discrete fracture network model is construced with the geostatistical modeling method，which is coarsened to be equivalent to the fracture attribute model. The model fitting rate is applied to the decision-making optimization for Oilfield M development.It is preferable to use horizontal wells or highly deviated wells for development in the areas with relatively developed fractures，and the average daily oil production per well reaches thousands of barrels. The fracture development revealed by the new well is consistent with the results of pre-drilling prediction，and the output of a single production well is significantly higher than that of a previous development well.