Baturaja formation has become one prospective reservoir in field A, which has potential number gas inplace with recovery factor up to 29%. However it has a big challenge for developing because of its structural complexity. From special core analysis data and Formation Micro Imaging (FMI) data analysis, it was shown that this reservoir is dominated by fracture indicated by cementation exponent (m) below 2. This paper aims to perform fracture modeling based on the simultaneous integration of geophysical, geological and engineering data to improve reservoir characterization.These integrated data so called fracture drivers, which contain seismic attributes, curvature, porosity, facies, acoustic impedance, elastic impedance and production data. This fracture driver will be ranked using fuzzy-logic tool. After having rank and eliminating the less influential driver, the effect of each fracture driver on the fracturing was analysed. The ranked drivers were used to establish the complex, non-linear relationship relating the fracture intensity to these drivers. This process is performed by using neural-network algorithm.Our experiments show that this approach succeed in distributing the fracture frequency, which is associated with permeability. Finally, the predicted permeability can be useful for reservoir simulation and helps us in developing carbonate reservoir.