The challenge in oil and gas exploration has now shifted due to increasingly difficult to get back up economic value in a conventional reservoir. Explorationist are developing various drilling technology, optimizing conventional reserves and unconventional reserve in reservoirs. One of the unconventional reservoir that has been developed is the basement reservoir. This rock type has no primary porosity and the permeability of the rocks of this type are generally influenced by the naturally fracture networks. The purpose of this study is to map the fracture intensity distribution in the basement reservoir using Continuous Fracture Modeling (CFM) method. CFM method applies the basic concepts of neural network in finding a relationship between well data with seismic data in order to build a model of fracture intensity. The Formation Micro Imager (FMI) interpretation data is used to identify the presence of fracture along the well as dip angle and dip azimuth. This indicator will be laterally populated in 3D grid model. Several seismic attribute which are generated from seismic data is used as a guidance to populate fracture intensity in the model. The results from the model were validated with Drill Stem Test (DST) data. Zones of high fracture intensity on the model correlates positively with the presence of fluid in accordance with DST data.