In highly heterogeneous reservoirs classical characterization methods often fail to detect the location and orientation of the fractures. Recent applications of Artificial Intelligence to the area of reservoir characterization have made this challenge a possible practice. Such practices consist of seeking the complex relationship between the fracture index and some geological and geomechanical drivers (Facies, porosity, permeability, bed thickness, proximity to faults, slopes and curvatures of the structure) in order to obtain a fracture intensity map using Fuzzy Logic and Neural Network.

This paper shows the successful application of Artificial Intelligence tools such as Artificial Neural Network and Fuzzy Logic to characterize naturally fractured reservoirs. A 2D fracture intensity map and fracture network map in a large block from Hassi Messaoud field have been developed using Artificial Neural Network and Fuzzy Logic.

This was achieved by first building the geological model of the permeability, porosity and shale volume using stochastic conditional simulation. Then by applying some geomechanical concepts first and second structure directional derivatives, distance to the nearest fault, and bed thickness were calculated throughout the entire area of interest. Two methods were then used to select the appropriate fracture intensity index. In first method well performance was used as a fracture index. In the second method, which consists of a new proposed approach, a Fuzzy Inference System (FIS) was built. With such system static to dynamic data was coupled to reduce the uncertainty and resulted in a more reliable Fracture Index. The different geological and geomechanical drivers were ranked with the corresponding fracture index for both methods using a Fuzzy Ranking algorithm. Only much important data were selected to be mapped with the appropriate fracture index using a feed forward Back Propagation Neural Network (BPNN). The neural network was then used to obtain a fracture intensity maps throughout the entire area of interest. A mathematical model based on "the weighting method" was then applied to obtain fracture network maps, which resulted in a deep insight about the major fracture trends.

The obtained maps were compared in the end and the results show that the proposed approach is a feasible methodology to map the fracture network.

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