Hydraulic fracturing of naturally fractured reservoirs is a critical issue for petroleum industry, as fractures can have complex growth patterns when propagating in systems of natural fractures. Hydraulic and natural fracture interaction may lead to significant diversion of hydraulic fracture paths due to intersection with natural fractures which causes difficulties in proppant transport and eventually job failure. In this study, a comparison has been made between numerical modeling and artificial intelligence to investigate hydraulic and natural frcature interaction. First of all an eXtended Finite Element Method (XFEM) model has been developed to account for hydraulic fracture propagation and interaction with natural fracture in naturally fractured reservoirs including fractures intersection criteria into the model. It is assumed that fractures are propagating in an elastic medium under plane strain and quasi-static conditions. Comparison of the numerical and experimental studies results has shown good agreement. Secondly, a feed-forward with back-propagation artificial neural network approach has been developed to predict hydraulic fracture path (crossing/turning into natural fracture) due to interaction with natural fracture based on experimental studies. Effective parameters in hydraulic and natural fracture interaction such as in situ horizontal differential stress, angle of approach, interfacial coefficient of friction, young's modulus of the rock and flow rate of fracturing fluid are the inputs and hydraulic fracturing path (crossing/turning into natural fracture) is the output of the developed artificial neural network. The results have shown high potentiality of the developed artificial neural network approach to predict hydraulic fracturing path due to interaction with natural fracture. Finally, both of the approaches have been examined by a set of experimental study data and the results have been compared.

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