Investigation of Hydraulic and Natural Fracture Interaction: Numerical Modeling or Artificial Intelligence?

Abstract

Hydraulic fracturing of naturally fractured reservoirs is a critical issue for petroleum indus‐ try, 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 hy‐ draulic 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 frac‐ ture 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(cross‐ ing/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 ap‐ proach 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 re‐ sults have been compared. It is clearly observed that both of them yield promising results © 2013 Keshavarzi and Jahanbakhshi; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. while numerical modeling yields more detailed results which can be used for further inves‐ tigations but it is computationally more expensive and time-consuming than artificial neural network approach. On the other hand, since artificial neural network approach is mainly da‐ ta-driven if just the input data is available (even while fracturing) the hydraulic fracture path (crossing/turning into natural fracture) can be predicted real-time and at the same time that fracturing is happening.