- Investigation of Hydraulic and Natural Fracture Interaction: Numerical Modeling or Artificial Intelligence?
- R. Keshavarzi ; Re Jahanbakhshi
- Book Title / Journal: Effective and Sustainable Hydraulic Fracturing
- Year: 2013 , Series: Chapter 53
- Other Geotechnical
- Keywords: Numerical Modeling ; Artificial Intelligence ; Hydraulic and Natural Fracture Interaction
- 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 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 (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. It is clearly observed that both of them yield promising results while numerical modeling yields more detailed results which can be used for further investigations 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 data-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.