Embedded Discrete Fracture Modeling And Application In Reservoir Simulation
by Kamy Sepehrnoori 2020-11-23 19:05:07
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The development of naturally fractured reservoirs, especially shale gas and tight oil reservoirs, exploded in recent years due to advanced drilling and fracturing techniques. However, complex fracture geometries such as irregular fracture networks an... Read more

The development of naturally fractured reservoirs, especially shale gas and tight oil reservoirs, exploded in recent years due to advanced drilling and fracturing techniques. However, complex fracture geometries such as irregular fracture networks and non-planar fractures are often generated, especially in the presence of natural fractures. Accurate modelling of production from reservoirs with such geometries is challenging. Therefore,Embedded Discrete Fracture Modeling and Application in Reservoir Simulationdemonstrates how production from reservoirs with complex fracture geometries can be modelled efficiently and effectively.

This volume presents a conventional numerical model to handle simple and complex fractures using local grid refinement (LGR) and unstructured gridding. Moreover, it introduces an Embedded Discrete Fracture Model (EDFM) to efficiently deal with complex fractures by dividing the fractures into segments using matrix cell boundaries and creating non-neighboring connections (NNCs). A basic EDFM approach using Cartesian grids and advanced EDFM approach using Corner point and unstructured grids will be covered.

Embedded Discrete Fracture Modeling and Application in Reservoir Simulationis an essential reference for anyone interested in performing reservoir simulation of conventional and unconventional fractured reservoirs.



  • Highlights the current state-of-the-art in reservoir simulation of unconventional reservoirs
  • Offers understanding of the impacts of key reservoir properties and complex fractures on well performance
  • Provides case studies to show how to use the EDFM method for different needs
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  • 9 X 6 X 0 in
  • 304
  • Elsevier Science
  • August 31, 2020
  • English
  • 9780128218723
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