Fracture prediction and modelling is one area where there is scope for significant technical advance. At present fractures are modelled either by modifying the bulk rock properties to take account of the fracture porosity and permeability, or using stochastically generated Discrete Fracture Network (DFN) models. Both methods tend to give a poor history match because the distribution, orientation, length and connectivity of fractures in the subsurface is not well constrained. We propose to improve the prediction of fluid flow in a fractured reservoir, by developing an algorithm to accurately model the key parameters of a fracture population (fracture density, fracture size distribution, and connectivity) based on the geomechanics of fracture nucleation and propagation, and using this to generate a mechanically-based DFN that more accurately represents the subsurface fracture geometry. We will use a preliminary version of this model to demonstrate some of the key geomechanical controls on fracture geometry, such as fracture propagation rate and strain anisotropy, and illustrate the different types of fracture network that can develop under different conditions.


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