The flow of water, hydrocarbons and other fluids through bedrock is often strongly influenced by the complexity and heterogeneity of natural fracture systems. In the oil and gas industry, fracture network modelling is used extensively to predict the storage capacity of fractured reservoirs, to improve understanding of reservoir geomechanics, and to optimise field development plans and production strategies.
Key parameters typically required as inputs for fracture modelling include the mean orientation and angular dispersion of each fracture set, the distribution of fracture sizes (lengths, heights, apertures), spatial distribution (spacing, clustering), connectivity, and factors causing spatial variability in fracture density (e.g. mechanical stratigraphy, position of fold structures, proximity to faults). Since some of these aspects of the fracture system often display marked scaling behaviour, the validity of the modelling results can be strongly influenced by the quality and suitability of the input data.
A key lesson from the oil and gas industry is that data from borehole (well log imagery, core) are too sparse, and the resolution of 3D seismic is too low to characterise adequately the fracture network, and wherever possible should be supplemented by studies of suitable outcrop analogues.
Modern airborne and terrestrial geospatial methods, including lidar (laser-scanning), digital photogrammetry, unmanned aerial vehicles (UAVs, ‘drones’), and satellite imagery are able to capture large areas of outcrop very rapidly, in high detail, with high spatial precision. In combination with traditional field methods this provides robust, quantitative fracture characteristics that are ideal as input for fracture network modelling.
We draw upon extensive commercial application of fracture network characterisation in the oil and gas industry to present lessons and pitfalls that may be applied to geothermal systems.