From outcrop observations to dynamic simulations: an efficient workflow for generating ensembles of geologically plausible fracture networks and assessing their impact on flow and transport

Fractures are ubiquitious in geological formations and can often impact subsurface applications such as geothermal energy, groundwater management, or CO 2 storage. Quantifying the relationship between the uncertainties inherent to fracture networks and the corresponding flow behaviour for these applications remains an open challenge. Simulation studies that are based on outcrop analogues of fracture networks have yielded many new insights about heat and mass transfer in fractured geological formations but are restricted to a limited number of fracture network realisations, simplified assumptions about fracture network properties, or deterministic models, making it difficult to analyse a wide range of uncertainties. This study introduces a flexible workflow that generates ensembles of geologically plausible fracture networks that can be based on statistical data from outcrop analogues. The fracture networks are generated using a computationally efficient approach that combines mechanical and statistical methods. The ensembles are then seamlessly linked to multi-purpose flow and transport simulations where the fractures are represented explicitly in a porous and permeable rock matrix. This approach can enable new uncertainty quantification methods, supported by machine-learning-based emulators, to analyse how fracture network properties such as fracture intensity, fracture aperture, or fracture orientation, influence heat and mass transfer in fractured geological formations. The workflow is illustrated using two classical example applications pertinent to fracture network modelling, one based on outcrop data to assess thermal behaviour in geothermal systems, and one synthetic study to analyse the transition from matrix-dominated to fracture-dominated flow, and released as open-source code.