An automated approach for conditioning discrete fracture network modelling to in situ measurements

Creation of a geological fracture network model conditioned to in situ geometric measurements is of great importance to geoprofessionals, as fractures dominate pathways for fluid flow, a major concern for many engineering applications. This paper introduces and applies the Stochastic Nelder Mead simplex method to automatically calibrate stochastic parameters of geometric characterisations of a discrete fracture network model. This method can overcome the non-convergence of a previous exploratory approach on the classic Nelder Mead method by others, and is an effective substitution to the manual trial-and-error method and is complementary to existing conditional simulation approaches. The procedure to integrate the Stochastic Nelder Mead with a discrete fracture network is presented in detail, and a case study was conducted. Results show that the improved model can better handle the stochastic nature of the underlying system and effectively simulates the observed number and mean trace length of these fractures, although the model results underestimate its standard deviation. Simulated distributions of trace lengths and spacings are within acceptable ranges except for some small offsets, which can be adjusted during model runs.