CONDITIONING DISCRETE FRACTURE NETWORK MODELS OF GROUNDWATER FLOW

Many geological formations consist of crystalline rock that have very low matrix permeability but allow flow through an interconnected net- work of fractures. Understanding the flow of groundwater through such rocks is important in considering disposal of radioactive waste in underground reposi- tories. A specific area of interest is the conditioning of fracture transmissivities on measured values of pressure in these formations. While there are exist- ing methods to condition transmissivity fields on transmissivity, pressure and flow measurements for a continuous porous medium, considerably less work has been devoted to conditioning discrete fracture networks. This article presents two new methods for conditioning fracture transmissivities on measured pres- sures in a discrete fracture network. The first approach adopts a linear ap- proximation when fracture transmissivities are mildly heterogeneous, while the minimisation of a suitable objective function is undertaken when fracture trans- missivities are highly heterogeneous. The second conditioning algorithm is a Bayesian method that finds a maximum a posteriori (MAP) estimator which maximises the posterior distribution defined by Bayes' theorem using informa- tion from the prior distribution of fracture transmissivities and observations in the form of measured pressures. The conditioning methods are tested on two separate, large scale test cases that model a potential site for radioactive waste disposal. Results from these test cases are shown and comparisons between the two conditioning methods are made.