Multiple-point statistical prediction on fracture networks at Yucca Mountain

Abstract

In many underground nuclear waste repository systems, such as Yucca Mountain project, water flow rate and amount of water seepage into the waste emplacement drifts are mainly determined by hydrological properties of fracture network in the surrounding rock mass. Natural fracture network system is not easy to describe, especially with respect to its connectivity which is critically important for simulating the water flow field. In this paper, we introduced a new method for fracture network description and prediction, termed multi-point-statistics (MPS). The process of Multi-point Statistical method is to record multiple-point statistics concerning the connectivity patterns of fracture network from a known fracture map, and to reproduce multiple-scale training fracture patterns in a stochastic manner, implicitly and directly. It is applied to fracture data to study flow field behavior at Yucca Mountain waste repository system. First, MPS method is used to create fracture network with original fracture training image from Yucca Mountain dataset. After we adopt a harmonic and arithmetic average method to upscale the permeability to a coarse grid, THM simulation is carried out to study near-field water flow in surrounding rock of waste emplacement drifts. Our study shows that connectivity or pattern of fracture network can be grasped and reconstructed by Multi-Point-Statistical method. In theory, it will lead to better prediction of fracture system characteristics and flow behavior. Meanwhile, we can obtain variance from flow field, which gives us a way to quantify uncertainty of models even in complicated coupled THM simulation. It indicates that Multi-Point Statistics is a potential method to characterize and reconstruct natural fracture network in a fractured rock mass with advantages of quantifying connectivity of fracture system and its simulation uncertainty simultaneously.