Abstract

Understanding the anisotropic hydraulic and mechanical properties of fractured rock masses is of great importance for a safe and optimal utilisation of the subsurface. Two sandstone quarries are utilized to obtain fracture network characteristics by Terrestrial Laser Scanning (TLS) producing 3d point cloud data. Semiautomatic analysis of the point clouds provides the probability density functions for each of the fracture parameters used as stochastic input for a Discrete Fracture Network (DFN) model. Rock mechanical laboratory tests are carried out to determine the mechanical properties of the intact rock and fractures. These parameters are then combined in the DFN model to calculate spatially variable tensors for permeability, Young’s modulus and Poisson’s ratio. Thereby, the spatial resolution of the tensor description is adapted to the grid size which can be used in further hydromechanical models. The approach allows to populate these models with more realistic parameters which incorporate also the effect of fractures on the rock mass behaviour. Obtained results are subsequently compared with conventional engineering rock mass classifications. The applied workflow allows for upscaling of rock properties determined in the laboratory to the anisotropic rock mass properties required for further hydromechanical modelling on larger scales, e.g., the reservoir scale.

Introduction

Regardless of their origin, fracture networks significantly contribute to the hydromechanical properties of rock masses, implying that a detailed description of the fracture network is essential for characterization of fractured rock masses [1]. A potential opportunity to determine the hydraulic properties of rock masses are in situ measurements such as water-pressure and slug-tests [2]. Mechanical properties can be obtained by in situ measurements like plate jacking and plate load tests [3] or indirectly by empirical relationships based on empirical classification systems such as Rock Quality Designation (RQD) [4], Rock Mass Rating (RMR) [5], Geological Strength Index (GSI) [6] or the Q-System [7]. A further new approach is the development of a deterministic-stochastic Discrete Fracture Network (DFN) model of a fractured rock mass [8] which allows the calculation of anisotropic and spatially variable hydromechanical rock properties [9], i.e. tensors for Permeability, Young’s modulus and Poisson’s ratio. The input data to set up a DFN model can be gathered from surface outcrops and excavations as well as core and image log data. The present study provides a working example of hydromechanical characterization of rock masses using a DFN modeling approach to derive hydraulic and mechanical parameters. Obtained mechanical properties are further validated by a comparison to conventional rock mass classification systems. The goal is to present a framework for realistically describing and predicting the hydraulic and mechanical behavior of fractured rock masses incorporating the spatial variability of the predominant fracture network.

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