Stochastic fracture generation accounting for the stratification orientation in a folded environment based on an implicit geological model

Abstract

a b s t r a c t Keywords:Geological modeling Discrete fracture network (DFN) Stochastic modeling Structural geology This paper presents a new approach in generating stochastic discrete fracture networks. The particularity of the approach is that it allows us to simulate the theoretical families of fractures that are expected in a folded environment. The approach produces fractures that are consistent with the local stratigraphic orientation. The fractures are modeled as simple rectangular planar objects. When they are modeled, they are rotated according to the local stratigraphic orientation. As the stratigraphy is modeled using an implicit approach, we use the gradient of this geological potential field to retrieve the information about the geological orientation. The fracture number and size are following user-defined probability density functions.

Can one identify karst conduit networks geometry and properties from hydraulic and tracer test data?

“…In this paper, the Stochastic Karst Simulator (SKS) pseudogenetic method developed in Borghi et al [8] is used to model the 3D geometry of conduit network. The method includes the following steps: (1) a 3D geological model is built; (2) a stochastic fracture model is used to add heterogeneity into the 3D geological model [7] ; (3) the conduits of the karstic network are generated using the pseudo-genetic approach, which uses a Fast Marching Algorithm [FMA, [56] ] to compute minimum effort paths between karstic inlets (dolines and sinkholes) and springs. This minimum effort path computation is based on the assumption that the water (and consequently the conduits generated by dissolution) will preferentially flow inside the more conductive discontinuities like fractures and bedding planes, termed as inception horizons [19] .…”

Quartz c-axis orientation patterns in fracture cement as a measure of fracture opening rate and a validation tool for fracture pattern models

“…Geomechanical models predict fracture attributes such as length, aperture, and spacing, as well as rates of fracture growth (Olson, 1993(Olson, , 2004Renshaw and Pollard, 1994;Maerten et al, 2006;Borghi et al, 2015). Some of these models predict fracture pattern development over several millions of years (e.g., Olson et al, 2009), implying cumulative opening rates that are slow.…”

“…Fracture networks are challenging to measure in subsurface rocks, in part because they may comprise fractures of differing age, size, and degree of mineral fill. Differences in network patterns can have profound effects on a wide range of engineering operations (Aguilera, 1980;Philip et al, 2005;Weng et al, 2011), so considerable effort has been made to build process-based models to predict fracture network growth and resulting patterns (Olson, 1993(Olson, , 2004Renshaw and Pollard, 1994;Maerten et al, 2006;Borghi et al, 2015). Although such models predict rates of fracture growth, a significant challenge for validating such models is the extremely limited capacity of geologic methods to independently measure age, rates, or duration of fracture growth.…”

A Double Scale Methodology to Investigate Flow in Karst Fractured Media via Numerical Analysis: The Cassino Plain Case Study (Central Apennine, Italy)

“…Usually, followed approaches take into account mean hydraulic parameters, valid at regional scales. Modern methods investigated the fractured and karst media by a discrete approach: Atkinson [9] described the groundwater flow in a Carboniferous karst aquifer combining turbulent conduit flow and Darcian flow in fine fractures; Andersson and Dverstorp [10] predicted the groundwater flows through a network of discrete fractures statistically generated, via a Monte-Carlo simulation; Berkowitz [11] analysed open questions of flow and transport in fractured geological media; Maramathas and Boudouvis [12] described a deterministic mathematical method by the characterization of the fractal dimension of the network; Pardo-Igúzquiza et al [13] adopted a method to generate conduit geometries via a statistical approach, based on speleological surveys; Borghi et al [14] proposed a stochastic method to generate a karst network; Bauer et al [15] studied the karstification process of a single conduit via a hybrid continuum-discrete approach; Liedl et al [16] used a continuum pipe-model describing the evolution of a karst media; Langevin [17] Maggiore Hy.…”