Inference of Transmissivity in Crystalline Rock Using Flow Logs Under Steady‐State Pumping: Impact of Multiscale Heterogeneity

Abstract

The significance of fracture roughness for hydraulic characterization of sparsely fractured (crystalline) rock using flow logs under steady‐state pumping conditions is investigated by numerical simulations in three‐dimensional fracture networks. A new solver for simulating flow in three‐dimensional discrete fracture networks is implemented using a hybrid finite volume and finite element method. The analysis is focused on different scales of heterogeneity in three‐dimensions: the network scale heterogeneity, the fracture‐to‐fracture scale heterogeneity, and the individual fracture scale heterogeneity (fracture roughness). The results show that the individual fracture scale heterogeneity due to fracture roughness is potentially an additional source of uncertainty for hydraulic tests in fractured rocks using flow logs. Specifically, it enhances the variation in measured flowrates and inferred transmissivity in addition to the network scale heterogeneity and the fracture‐to‐fracture scale heterogeneity. However, the individual fracture scale heterogeneity has relatively small impact on the inferred transmissivity distributions compared to the fracture‐to‐fracture scale heterogeneity. Moreover, fracture roughness and fracture‐to‐fracture heterogeneity have limited influence on the median values of the inferred transmissivity such that the median transmissivity is mostly reliable when inferred from flow logs. Comparison between the inferred and underlying input transmissivity distributions shows that interpreting hydraulic tests in crystalline rock using flow logs and the Thiem equation may underestimate the variation range of the underlying transmissivity. The results are helpful for understanding the uncertainty in hydraulic characterization of sparsely fractured rocks using flow logs that are relevant for various geo‐engineering and water resources applications.

A Geo‐Structurally Based Correction Factor for Apparent Dissolution Rates in Fractured Media

“…While previous studies incorporated in‐plane variability of a fracture's permeability into network scale simulations (de Dreuzy et al., 2012; Frampton et al., 2019; Huang et al., 2019; Hyman et al., 2021; Karra et al., 2015; Makedonska et al., 2016; Zou & Cvetkovic, 2020), this is the first to report dynamically evolving in‐plane variability of a fracture's permeability due to geochemical reactions in a 3D DFN.…”

Scale‐Bridging in Three‐Dimensional Fracture Networks: Characterizing the Effects of Variable Fracture Apertures on Network‐Scale Flow Channelization

“…DFN models explicitly represent fractures as 2D planar objects in 3D space and typically model apertures as perfectly smooth parallel plates. Recent advances in DFN models, where fractures and fracture networks are explicitly represented as opposed to continuum models where their properties are upscaled into effective properties (Karimi‐Fard et al., 2006; Neuman, 2005; Oda, 1985; Sweeney et al., 2020), now allow for fracture aperture variability to be incorporated in network‐scale simulations (de Dreuzy et al., 2012; Frampton et al., 2019; Huang et al., 2019; Karra et al., 2015; Makedonska et al., 2016; Zou & Cvetkovic, 2020). Using the dfnWorks modeling suite (Hyman, Karra, et al., 2015), we generate 10 separate micro‐scale 3D DFNs, each within a centimeter cube.…”

“…While aperture variability is known to lead to local flow dispersion and channelization within single fractures (Boutt et al., 2006; Brown, 1987a; Brown et al., 1998; Cardenas et al., 2007; Detwiler et al., 2000; Kang et al., 2016; Renshaw, 1995; Zimmerman et al., 1991; Zou et al., 2015, 2017a, b), it has yet to be definitely established whether and how these micro‐scale flow effects impact macro‐scale flow observables. It is difficult to carry out the requisite experiments of flow through real rock fracture networks at in‐situ conditions within a laboratory to investigate these connections and previous numerical simulations have been limited to using statistical representations of the aperture field within each fracture (de Dreuzy et al., 2012; Frampton et al., 2019; Huang et al., 2019; Karra et al., 2015; Makedonska et al., 2016; Zou & Cvetkovic, 2020). Although some measurements of surface roughness of exhumed and laboratory‐manufactured fracture surfaces possess self‐affine statistical properties (Brown, 1987b; Lee et al., 1990; Renard et al., 2006; Stigsson & Ivars, 2019), it is unclear if these stochastic representations adequately capture the key attributes (including aperture and contact area) that influence flow at the network scale.…”

From Fluid Flow to Coupled Processes in Fractured Rock: Recent Advances and New Frontiers

“…Although computationally expensive, the main advantage of discrete models is that they provide a more faithful representation of gradients between the properties of fractures and the rock matrix (de Dreuzy et al., 2002, 2012; Edery et al., 2016; Frampton & Cvetkovic, 2010; Hardebol et al., 2015; Hyman, 2020; Hyman et al., 2019a; Kang et al., 2019; Maillot et al., 2016; Painter et al., 2002; Selroos et al., 2002; Zou & Cvetkovic, 2020, 2021) across a wide range of length scales (Bogdanov et al., 2007; de Dreuzy et al., 2002, 2012; Frampton & Cvetkovic, 2010; Frampton et al., 2019; Hyman et al., 2019b; Joyce et al., 2014; Makedonska et al., 2016; Sweeney & Hyman, 2020; Wellman et al., 2009) and offer the ability to test hypotheses about the importance of one scale of heterogeneity against another. This aspect makes them better suited to inform field site operators about what data would be the most impactful to constrain predictive models.…”