Calibrating the EGS Flow Stimulation Process for Basement Rock

Leary, Peter; Malin, Péter; Saarno, Tero; Kukkonen, Ilmo

doi:10.20944/preprints201709.0079.v1

Cited by 2 publications

(7 citation statements)

References 14 publications

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“…In particular, Equation 5provides an easy physical understanding of how κ ij and ϕ ij are linked via the interconnectivity of networks of fracture, allowing fluid propagation across scales from centimeters to kilometers (Leary & Malin, 2021). Fracture networks exhibit log-normal distributions (McKean et al, 2019) corresponding to the log-normal pattern observed in the permeability fields (Leary & Malin, 2021;Leary et al, 2012). To capture all these observed features in our model, we generate log-normally distributed permeability fields with different β by employing a non-Gaussian transformation method (Khajehdehi, Karimi, & Davidsen, 2022;Turcotte, 1997;Voss, 1988), see Text S1 in Supporting Information S1 for more details.…”

Section: Model Parameterizationmentioning

confidence: 99%

“…As observed in well-log data, porosity and permeability follow Gaussian and log-normal distributions, respectively. Porosity in related geological settings of fluid-induced seismicity is empirically in the range of 0.1 < ϕ ij < 0.35 (Leary et al, 2012(Leary et al, , 2017, whereas permeability values are much more variable, spanning 3-4 orders of magnitude (Townend & Zoback, 2000;Zhang et al, 2013;Zoback, 2010). Additionally, porosity and log(permeability) commonly display power-law behavior in their power spectral density S(k) ∝ 1/k β with β ∈ (0, 2) for 1/Km < k < 1/cm, where β quantifies the degree of spatial correlations present in the porosity and permeability of the medium (Khajehdehi, Karimi, & Davidsen, 2022;Leary & Malin, 2021;Malin et al, 2020).…”

Section: Model Parameterizationmentioning

confidence: 99%

“…Most well-log correlations result from fluctuations in the spatial distribution of fractures at the grain level (Leary & Malin, 2021), which affect porosity and permeability observations, and contribute to the 𝐴𝐴 1 𝑘𝑘 𝛽𝛽 spectrum observed in a well-log (Bean, 1996;Leary & Malin, 2021). According to empirical data obtained from well-core samples, a robust relationship between porosity and permeability exists (Leary et al, 2012)…”

Section: Model Parameterizationmentioning

confidence: 99%

“…where κ 0 and ϕ 0 correspond to the minimum permeability and porosity, respectively, and 20 < ω < 60 (Leary et al, 2012). The empirical relation, expressed as Equation 5, mathematically connects fractures at the grain level to percolation of fluid via fracture density (Leary et al, 2012).…”

Section: Model Parameterizationmentioning

confidence: 99%

“…Most well‐log correlations result from fluctuations in the spatial distribution of fractures at the grain level (Leary & Malin, 2021), which affect porosity and permeability observations, and contribute to the

\frac{1}{{k}^{\beta }}

spectrum observed in a well‐log (Bean, 1996; Leary & Malin, 2021). According to empirical data obtained from well‐core samples, a robust relationship between porosity and permeability exists (Leary et al., 2012)

{\kappa }_{ij}={\kappa }_{0}{e}^{\omega \left({\phi }_{ij}-{\phi }_{0}\right)}

where κ 0 and ϕ 0 correspond to the minimum permeability and porosity, respectively, and 20 < ω < 60 (Leary et al., 2012). The empirical relation, expressed as Equation 5, mathematically connects fractures at the grain level to percolation of fluid via fracture density (Leary et al., 2012).…”

Section: Modelmentioning

confidence: 99%

See 4 more Smart Citations

On the Potential Role of Viscoelasticity in Fluid‐Induced Seismicity

Khajehdehi,

Davidsen

2023

JGR Solid Earth

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Fluid‐induced earthquakes adversely affect industrial operations like hydraulic fracturing (e.g., 4.6 Mw in Alberta, Canada) and enhanced geothermal systems (e.g., 5.5 Mw in Pohang, South Korea). Identifying all underlying physical processes contributing to fluid‐induced seismicity presents an open challenge. Recent work reports signatures of event‐event triggering or aftershocks—common for tectonic settings—within the context of fluid‐induced seismicity. Here, we investigate the underlying potential cause of these field observations from a modeling perspective. We extend a novel conceptual model to simulate the characteristics of crustal rheology and stress interactions in a porous medium by combining viscoelastic effects with fluid diffusion and invasion percolation associated with a point source. Our model successfully reproduces realistic aftershock behavior and statistical properties similar to those resulting from tectonic loading indicating that the statistical properties of aftershocks are unaffected by the fluid injection rate. At the same time, the Gutenberg‐Richter relation, the spatial footprint of fluid‐induced events and their dependence on the permeability field are largely unaltered by the viscoelasticity of the medium and the aftershocks it causes. Furthermore, we investigate the impact of varying fluid injection rates on detecting aftershocks and event‐event triggering sequences during viscoelastic stress redistribution. We find that when the injection rate is sufficiently high, aftershock detection and recovery of their statistical properties are only feasible when the underlying internal stress redistribution is directly accessible. This could explain why aftershocks have not been reported in some field studies of fluid‐induced seismicity.

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