Crustal Permeability ebook

Table of Contents

Title Page

Copyright

Dedication

Conversion factors for permeability and hydraulic-conductivity units

List of contributors

About the companion websites

Chapter 1: Introduction

Motivation and background

Nomenclature: porosity, permeability, hydraulic conductivity, and relative permeability

Static versus dynamic permeability

Contents of this book

Data structures to integrate and extend existing knowledge

Acknowledgments

Chapter 2: DigitalCrust – a 4D data system of material properties for transforming research on crustal fluid flow

Motivation

Data integration to transform science

The DigitalCrust vision

An action plan

Concluding remarks

Acknowledgments

Part I: The physics of permeability

Chapter 3: The physics of permeability

Chapter 4: A pore-scale investigation of the dynamic response of saturated porous media to transient stresses

Introduction

Background

Methods

Results

Discussion

Conclusions

Acknowledgments

Chapter 5: Flow of concentrated suspensions through fractures: small variations in solid concentration cause significant in-plane velocity variations

Introduction

Overview of experiments

Image analysis

Experimental results

Computational simulations

Concluding remarks

Acknowledgments

Supporting information

Chapter 6: Normal stress-induced permeability hysteresis of a fracture in a granite cylinder

Introduction

Theoretical aspects

Experimental procedures

Experimental procedures and results

Concluding remarks

Acknowledgements

Chapter 7: Linking microearthquakes to fracture permeability evolution

Introduction

Channeling flows through heterogeneous fractures at laboratory scale

Channeling flows through heterogeneous fractures beyond laboratory scale

Conclusions

Acknowledgments

Chapter 8: Fractured rock stress–permeability relationships from in situ data and effects of temperature and chemical–mechanical couplings

Introduction

Fractured rock stress–permeability relation and sample size effect

In situ

block and ultra-large core experiments

Borehole injection tests

Model calibration against excavation-induced permeability changes

Depth-dependent permeability of shallow bedrock

Model calibration against the Yucca Mountain drift scale test

Thermal and chemically mediated mechanical changes

Application to geoengineering activities and potential implications for crustal permeability

Concluding remarks

Acknowledgments

Part II: Static permeability

Chapter 9: Static permeability

Sediments and sedimentary rocks

Igneous and metamorphic rocks

Part II(A): Sediments and sedimentary rocks

Chapter 10: How well can we predict permeability in sedimentary basins? Deriving and evaluating porosity–permeability equations for noncemented sand and clay mixtures

Introduction

Data and methods

Results

Conclusions

Acknowledgments

Supporting information

Chapter 11: Evolution of sediment permeability during burial and subduction

Introduction

Subduction zone sediments

Methods

Results and discussion

Conclusions

Acknowledgments

Supporting information

Part II(B): Igneous and metamorphic rocks

Chapter 12: Is the permeability of crystalline rock in the shallow crust related to depth, lithology, or tectonic setting?

Introduction

Data sources, synthesis, and analysis

Results and discussion

Conclusions

Data availability

Acknowledgments

Chapter 13: Understanding heat and groundwater flow through continental flood basalt provinces: insights gained from alternative models of permeability/depth relationships for the Columbia Plateau, United States

Introduction

Background

Methods of analysis and results

Results

Discussion

Conclusions

Acknowledgments

Chapter 14: Deep fluid circulation within crystalline basement rocks and the role of hydrologic windows in the formation of the Truth or Consequences, New Mexico low-temperature geothermal system

Introduction

Field measurements

Thermal Peclet number analysis methods

Geothermometry methods

Hydrothermal modeling methods

Results

Discussion and conclusions

Acknowledgments

Chapter 15: Hydraulic conductivity of fractured upper crust: insights from hydraulic tests in boreholes and fluid–rock interaction in crystalline basement rocks

Introduction

Permeability – significance in fractured basement rocks

Permeability and fluid flow in the crust

Reactive fluid flow in the crust and its effect on permeability

Fluid flow and permeability structure of the upper crust

Summary and conclusions

Acknowledgments

Part III: Dynamic permeability

Chapter 16: Dynamic permeability

Oceanic crust

Fault zones

Crustal-scale behavior

Effects of fluid injection at the scale of a reservoir or ore deposit

Part III(A): Oceanic crust

Chapter 17: Rapid generation of reaction permeability in the roots of black smoker systems, Troodos ophiolite, Cyprus

Introduction

The Troodos ophiolite: Geological setting

Epidosite zones: Previous work

Results

Discussion

Conclusions

Acknowledgements

Part III(B): Fault zones

Chapter 18: The permeability of active subduction plate boundary faults

Introduction

Fault zone architecture: inferences about hydraulic properties and behavior

Observations of fluid flow, advective transport, and simple models

Quantitative constraints on fault zone permeability from measurements and flow models

Implications and key outstanding questions

Acknowledgments

Chapter 19: Changes in hot spring temperature and hydrogeology of the Alpine Fault hanging wall, New Zealand, induced by distal South Island earthquakes

Introduction

Setting and context

Copland hot spring temperature observations

Fluid chemistry of Copland hot spring

Dynamic shaking

Permanent deformation

Discussion

Summary and conclusions

Acknowledgements

Supporting information

Chapter 20: Transient permeability in fault stepovers and rapid rates of orogenic gold deposit formation

Introduction

Association between stepovers and mineralisation

Geometry and scaling properties of stepovers

Numerical analysis of the relationship between stepover geometry and fault damage

Discussion

Conclusions

Acknowledgements

Supporting information

Chapter 21: Evidence for long-timescale (>10 3 years) changes in hydrothermal activity induced by seismic events

Introduction

Study area

Methods

Results

Discussion and conclusions

Acknowledgments

Part III(C): Crustal-scale behavior

Chapter 22: The permeability of crustal rocks through the metamorphic cycle: an overview

Introduction

Permeability and fluid flow in metamorphic rocks

Permeability during devolatilization

The contribution of metamorphism to the permeability structure of the crust

Acknowledgements

Chapter 23: An analytical solution for solitary porosity waves: dynamic permeability and fluidization of nonlinear viscous and viscoplastic rock

Introduction

Mathematical formulation

Analytical solution for the 1D steady state

Discussion

Concluding remarks

Acknowledgments

Appendix: nondimensionalization

Chapter 24: Hypocenter migration and crustal seismic velocity distribution observed for the inland earthquake swarms induced by the 2011 Tohoku-Oki earthquake in NE Japan: implications for crustal fluid distribution and crustal permeability

Introduction

Data and method

Results

Discussion

CONCLUSIONS

ACKNOWLEDGMENTS

Chapter 25: Continental-scale water-level response to a large earthquake

Introduction

The Wenchuan earthquake and the groundwater-level monitoring network

Coseismic groundwater-level changes induced by the Wenchuan earthquake

Mechanisms of the coseismic water-level change

Discussion

Conclusions

Acknowledgments

Supporting information

Part III(D): Effects of fluid injection at the scale of a reservoir or ore-deposit

Chapter 26: Development of connected permeability in massive crystalline rocks through hydraulic fracture propagation and shearing accompanying fluid injection

Introduction

Numerical approach

Experiment and numerical analysis description

Results

Discussion on the development of connected permeability

Conclusions

Acknowledgments

Chapter 27: Modeling enhanced geothermal systems and the essential nature of large-scale changes in permeability at the onset of slip

Introduction

The Basel fluid injection experiment

The model

Initial conditions

Modeling results

Discussion

Conclusions

Acknowledgments

Chapter 28: Dynamics of permeability evolution in stimulated geothermal reservoirs

Introduction

Methodology

Coupling strategy

Results

Conclusion

Chapter 29: The dynamic interplay between saline fluid flow and rock permeability in magmatic–hydrothermal systems

Introduction

Porphyry copper and epithermal gold deposits

Methods

Results

Discussion

Conclusions

Acknowledgments

Part IV: Conclusion

Chapter 30: Toward systematic characterization

Index

End User License Agreement

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Guide

Table of Contents

Part I

Begin Reading

List of Illustrations

Chapter 1: Introduction

Figure 1.1 Crustal-scale permeability (

k

) data. Arrows above the graph indicate approximate ranges of

k

over which certain geologically significant processes are likely. The “mean crust”

k

curve is based on

k

estimates from hydrothermal modeling and the progress of metamorphic reactions (Manning and Ingebritsen 1999). However, on geologically short timescales,

k

may reach values significantly in excess of these mean crust values (Ingebritsen and Manning 2010). The power-law fit to these high-

k

data – exclusive of the Sumatra datum (Waldhauser

et al

. 2012) – is labeled “disturbed crust.” The evidence includes rapid migration of seismic hypocenters (solid circles), enhanced rates of metamorphic reaction in major fault or shear zones (open circles), recent studies suggesting much more rapid metamorphism than had been canonically assumed (solid squares), and anthropogenically induced seismicity (open squares); bars depict the full permissible range for a plotted locality and are not Gaussian errors. Red lines indicate

k

values before and after enhanced geothermal systems reservoir stimulation at Soultz (upper line) (Evans

et al

. 2005) and Basel (lower line) (Häring

et al

. 2008) and green rectangle is the

k

-depth range invoked in modeling the formation of porphyry-copper ores (Weis

et al

. 2012). (

See color plate section for the color representation of this figure.

)

Figure 1.2 Cross section through a hypothetical ash-flow tuff unit showing typical values of porosity (

n

) and permeability (

k

)

.

The thickness of individual ash-flow tuff sheets ranges from a few meters to more than 300 m. Tertiary ash-flow tuffs are widespread in the western United States, particularly in the Basin and Range province.

Chapter 2: DigitalCrust – a 4D data system of material properties for transforming research on crustal fluid flow

Figure 2.1 The geologic scaffolding of DigitalCrust from the critical zone to the brittle–ductile transition (A), receiving contribution from and delivering service to a wide range of Earth science disciplines (B–E).

Chapter 4: A pore-scale investigation of the dynamic response of saturated porous media to transient stresses

Figure 4.1 Pore-scale representations of the four porous media. Warm colors (red) show larger pores. All textures are synthetically constructed; spheres and cuboids are constructed with a stochastic nucleation and growth algorithm following the procedure described in Hersum and Marsh (2006). (

See color plate section for the color representation of this figure

.)

Figure 4.2 Diagram showing the velocity discretization used for the lattice Boltzmann modeling. (A) The seven-velocity

e

i

(including a rest velocity

e

0

) model for the calculation of the formation factor is shown. (B) The 19-velocity model c, used for the flow calculations at the pore scale, is illustrated. (

See color plate section for the color representation of this figure.

)

Figure 4.3 Pore-size distribution for the four media calculated from the model of Yang

et al.

(2009). In this model, the radius of each pore is determined by the largest sphere that can be fully included into the pore.

Figure 4.4 Thermal field in the porous medium (cuboids) at steady state. Here, the solid fraction is assumed as a perfect insulator. The formation factor is computed from the effective thermal conductivity of the medium at steady state. (

See color plate section for the color representation of this figure.

)

Figure 4.5 Effective (dynamic) permeability as function of frequency for three of the four porous media used in this study. (A) How differences in porosity, static permeability

k

0

, and formation factor influence the response. (B) The rollover frequency

ω

c

provides a satisfying normalization factor to observe a self-similar behavior between the different media.

Figure 4.6 The phase lag

φ

between the pore-pressure forcing at the outlet and the discharge in the porous medium as function of frequency (A) and normalized frequency (B). The results are consistent with previous studies (Johnson

et al.

1987; Sheng & Zhou 1988; Smeulders

et al.

1992; Müller & Sahay 2011; Pazdniakou & Adler 2013).

Figure 4.7 Same as Figure 4.5 but with the last porous medium (spheres). Note that the medium referred to as spheres has the broadest pore-size distribution.

Figure 4.8 Phase lag between the pore-pressure fluctuations and the discharge as function of normalized frequency for all media.

Figure 4.9 Analogy between the dynamic permeability model of Johnson

et al.

(1987) and RC electric circuits in parallel. The spectral response of the effective permeability is similar to the spectral response of the overall impedance of the electric circuit.

Figure 4.10 Comparison between the sampled forcing frequency used in our simulations and the estimated range for the resonant frequency of the hyperbolic forced mass conservation equation

ω

*. We observe that our sampling should have allowed us to observe resonance in all four media. The dark grey ellipse marks the region where we observe resonance for the spheres data.

Figure 4.11 3D visualization of the pore-pressure field (normalized) at the forcing frequency corresponding to the maximum of the resonance peak in the spheres medium. The four images show the temporal evolution of the pressure field every quarter period. Note the regions with large lateral pore-pressure gradients (the imposed gradient is left to right) highlighted in red. It shows that flow pathways with different hydraulic connectivity have different response times to the forcing and that large pore-scale pore-pressure imbalance can emerge, violating the assumption of planar pressure wave propagating from the outlet. (

See color plate section for the color representation of this figure.

)

Chapter 5: Flow of concentrated suspensions through fractures: small variations in solid concentration cause significant in-plane velocity variations

Figure 5.1 Photograph of experimental setup and fracture cell (inset).

Figure 5.2 Schematic of inlet and outlet manifold configurations for Experiments A and B. The separate schematics shown for Experiments A and B highlight the difference between the manifold geometries for the two experiments. For Experiment A, a large rectangular channel (much more conductive than the fracture) bounded each end of the fracture. For Experiment B, the manifold gradually tapered from the inlet/outlet tubing to the fracture geometry. The schematic shows the location of the inlet (dark gray arrows), outlet (black arrows), and waste (light gray arrow; Experiment A only). Symbol ‘x’ marks the locations of the pressure ports that were connected to the differential pressure transducer.

Figure 5.3 Measured viscosities (

μ

f

) plotted against shear rate () for water, glycerol, and the 0.75% guar–water mixture used as the carrying fluid for the experiments.

Figure 5.4 Particle-size distribution for the solids used in the flow experiments. The solids consisted of angular silica sand grains.

Figure 5.5 Mass flow rate of water into the syringe pump (black circles) and differential pressure across the fracture (dark gray) plotted against time for Experiments A and B. The dashed black line is the ratio of the mass flow rate of high-

φ

effluent from the fracture over the mass flow rate of water into the syringe. At steady state, the dashed black line will be equal to the density of the high-solid-concentration fluid (∼1.8 kg l

−1

).

Figure 5.6 Volumetric flow rate through the fracture (

Q

out

) plotted against differential pressure (Δ

P

f

) for the full range of measured flow rates for both experiments.

Figure 5.7 Normalized velocity fields measured over the entire fracture for a subset of the flow rates for Experiments A and B superimposed with the corresponding streamlines. The velocity fields are normalized by the depth-averaged velocity (V =

Q

out

/W

⟨

h

⟩), during each step to facilitate the comparison of velocity fields and profiles at different flow rates. A jammed (∼zero velocity) region developed in Experiment A. High-velocity regions/bands are observed in both experiments near the no-flow (top and bottom) boundaries. See the included supplemental material for animations detailing the evolution of the velocity field for both experiments.

Figure 5.8 Normalized velocity profiles corresponding to the velocity fields shown in Figure 5.7. These are average profiles (over the fracture length) measured perpendicular to the flow direction. The velocity near the no-flow boundaries is approximately twice the velocity in the middle of the fracture.

Figure 5.9 The apparent Newtonian viscosity, μ, of fully developed slurry flow between two plates can be predicted given the Newtonian viscosity (μ

f

) of the carrier fluid and the solid concentration (φ) of the slurry (Lecampion & Garagash 2014).

Figure 5.10 The regular cell structure employed by the width-averaged flow solver. The domain

Ω

ij

of cell

ij

is highlighted in gray. The pressure,

P

ij

, is cell centered, while fluxes into neighboring elements are face centered.

Figure 5.11 Numerical simulation of fully developed flow of high-solid-volume slurry in an attempt to match the experimentally observed heterogeneities by introducing blockages within the upstream and downstream manifolds through changes to the aperture field (A) while assuming the fluid remains homogeneous. The velocity field (B) and profiles (C) indicate that the flow midway along the fracture is essentially homogeneous due to the dissipative nature of flow within the fracture.

Figure 5.12 Numerical simulation of fully developed flow of high-solid-volume slurry in the fracture with imposed heterogeneity in

φ

at the upstream boundary. The gray scale represents the evolving values of

φ

within the fracture. In this example, we impose

φ

= 0.47 at the upstream boundary (at right) within high-speed channels of approximately 2 cm width. The central region has

φ

= 0.515, resulting in

φ

= 0.5 overall. Our numerical model indicates that prescribed upstream variations in

φ

will propagate from inlet to outlet in a stable manner.

Figure 5.13 Steady-state velocity profiles corresponding to the heterogeneous-solid-concentration model shown in Figure 5.12. The model predicts that the slurry velocities are approximately doubled in regions of approximately 2 cm width, which is in good agreement with the experiment (Figure 5.8)

Figure 5.14 This plot explores the influence of increasing solid-concentration contrast in the fracture on the heterogeneity in the flow field using the same geometry shown in Figure 5.12. The lower curve shows the predicted ratio of the velocity in the central portion of the fracture to the velocity in the two 2-cm-wide low-

φ

channels. As

φ

approaches 0.34 in the fast channel, the flow in the central region stagnates. A value of

φ

= 0.47 approximates the experimental observation of velocity doubling in the fast channel zones (a ratio of 0.5 on this plot). The upper curve shows the ratio of total flow rate in the heterogeneous scenario compared with the flow of a homogeneous solid concentration of 0.5 for the same pressure drop across the fracture. We see that even as the central region stagnates, the total flow rate differs from the homogeneous solution by only tens of percent.

Figure 5.15 We explore the influence of varying the width of fast channels with

φ

= 0.47, while maintaining the same average value of

φ

across the entire fracture using the same geometry as shown in Figure 5.12. The upper curve shows the predicted ratio of the flow rate in the slower central portion of the fracture to two channels with

φ

= 0.47. As the width of the fast channels increases, the flow in the central region progressively slows due to increased solid concentration. Also, two channels of width 2 cm approximate the experimental observation of velocity doubling in the fast channel zones (a ratio of 0.5 on this plot). The upper curve shows the ratio of total flow rate in the heterogeneous scenario compared with the flow of a homogeneous solid concentration of 0.5 for the same pressure drop across the fracture. Even as the central region stagnates, the total flow rate is well approximated by the homogeneous solution.

Chapter 6: Normal stress-induced permeability hysteresis of a fracture in a granite cylinder

Figure 6.1 Cylindrical sample of the Barre Granite used in the experimental investigations containing a V-notch for fracture creation.

Figure 6.2 Details of the experimental arrangements for the application of external loads and for the pressurization of the central fluid-filled cavity.

Figure 6.3 The LVDT arrangement for movements of the fracture.

Figure 6.4 Schematic view of the radial flow permeability testing arrangement.

Figure 6.5 Demec markers used for mated alignment of the fractured segments of the cylinder and measurement of the initial aperture width.

Figure 6.6 The fracture surface topography (all dimensions are in mm).

Figure 6.7 Fracture closure during application of normal stresses. The starting position of the fracture during cycle 3 does not coincide with the end position of cycle 2. This is most likely due to unloading effects and the release of elastic stain energy in the system. The discrepancy, however, does not affect the third cycle of loading and the aperture closure trend with increasing axial stress is consistent with that observed in cycles 1 and 2. (

See color plate section for the color representation of this figure.

)

Figure 6.8 Evolution of the permeability of the fracture during quasi-static application of loading–unloading cycles. (

See color plate section for the color representation of this figure.

)

Figure 6.9 Fracture topography mismatch after three cycles of axial compression normal to the plane of the fracture to a maximum stress of 7.5 MPa. (

See color plate section for the color representation of this figure.

)

Figure 6.10 Leakage through the matrix region during fracture permeability testing using a double-packer system.

Chapter 7: Linking microearthquakes to fracture permeability evolution

Figure 7.1 Channeling flow seen as the discrete outflow of groundwater from a continuous fracture at the Tatsunokuchi gorge in Sendai city, Japan.

Figure 7.2 (A) Cylindrical granite samples containing fracture network and (B) 3D channeling flow within the fracture network analyzed by GeoFlow.

Figure 7.3 (A) Cylindrical granite samples containing single tensile fractures of different sizes having (A) no shear displacement or (B) a shear displacement of 5 mm in the radial direction, and (C) experimental system for permeability measurement under confining stress.

Figure 7.4 Changes in (A) fracture permeability, (B) geometric mean of aperture, and (C) contact area with fracture length for real laboratory-scale fractures with no shear displacement and with a shear displacement of 5 mm at confining stresses of 10 and 30 MPa.

Figure 7.5 Representative results for channeling flow within the heterogeneous aperture distribution of real laboratory-scale fractures (A) with no shear displacement and (B) with a shear displacement of 5 mm.

Figure 7.6 Representative results for channeling flow within the heterogeneous aperture distribution of synthetic fractures (A) with no shear displacement, (B) with a shear displacement of 5 mm, and (C) with a shear displacement of constant

δ/l

(

δ/l

= 0.01).

Figure 7.7 Comparisons of (A) the geometric mean of aperture and (B) permeability between synthetic and real laboratory-scale fractures with no shear displacement and with a shear displacement of 5 mm. Values for the real fractures are determined at confining stresses of 10 and 30 MPa.

Figure 7.8 Comparison of maximum aperture–fracture length relations between synthetic and natural fractures. The results are for synthetic fractures with constant ratios of shear displacement to fracture length (

δ/l

= 0.0025, 0.005, 0.01, and 0.02). One of the linear curves,

e

max

= 2.5 × 10

−3

l

0.48

, corresponds to the relation for a joint, whereas the other linear curves correspond to the relation for a fault (Schlische

et al.

1996; Schultz

et al.

2008).

Figure 7.9 Predicted typical changes in the (A) geometric mean of aperture, (B) permeability, and (C) flow area with fracture length, for fractures with no shear displacement (joints) and fractures with constant ratios of shear displacement to fracture length (faults). Note that the curves approximated by Eqs 7.6, 7.7, 7.9, and 7.10 are represented.

Figure 7.10 (A) Estimated relations between fracture length and moment magnitude and shear displacement and moment magnitude, and changes in (B) mean aperture and (C) fracture permeability as a function of moment magnitude.

Chapter 8: Fractured rock stress–permeability relationships from in situ data and effects of temperature and chemical–mechanical couplings

Figure 8.1 Two alternative ways (Path 1 and Path 2) for deriving a stress–permeability relationship of a fractured rock unit. Path 1 involves laboratory testing on single fractures and an effective medium theory, whereas Path 2 involves back analysis by model calibration against field data. (

See color plate section for the color representation of this figure

.)

Figure 8.2 Schematic showing the effects of unrepresentative sampling through a fracture. (A) The

in situ

fracture and (B) potential core samples if drilled through the fracture. The large void under

in situ

conditions may not be captured in laboratory experiment on core samples that would indicate a smaller aperture and higher stiffness than for the original larger scale fracture.

Figure 8.3 Typical hydromechanical behavior of rock fractures during normal (A, B) and shear (C, D) deformation (Rutqvist & Stephansson 2003). Effects of sample size is indicated with the laboratory sample response (dashed lines) compared with

in situ

fracture response (1-m

2

size).

Figure 8.4 Simulation of the horizontal permeability evolution of an intensely fractured rock mass subject to increasing shear stress (

σ

x

/

σ

Y

) ratio (results from Min

et al.

2004). Elastic simulation results are compared with that of elastoplastic using Mohr–Coulomb failure along fractures to investigate the effect of shear dilation on horizontal permeability.

Figure 8.5 Hydromechanical behavior of fractures from

in situ

block and ultra-large core experiments: (A) fracture conductivity (in cm s

−1

) as a function of normal stress for several experiments of different sample sizes compared in Witherspoon

et al.

(1979), (B) unit flow rate (in cm

3

s

−1

MPa

−1

) as a function of normal stress for a fractured ultra-large core specimen (Sundaram

et al.

1987), (C) unit flow rate (in cm

3

s

−1

MPa

−1

) as function of normal stress for an

in situ

block experiment (Hardin

et al.

1982), (D) stress-versus-hydraulic conducting aperture from an

in situ

block experiment, including a mineral-filled fracture (Makurat

et al.

1990).

Figure 8.6 Uniaxial compression on an ultra-large core of fractured Stripa Granite conducted at the Lawrence Berkeley National Laboratory (Thorpe

et al.

1982). (A) Schematic of test arrangement, (B) fracture mapping and seepage during a hydraulic test conducted before the unixial compression test, (C) shear stress versus shear displacement, (D) normal stress versus normal displacement, and (E) flow versus axial stress.

Figure 8.7

In situ

determination of stress–transmissivity relationships, using a combination of pulse, constant head, and hydraulic jacking tests. (A) Schematic representation of pressure and flow versus time, (B) the radius of influence in each test, and (C) results of a hydraulic jacking test at 267 m depth.

Figure 8.8 Back-calculated hydromechanical properties of fractures intersecting a borehole at a crystalline rock site in Sweden (Rutqvist

et al.

1997): (A) Fracture transmissivity versus effective normal stress for fractures at depths between 266 and 338 m (solid lines) with comparison to results from

in situ

block experiment by Makurat

et al.

(1990a) and ultra-large core experiment by Witherspoon

et al.

(1979) (dashed lines), (B) borehole image indicating open fractures at about 338 m depth. (

See color plate section for the color representation of this figure

.)

Figure 8.9 Calculated and measured permeability changes around the TSX tunnel (Rutqvist

et al.

2009b). Permeability versus radius along (A) a horizontal profile from the side of the tunnel and (B) a vertical profile from the top of the tunnel. (

See color plate section for the color representation of this figure

.)

Figure 8.10 Calibrated stress-versus-permeability relationship according to Eq. 8.1, with

β

1

= 4 × 10

−7

Pa

−1

,

k

r

= 2 × 10

−21

m

2

, Δ

k

max

= 8 × 10

−17

m

2

,

γ

= 3 × 10

−7

Pa

−1

, and the critical deviatoric stress for onset of shear-induced permeability is set to 55 MPa. MEQ refers to microearthqaukes, which were also triggered in areas around the tunnel where deviatoric stress exceeded 55 MPa.

Figure 8.11 Results of pre- to postexcavation air-permeability tests above a niche in fractured unsaturated tuff. The results shown are for a niche with three boreholes (UL, UM, and UR) located above niche 3560 (Wang

et al.

2001). (

See color plate section for the color representation of this figure

.)

Figure 8.12 Measured and calculated mean values of pre- to postpermeability change ratio at three niches (Niche 3107, 3560, and 4788).

Figure 8.13 Fracture stress–transmissivity behavior of fractures back-calculated from permeability measurements during excavation of niches: (A) exponential stress–aperture relationship and (B) corresponding stress–transmissivity relationship.

Figure 8.14 Depth-dependent permeability in fractured rock obscured by chemical and mechanical processes (Rutqvist & Stephansson 2003). Permeability was measured in short-interval well tests in fractured crystalline rocks at Gideå, Sweden (data points from Wladis

et al.

1997). Schematic of effects of shear dislocation and mineral precipitation/dissolution processes that obscure the dependency of permeability on depth (stress). The permeability values on the left-hand side represent intact rock granite, whereas the permeability values on the right-hand side represent highly conductive fractures.

Figure 8.15 Field data of (A) fractured rock permeability, (B) fracture frequency, and (C) calculated hydraulic conducting aperture through a vertical sequence of fractured volcanic rock units at Yucca Mountain, Nevada (Rutqvist 2004). Subparallel curved lines in (C) correspond to the exponential stress–aperture function for various residual apertures.

Figure 8.16 Three-dimensional view of the Yucca Mountain drift scale test. The color-coded lines indicate boreholes for various measurements of thermally driven thermal–hydrological–mechanical–chemical responses.

Figure 8.17 Schematic of the range of measurements of air permeability at the Yucca Mountain drift scale test. Details of calculated and measured responses in all 44 measurement intervals can be found in

Figure 8.18 Fracture stress-versus-permeability behavior back-calculated from air-permeability measurements at the Yucca Mountain drift scale test: (A) fractured rock permeability versus stress along one set of fractures and (B) corresponding aperture-versus-stress relationship for two assumptions regarding the spacing of dominant fractures.

Figure 8.19 (A) Perspective of the Terra Tek

in situ

block experiment (Adapted from Hardin

et al

. 1982 and (B) comparison of calculated (lines) and measured (filled circles) evolution of hydraulic conducting fracture aperture with stress and temperature at the Terra Tek

in situ

heated block experiment.

Figure 8.20 (A) Perspective of G-tunnel heated block experiment (Adapted from Zimmerman

et al

. 1985) and (B) hydraulic conducting aperture as a function of normal stress evaluated from the G-tunnel

in situ

block experiment. The data from Zimmerman

et al.

(1985) are separated in the sequential steps showing additional fracture closure as a result of heating.

Figure 8.21 Comparison of stress-versus-aperture relationships derived by model calibration from Yucca Mountain niche-excavation experiments (dashed curve) and the drift scale test (solid curve) and possible chemically mediated fracture closure as a result of rock-mass heating during the drift scale test.

Figure 8.22 Compilation of permeability measurements in boreholes in crystalline bedrock (from Juhlin

et al.

1998) with added schematic of upper and lower limits of permeability related to mechanical and chemomechanical behavior. (

See color plate section for the color representation of this figure

.)

Chapter 9: Static permeability

Figure 9.1 Lithologic composition of continental crust with depth; arrows indicate proportions of outcrop at the Earth's surface from Food and Agricultural Organization (FAO) global maps.

Chapter 10: How well can we predict permeability in sedimentary basins? Deriving and evaluating porosity–permeability equations for noncemented sand and clay mixtures

Figure 10.1 Porosity and permeability data of sand–clay mixtures and pure sands and clays. (

See color plate section for the color representation of this figure.

)

Figure 10.2 Grain size distribution data for samples from (A) well AST-02 in the Roer Valley Graben (Heederik 1988) and (B) the London Clay data set (Dewhurst

et al

. 1999a). (C) Good correlation (

R

2

= 0.94) between mean grain size and clay content suggests that independent information on clay content can be used to provide rough estimates of mean grain size. In general, the standard deviation of the distribution of grain size increases with increasing clay content, although clay content only explains 56% of the variation of the grain size distribution (D).

Figure 10.3 Comparison of calculated and observed permeability for (A) quartz sand and (B) pure clays. For sands, the Kozeny–Carman equation (Eq. 10.1) reproduces the data well, but only when the equation includes a percolation threshold and the value of the specific surface is calibrated. For clays, the permeability data are closely matched when permeability is calculated as a power law function of the void ratio (Eq. 10.4). Data for sands were reported by Bourbie & Zinszner (1985). Permeability data for pure clays were obtained from Al-Tabbaa & Wood (1987), Mesri & Olson (1971), Olsen (1966), and Vasseur

et al

. (1995). The Figure also shows data on mixed clay types from Schloemer & Krooss (1997) and Neuzil (1994) that were not used to calibrate the porosity–permeability equations. See Table 10.2 for the fit statistics of the permeability equations and Table 10.3 for calibrated parameter values. (

See color plate section for the color representation of this figure.

)

Figure 10.4 Relation of permeability to (A) porosity, (B) clay content, and (C) mean grain size and (D) the relation between clay content and porosity for two data sets of natural sand–clay mixtures and one experimental data set that consists of a mixture of kaolinite and quartz sand with a uniform grain size. The data for natural sediments were derived from unconsolidated shallow marine sands in the Roer Valley Graben (Heederik 1988) and the London Clay in southeast England (Dewhurst

et al

. 1999a). The experimental data were reported by Knoll (1996). The calculated permeabilities of the clay and sand fraction of each sample of the Roer Valley Graben and London Clay data sets are also shown in (A). The permeabilities of the Roer Valley Graben and the London Clay data sets are relatively close to the calculated permeabilities of their sand and clay fractions, respectively. The error bars for the clay fraction reflect the uncertainty in the mineral composition. (

See color plate section for the color representation of this figure.

)

Figure 10.5 Normalized permeability difference for two natural and one experimental data sets of sand–clay mixtures. The normalized permeability difference is calculated as the difference between the log-transformed permeability and the calculated permeability of the pure clay fraction, normalized by the difference in permeability between pure sand and clay. The three data sets show markedly different behavior. The fine-sand and silt-dominated samples of the Roer Valley Graben data set maintain relatively high permeability over the entire range of clay contents of 0–60%. In contrast, the experimental data set shows permeability decreasing rapidly with increasing clay content. The London Clay data set shows relatively low values of permeability that are close to the predicted permeability of the clay fraction, even though the samples also contain a silt fraction of 35–45%. The permeabilities calculated as the harmonic, geometric, or arithmetic mean of the sand and clay components are also shown for reference. Note that, due to the normalized log scale on the

y

-axis, the harmonic mean and arithmetic mean permeability cannot be shown as a single line.

Figure 10.6 Comparison between observed permeability of natural sediments and permeability calculated as the harmonic, geometric, and arithmetic mean of the sand and clay fractions (A–C) and calculated values of the power mean exponent (

P

) for each sediment sample (D). Calculated values of the power mean exponent (

P

) for each sediment sample. For the Roer Valley Graben data set, the calculated power mean exponent clusters around a value of 0 (D). For the London Clay data set, permeability is best predicted using a value of

P

that lies approximately halfway between the geometric and harmonic means, with a mean calculated value of

P

of −0.4.

Figure 10.7 Permeability anisotropy in

n

= 224 sediment samples from the Beaufort-Mackenzie Basin (Hu & Issler 2009);

k

h

and

k

v

denote horizontal and vertical permeability, respectively.

Figure 10.8 Clay content measured in core samples versus the difference between neutron porosity and observed porosity for the Roer Valley Graben data set. Neutron porosity includes water bound to clay minerals and is higher than the actual porosity in clay-rich sediments. The theoretical neutron porosity of kaolinite, illite, and smectite is shown for comparison (Rider 2002). The clay content of the core samples was calculated as the fraction of grain sizes smaller than 2 µm.

Figure 10.9 Comparison of well-log data with porosity, clay content, and permeability from core samples in well AST-02. Permeability was calculated using well-log-derived estimates of porosity, clay content, and grain size distribution. Grain size distribution was calculated using the empirical correlations between clay content and grain size distributions shown in Figure 10.2C and D. The calculated permeability shows a relatively good match with observed permeability and estimates permeability within 1 order of magnitude for 80% of the samples. The uncertainty range of the calculated permeability averages ±1.0 orders of magnitude and was calculated using minimum and maximum estimates of clay mineralogy and grain size distribution. (

See color plate section for the color representation of this figure.

)

Chapter 11: Evolution of sediment permeability during burial and subduction

Figure 11.1 (A) Locations of samples used in this investigation. (B) Schematic of a shallow subduction zone showing domains. This Figure was based on an accretionary system. For simplicity, only the plate boundary and frontal thrust are shown. In reality, the prism is extensively faulted. In an erosive system, only the seawardmost portion of the upper plate would be a sediment prism, typically consisting of reworked slope sediments. Scale bar gives example dimensions and vertical exaggeration (V.E.). (

See color plate section for the color representation of this figure.

)

Figure 11.2 Permeability–porosity relationships for clays. (A) Group 1 (>80% clay). (B) Group 2 (60–80% clay). (C) Group 3 (<60% clay). Best-fit lines are shown. Regression coefficients are given in Table 11.2. Tohoku data shown were not used for the best-fit lines. (

See color plate section for the color representation of this figure.

)

Figure 11.3 Comparison by measurement method. (A) Data for all clay samples. (B) Trends for all clay samples. (C) Comparison restricted to Group 3 samples. Regression coefficients are given in Table 11.2.

Figure 11.4 Permeability of silts/sands sediments. Regression coefficients are given in Table 11.2.

Figure 11.5 (A) Permeability of carbonate oozes and chalks by location. (B) Results separated by burial depth and combined with deep sediment data from Mallon

et al

. (2005). Regression coefficients are given in Table 11.2. (

See color plate section for the color representation of this figure.

)

Figure 11.6 Comparison by location. (A) Data. (B) Trends. Regression coefficients are given in Table 11.2. (

See color plate section for the color representation of this figure.

)

Figure 11.7 Comparison by structural domain. (A) Data for all clay samples. (B) Comparison restricted to Group 3 samples. Regression coefficients are given in Table 11.2. (

See color plate section for the color representation of this figure.

)

Figure 11.8 Anisotropy as a function of porosity for the prism, reference, and slope structural domains. Prediction from uniaxial consolidation and grain rotation (Eq. 1) is shown for reference. The parameters used for the uniaxial prediction were

n

0

= 0.8 and

θ

0

= 45°.

Figure 11.9 Comparison of permeability–porosity relationships from field data and laboratory consolidation tests. (A) Prism structural domain. (B) Reference structural domain. (C) Slope structural domain. Regression coefficients are given in Table 11.2.

Figure 11.10 Comparison of

γ

(white) and

k

0

(gray) determined from field and laboratory data. (A) By lithology. (B) By structural domain. (C) Only clays by coring system. (D) Only silts/sands by coring system. HPC/APC = hydraulic piston core; XCB = extended core barrel; and RCB = rotary core barrel. Error bars are given for all laboratory/field ratios.

Figure 11.11 (A) Single-packer test results from Barbados (Holes 948D, 949C) and Cascadia (Hole 892B) shown as box-and-whisker plots with fault-zone permeability data. (B) Permeability results from prism domain in the Kumano area with single-probe and dual-packer test results from Hole C0009A. Regression coefficients are given in Table 11.2.

Figure 11.12 Compilation of permeability data and best-fit lines for each group. Carbonate best-fit line only for samples >200 mbsf and includes deeper data from Mallon

et al

. (2005), which are shown in Figure 11.10B. (

See color plate section for the color representation of this figure.

)

Figure 11.13 (A) Comparison of Groups 2 and 3 with data from deep sediments belonging to Groups 2 and 3. Best-fit lines shown for subduction zone sediments (black) and all data (green). (B) Comparison of silt/sand data with deep silt/sand sediment data. Best-fit lines shown for subduction zone sediments (black) and all data (green). All regression coefficients are given in Table 11.2. (C) Permeability plotted against depth for Groups 2 and 3 and deep sediments showing depth range of deep sediments. (D) Permeability plotted against depth for silt/sand data and deep silt/sand samples. Note that the Boom Clay depth reported is the present-day depth; this formation is presumed to have been uplifted (Hildenbrand

et al.

2002). Data sources:

a

Hildenbrand

et al.

(2002),

b

Hildenbrand

et al.

(2004),

c

Yang & Aplin (2007),

d

Marschall

et al.

(2005),

e

Amann-Hildenbrand

et al.

(2013). (

See color plate section for the color representation of this figure.

)

Chapter 12: Is the permeability of crystalline rock in the shallow crust related to depth, lithology, or tectonic setting?

Figure 12.1 Locations of permeability data and indicators of (A) short-term (years) and (B) long-term (million years) tectonic activity. Permeability data are derived from southern Germany and the Black Forest (BF), the Molasse basin (MB) in Switzerland and the Fennoscandian Shield (FS) in Sweden. Seismic events in (A) denote events since the year 2000 that exceed the magnitude of 3 on the Richter scale from the National Earthquake Information Center (http://earthquake.usgs.gov/regional/neic/). (B) AFT denotes apatite fission track data obtained from Herman

et al.

(2013). Apatite fission track data are a proxy for long-term tectonic activity. The apatite fission track age is approximately equal to the last time the rock outcrop was at a temperature of 120 °C (Wagner & Reimer 1972), which at normal geothermal gradients corresponds to a depth of approximately 3–5 km. (

See color plate section for the color representation of this figure.

)

Figure 12.2 Full data set of permeability data for crystalline rock (

n

= 973). Black points are singular or average permeability values (

n

= 422). Red lines are permeability values reported over a tested interval (

n

= 426). Gray points are data with reported detection limits (

n

= 80). Green points are the midpoints of permeability values reported as ranges, with the error bar showing the range (

n

= 37). Purple lines are data with tested intervals >500 m (

n

= 8). The vertical extent of a point indicates the extent of the tested interval. (

See color plate section for the color representation of this figure.

)

Figure 12.3 The relationship between permeability and depth for the full data set, with error bars removed for clarity. Ranges are plotted as the midpoint. Gray rectangles indicate measurements at a detection limit. Purple lines indicate data points from tested intervals greater than 500 m. Red line indicates logarithmic fit through data (

R

2

= 0.230). Black line indicates Manning–lngebritsen fit (Ingebritsen & Manning 1999). Blue line indicates Shmonov

et al.

(2003) fit. Green line indicates Stober & Bucher (2007a) fit. Histograms display distribution of permeability data above and below 0.1 km. (

See color plate section for the color representation of this figure.

)

Figure 12.4 The distribution of permeability values in the full data set.

Figure 12.5 The relationship between permeability and lithology for metamorphic (blue) and intrusive (red) rocks. All data points are midpoints of tested intervals. Pink rectangles indicate intrusive detection limits. Cyan rectangles indicate metamorphic detection limits. Purple lines indicate data points from tested intervals >500 m. Reported

R

2

and

P

values are for logarithmic fits through data. Histograms identify the permeability distribution in four depth ranges. From top to bottom: <100, 100–200, 200–600, and >600 m. (

See color plate section for the color representation of this figure.

)

Figure 12.6 The relationship between permeability and tectonic setting. Red points indicate intrusive rocks. Blue points indicate metamorphic rocks. Pink rectangles indicate intrusive detection limits. Cyan rectangles indicate metamorphic detection limits. Purple lines indicate data points from tested intervals >500 m. All data points are midpoints. Reported

R

2

and

P

values are for logarithmic fits through the combination of intrusive and metamorphic data. Gray lines are functions from the literature (Stober & Bucher 2007; Jiang

et al.

2010c). (

See color plate section for the color representation of this figure.

)

Figure 12.7 The relationship between permeability and lithologies in different tectonic settings. Red indicates intrusive rocks. Blue indicates metamorphic rocks. Pink rectangles indicate intrusive detection limits, while cyan rectangles indicate metamorphic detection limits. Purple lines indicate data points from tested intervals >500 m. All data points are midpoints. Reported

R

2

and

P

values are for logarithmic fits through the data.

P

values in boldface indicate data sets that fail the

t

test at 5% significance. Histograms include text that indicates the median value of the distribution. (

See color plate section for the color representation of this figure.

)

Chapter 13: Understanding heat and groundwater flow through continental flood basalt provinces: insights gained from alternative models of permeability/depth relationships for the Columbia Plateau, United States

Figure 13.1 Heat-flow map of the northwestern United States showing apparent low heat flow in the vicinity of the Columbia Plateau Regional Aquifer System. Contours are based on published heat-flow data for the Cascade Range and adjacent regions assembled for the USGS geothermal database. The map was constructed using the methods of Williams & DeAngelo (2008). The

Approximate Extent of Columbia Plateau Regional Aquifer System

is the extent of Columbia River Basalts from the geologic model of Burns

et al.

(2011). (

See color plate section for the color representation of this figure.

)

Figure 13.2 Map of compiled borehole temperature logs (stars) and aquifer tests from groundwater supply wells (circles). The bottom of each temperature log is frequently at or above the depth of nearby aquifer tests, indicating that most temperature data were collected within the active groundwater system. To estimate intrinsic permeability from aquifer tests, the temperature of water pumped was estimated using the nearest temperature log. Representative cross sections A–A′ and B–B′ were used for coupled groundwater and heat-flow simulations. (

See color plate section for the color representation of this figure.

)

Figure 13.3 Conceptual model of aquifer system geometry (from Burns

et al.

2012a). Upland recharge can enter thin aquifers at flow margins. Geologic structures can act as flow barriers that may or may not crosscut all aquifers. (

See color plate section for the color representation of this figure.

)

Figure 13.4 Estimates of bulk horizontal permeability from Kahle

et al.

(2011) and Spane (2013). Using a 200 m window to compute a running average identifies a depth range of 600–900 m where permeability rapidly decreases. Above 600 m depth, average permeability is approximately constant. The deep data are sparse, but below 900 m depth, the rate of decrease in permeability apparently slows. Circles of the same color denote packer tests at different elevations within the same borehole. All other symbols represent single tests within a discrete borehole. (

See color plate section for the color representation of this figure.

)

Figure 13.5 Plot showing that permeability transitions rapidly in the temperature range 35–50 °C. For the Kahle data, temperatures were estimated from the nearest temperature log (Figure 13.2). For the remaining aquifer tests, temperatures were estimated from temperature logs collected within the borehole during a battery of geophysical tests. (

See color plate section for the color representation of this figure.

)

Figure 13.6 Estimated permeability vs. depth relations for use in the SUTRA models. The solid lines are the maximum (subhorizontal) permeability, and the dashed line is minimum (subvertical) permeability. The red lines are the power-law depth relation. The blue lines are the piece-wise-600 m depth relation. The constant-permeability depth relation model is not shown, but has a log permeability of −11.55, indistinguishable from the shallow exponential model value of −11.5. (

See color plate section for the color representation of this figure.

)

Figure 13.7 Typical cross sections through the Columbia Plateau Regional Aquifer System (see Figure 13.2). For both cross sections (units in m), the Columbia River is at the right-hand boundary, incised 250–300 m into the geologic units. (A) Cross section through the relatively gently dipping, low-recharge Palouse Slope. (B) Cross section from the crest of the higher recharge Blue Mountains through the Umatilla River basin. (Blue, pre-Miocene rocks; brown, Grande Ronde Basalts; green, Wanapum Basalts; red, Saddle Mountains Basalts; black, thin deposits of sedimentary overburden). (

See color plate section for the color representation of this figure.

)

Figure 13.8 Model formulation and boundary conditions: (A) hydrogeologic units with arrows showing that the preferential direction of permeability is rotated to align with the plane of basalt deposition (blue, pre-Miocene rocks; brown, Grande Ronde Basalts; green, Wanapum Basalts; red, Saddle Mountains Basalts); (B) hydrologic boundary conditions showing that recharge and discharge occur near the upper boundary and into the river on the right-hand side; (C) heat-flow boundary conditions showing the constant heat flux at the lower boundary and the prescribed temperature at land surface, with cooler temperatures in the uplands to the left. The thickness of the underlying ultralow-permeability unit was varied during model testing, with the final thickness being 20 km (Figure not to scale for this unit). (

See color plate section for the color representation of this figure.

)

Figure 13.9 Comparison of simulated temperatures for cross Section ′ (Umatilla) using the piece-wise-600 m depth relation. (A) shows the simulated subsurface temperature distribution assuming heat conduction only (no fluid flow). (B) shows the simulated temperature distribution for combined groundwater and heat flow. (C) shows the temperature difference between the two simulations; warmer colors show areas where temperatures are elevated by advective transport of heat, and cooler colors show areas where subsurface temperatures are lowered by heat advection. Plots are only colored in the part of the domain occupied by Columbia River Basalt Group units. (

See color plate section for the color representation of this figure.

)

Figure 13.10 Illustrations showing estimated vertical conductive heat flow for cross Section ′ (Umatilla) for the piece-wise-600 m depth relation: (A) local estimated vertical conductive heat flow and (B) estimated vertical heat flow for five depth ranges, computed as the bulk thermal conductivity times the representative gradient (slope of the best-fit line) of all temperatures in each depth range. Below approximately 600 m depth, heat flow is dominated by conduction. Plot A is only colored in the part of the domain occupied by Columbia River Basalt Group units. (

See color plate section for the color representation of this figure.

)

Figure 13.11 Illustrations showing estimated vertical heat flow for cross Section ′ (Umatilla) for the constant-permeability depth relation. Estimated vertical heat flow for five depth ranges, computed as the bulk thermal conductivity times the representative gradient (slope of the best-fit line), of all temperatures in each depth range (compare with Figure 13.10B). (

See color plate section for the color representation of this figure.

)

Figure 13.12 Heat-flow map with rivers; river line width is proportional to mean annual flow rate. Larger rivers are at the lowest elevations and receive water from long, regional groundwater flow paths. (

See color plate section for the color representation of this figure.

)

Chapter 14: Deep fluid circulation within crystalline basement rocks and the role of hydrologic windows in the formation of the Truth or Consequences, New Mexico low-temperature geothermal system

Figure 14.1 Crustal permeability versus depth relationships from previous investigations that provide a range of crustal permeabilities of 10

−18

to 10

−10

for depths <10 km. Crystalline basement permeabilities within the Rio Grande Rift are also indicated (black circles) and are high in comparison. This suggests that large fault structures and significant fracture networks have substantially increased the permeability of crystalline basement rocks in this region of New Mexico above global averages.

Figure 14.2 Boundary conditions used in our two-dimensional hydrothermal model and a schematic diagram showing the two groundwater flow hypotheses evaluated as part of this study. Boundary conditions are shown for both heat transport and groundwater flow. The inset portrays a close-up of the boundary conditions applied to the hot-springs district. The basement-circulation hypothesis (blue arrows) involves deep circulation of groundwater within highly fractured crystalline basement rocks. Groundwater discharges where hydrologic windows exist in overlying confining units, such as the Percha Shale (black). The shallow-circulation hypothesis (red arrows) considers shallow groundwater circulation through the carbonate Magdalena Group. (

See color plate section for the color representation of this figure.

)

Figure 14.3 Basemap (A) showing surface water drainages (light blue lines) and the location of the study area in south-central New Mexico (B). The presumed recharge area of the Sierra Cuchillo and San Mateo Mountains north of Truth or Consequences is also indicated for reference. The orientation of the geologic cross section and two-dimensional hydrothermal model is shown in black (A–B–C–D) in addition to the locations of wells discussed in this paper. The inset of the hot-springs district (C) shows geothermal well locations. The Rio Grande can also be seen in the lower right corner of this inset. The delineated areas on the New Mexico state map (B) are major drainage basins. Regional map coordinate datum is UTM NAD83 Zone 13. (

See color plate section for the color representation of this figure.

)

Figure 14.4 Geologic cross section depicting the stratigraphic units used in our two-dimensional hydrothermal model. Additional information about model parameters is provided in Table 14.1. The color legend of this Figure corresponds to the hydrostratigraphic units and descriptions in Table 14.3. The cross section was constructed by utilizing oil-well data, an east–west regional cross section (Lozinsky 1987), gravity data (Gilmer

et al.

1986), and surface geologic maps.

Figure 14.5 Groundwater contributing area (color-shaded contours) to the hot-springs district (HSD) and water-table contours in relation to our two-dimensional hydrothermal model transect (bold black line). The color contours denote the spatial distribution of annual precipitation across the watershed. The black contour lines are estimated water-table elevations from the New Mexico Office of the State Engineer. Precipitation and water-table patterns suggest recharge to the HSD primarily occurring in the Sierra Cuchillo and San Mateo Mountains northwest of Truth or Consequences. Apparent carbon-14 groundwater ages collected as part of this project are displayed on the basemap as well (annotated circles). Oldest groundwater ages are within the HSD (precipitation data from PRISM Climate Group, Oregon State University 2012). (

See color plate section for the color representation of this figure.

)

Figure 14.6 Temperature–depth profiles measured within the hot-springs district during October 2012–2013. The type of well the profile was measured in is indicated by its color (blue = type 1, red = type 2, green = type 3; see methods section for details). A calculated average profile for depths 0–44 m is also plotted (bold black line). The average profile was only calculated to this depth due to lack of abundant data at greater depths and was used when interpreting hydrothermal model results. (

See color plate section for the color representation of this figure.

)

Figure 14.7 Peclet number analysis results from two representative wells. Our best-fit curve-matched results indicate vertical specific discharge rates ranging from 2 to 4 m year

−1

beneath the hot-springs district (see Figure 14.3C for well locations).

Figure 14.8 Giggenbach (1991) plot of the geothermal waters collected in the hot-springs district. This plot classifies them as “immature waters,” implying that cation geothermometry results may not be representative of geothermal reservoir temperatures at depth due to mixing or lack of equilibration.

Figure 14.9 Piper diagram summarizing geochemical analyses discussed in this paper. The geothermal waters from the hot-springs district (HSD) are shown with red circles. Mildly geothermal waters in the vicinity (within 16 km) of the HSD are plotted as orange triangles. Two samples of Rio Grande surface waters collected upstream of Truth or Consequences are shown using yellow circles. Data from a low-temperature carbonate geothermal reservoir in the Etruscan Swell of Italy (Chiodini

et al.

1995) are shown with green squares. Data from the Woods Tunnel slim hole from the Socorro, New Mexico, geothermal system are shown by blue squares (Owens 2013). Truth or Consequences waters have a Na

+

/CI

−

signature characteristic of geothermal waters derived from igneous and metamorphic rocks. (

See color plate section for the color representation of this figure.

)

Figure 14.10 Plots of groundwater temperature (A), Na

+

(B), and B and Li

+

(C) against CI

−

. Mildly geothermal groundwaters near the hot-springs district (HSD) (within 16 km) are shown using black squares. The line in (A) connects mean annual temperature at Truth or Consequences (16.5 °C) to the temperature of the geothermal waters. The line in (B) is a 1:1 line. The arrow in (C) highlights a well located about 5 km north of the HSD that has elevated concentrations of boron. All plots show covariation, suggesting that geothermal Na

+

/CI

−

waters have undergone mixing with shallow non-geothermal groundwater.

Figure 14.11 Comparison of computed regional groundwater flow patterns (black lines with arrows) and temperatures for three shallow-circulation scenarios (left, A–C) and four basement-circulation scenarios (right, D–G). The base-10 logarithm of permeabilities used for the Magdalena Group and the crystalline basement are listed above each plot. Refer to Table 14.5 for simulation parameters and goodness of fit for subplots A–G. The location of the hot-springs district (HSD) is shown in graphic D. Groundwater flow directions are parallel to streamlines. Groundwater reaching the HSD in the shallow-circulation scenarios flows primarily through the shallow Magdalena Group. In contrast, deep-circulation scenarios are characterized by geothermal waters derived predominately from crystalline basement rocks. Shallow-circulation scenarios yield thermal patterns typical of a conductive thermal regime. (

See color plate section for the color representation of this figure.

)

Figure 14.12 Comparison of computed hot-springs district (HSD) temperature patterns for three shallow-circulation scenarios (left) and four basement-circulation scenarios (right). Refer to Table 14.5 for simulation parameters and goodness of fit for subplots A–G. The relative regional location of these cross sections is shown in Figure 14.11D denoted by “HSD”. Only two (simulations E and F) of the presented simulations reproduced average measured HSD temperatures of 41 °C. Both of these simulations required highly permeable crystalline basement rocks (10

−13

and 10

−12

m

2

). Increasing or decreasing basement permeabilities beyond this range resulted in reduced temperatures in the HSD. (

See color plate section for the color representation of this figure.

)

Figure 14.13 Comparison of simulated and observed temperatures along the model domain. The assigned base-10 logarithm permeabilities for the crystalline basement rocks (PC) and Magdalena Group (Pm) are shown in the legends (graphs A and B share the same legend). Simulated temperature profiles are compared to bottom-hole temperature data collected in oil wells approximately 15 km north of Truth or Consequences (Top, A and B). They agree fairly well with bottom-hole temperature data, suggesting that our assigned thermal properties, such as basal heat flux and thermal conductivities, represent those of the study area. Average simulated and measured temperature profiles from the hot-springs district (HSD) are compared in C and D. Only simulations having high crystalline basement permeability (10

−13

and 10

−12

m

2

) were able to reproduce observed HSD temperatures. (

See color plate section for the color representation of this figure.

)

Chapter 15: Hydraulic conductivity of fractured upper crust: insights from hydraulic tests in boreholes and fluid–rock interaction in crystalline basement rocks

Figure 15.1 log (

T

/

H

) data from well tests at five different depths in the Urach 3 borehole in gneiss of the Black Forest crystalline basement.

Figure 15.2 Schematic water flow systems in a crystalline basement area with moderate topography (e.g., Black Forest basement). The permeability contrast between granite and gneiss has a strong control on the flow paths, in addition to topography and fault structures. The Figure also illustrates changing water composition along flow.

Figure 15.3 Section from the Rhine River rift valley to the Black Forest basement along the Kinzig River valley. Horizontal scale of section about 20 km. Recharge water from the Black Forest is channeled along a fault system to the surface and does not reach the Rhine graben structure. For more details see Stober

et al.

1999.

Figure 15.4 Serpentine reaction veins in peridotite (Erro Tobbio mantle, Italy). The serpentinization of peridotite occurred along symmetrical reaction veins along brittle fractures. During the active period of serpentinization, the permeability of the fractured rock permitted flow and reaction of aqueous fluid. Flow and reaction ceased because of self-sealing of the fractures, preventing the peridotite (brown crust) from being completely serpentinized. Later, the fractures became completely sealed, and the vein system does not contribute to the near-surface permeability of the partly serpentinized peridotite. (

See color plate section for the color representation of this figure.

)