NNE Strike: (0-30 / 180-210) Techniques for Large-Scale...

Techniques for Large-Scale Site Characterization and Fracture Domain Modeling At The Forsmark Site, Uppland, Sweden

Aaron Fox1, Paul La Pointe

1, and Jan Hermanson

2

1Golder Associates, Inc., Redmond, WA, USA; 2Golder Associates AB, Stockholm, Sweden

Introduction to the Forsmark Site

Modeling Spatial Variation of Fracture Orientations

Fracture Domains as Control Volumes

Intensity Controls Through Multivariate Statistics

How Fracture (and Fault) Sizes are Treated

Acknowledgements and References

OSM Scale: 0.5m - ~ 564 m radius (~1m –

1000 m trace length) – Predominantly Joints

TFM Scale: 28m - 564m radius (50m – 1000m

trace length) – Predominantly Faults

OSM Scale: 0.5m - ~ 564 m radius (~1m –

1000 m trace length) – Predominantly Joints

TFM Scale: 28m - 564m radius (50m – 1000m

trace length) – Predominantly Faults

OSM Scale: 0.5m - ~ 564 m radius (~1m –

1000 m trace length) – Predominantly Joints

TFM Scale: 28m - 564m radius (50m – 1000m

trace length) – Predominantly Faults

NE Global Set (Euclidean scaling), Domain FFM03

1.00E-09

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+00

1.00E+01

0.1 1 10 100 1000 10000 100000

Tracelength (m)

Are

a-N

orm

ali

ze

d C

um

ula

tiv

e N

um

be

r

PFM 2.2 DZ Model, Clipped

PFM 2.2 DZ Model, Regional

Ground Magnetic Lineaments

AFM000053 (Linked)

AFM001243 (Linked)

AFM001244 (Linked)

TCM

~0.28

Range of TCM / TCMF models (0.5m to 564m radius, ~ 0.89m – 1000m trace length )

NE Global Set (Euclidean scaling), Domain FFM03

1.00E-09

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+00

1.00E+01

0.1 1 10 100 1000 10000 100000

Tracelength (m)

Are

a-N

orm

ali

ze

d C

um

ula

tiv

e N

um

be

r

PFM 2.2 DZ Model, Clipped

PFM 2.2 DZ Model, Regional

Ground Magnetic Lineaments

AFM000053 (Linked)

AFM001243 (Linked)

AFM001244 (Linked)

TCM

~0.28

Range of TCM / TCMF models (0.5m to 564m radius, ~ 0.89m – 1000m trace length )

Model Summaryb

.499a .249 .201 .53992 .249 5.202 3 47 .003

Model

1

R R Square

Adjusted

R Square

Std. Error of

the Estimate

R Square

Change F Change df1 df2 Sig. F Change

Change Statistics

Predictors: (Constant), BRNPegmatite_pegmatiticgranite, BRNBreccia, BRNGranite_finetomediumgraineda.

Dependent Variable: NEP10b.

Coefficientsa

.820 .122 6.734 .000 .575 1.064

83.819 30.888 .344 2.714 .009 21.681 145.957 .364 .368 .343 .995 1.005

-2.926 1.817 -.205 -1.610 .114 -6.581 .730 -.198 -.229 -.203 .987 1.013

-1.526 .658 -.295 -2.320 .025 -2.849 -.202 -.284 -.320 -.293 .990 1.010

(Constant)

BRNBreccia

BRNGranite_

finetomediumgrained

BRNPegmatite_

pegmatiticgranite

Model

1

B Std. Error

Unstandardized

Coefficients

Beta

Standardized

Coefficients

t Sig. Lower Bound Upper Bound

95% Confidence Interval for B

Zero-order Partial Part

Correlations

Tolerance VIF

Collinearity Statistics

Dependent Variable: NEP10a.

Collinearity Diagnosticsa

1.955 1.000 .09 .02 .06 .09

1.000 1.398 .00 .79 .16 .00

.816 1.547 .01 .18 .69 .09

.229 2.923 .90 .02 .09 .82

Dimension

1

2

3

4

Model

1

Eigenvalue

Condition

Index (Constant) BRNBreccia

BRNGranite

_finetomediu

mgrained

BRNPeg

matite_

pegmatiti

cgranite

Variance Proportions

Dependent Variable: NEP10a.

Residuals Statisticsa

-.0657 2.1027 .6053 .30165 51

-.75004 1.75567 .00000 .52347 51

-2.224 4.964 .000 1.000 51

-1.389 3.252 .000 .970 51

Predicted Value

Residual

Std. Predicted Value

Std. Residual

Minimum Maximum Mean Std. Deviation N

Dependent Variable: NEP10a.

Model Summaryb

.499a .249 .201 .53992 .249 5.202 3 47 .003

Model

1

R R Square

Adjusted

R Square

Std. Error of

the Estimate

R Square

Change F Change df1 df2 Sig. F Change

Change Statistics

Predictors: (Constant), BRNPegmatite_pegmatiticgranite, BRNBreccia, BRNGranite_finetomediumgraineda.

Dependent Variable: NEP10b.

Coefficientsa

.820 .122 6.734 .000 .575 1.064

83.819 30.888 .344 2.714 .009 21.681 145.957 .364 .368 .343 .995 1.005

-2.926 1.817 -.205 -1.610 .114 -6.581 .730 -.198 -.229 -.203 .987 1.013

-1.526 .658 -.295 -2.320 .025 -2.849 -.202 -.284 -.320 -.293 .990 1.010

(Constant)

BRNBreccia

BRNGranite_

finetomediumgrained

BRNPegmatite_

pegmatiticgranite

Model

1

B Std. Error

Unstandardized

Coefficients

Beta

Standardized

Coefficients

t Sig. Lower Bound Upper Bound

95% Confidence Interval for B

Zero-order Partial Part

Correlations

Tolerance VIF

Collinearity Statistics

Dependent Variable: NEP10a.

Collinearity Diagnosticsa

1.955 1.000 .09 .02 .06 .09

1.000 1.398 .00 .79 .16 .00

.816 1.547 .01 .18 .69 .09

.229 2.923 .90 .02 .09 .82

Dimension

1

2

3

4

Model

1

Eigenvalue

Condition

Index (Constant) BRNBreccia

BRNGranite

_finetomediu

mgrained

BRNPeg

matite_

pegmatiti

cgranite

Variance Proportions

Dependent Variable: NEP10a.

Residuals Statisticsa

-.0657 2.1027 .6053 .30165 51

-.75004 1.75567 .00000 .52347 51

-2.224 4.964 .000 1.000 51

-1.389 3.252 .000 .970 51

Predicted Value

Residual

Std. Predicted Value

Std. Residual

Minimum Maximum Mean Std. Deviation N

Dependent Variable: NEP10a.

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Legend

Local Model Area

Candidate area

Repository Draft Layout, 11/08/2005

Regional Model Area

Aerial photo of the Forsmark site, with modeling domains and

draft repository layout overlain.

250 0 250 500 750 1,000125

Meters

F

Legend

NS Set Ground Magnetic Lineaments

NW Set Ground Magnetic Lineaments

NE Set Ground Magnetic Lineaments

ENE Set Ground Magnetic Lineaments

EW Set Ground Magnetic Lineaments

Candidate Area (limits of GeoDFN)

Lineament (DZ and MDZ) map created from high-resolution

ground magnetic survey data /Isaksson and others, 2006/

Work is currently underway at two

candidate sites in Sweden to license and

construct a long-term geologic repository

for spent nuclear fuel. The Forsmark site,

located in the municipality of Östhammar,

in northern Uppland, is one of these

candidate sites. The site sits on

Sweden’s eastern coast, and is adjacent

to the Forsmark Nuclear Power Plant and

the CLAB interim waste storage facility.

The site is located in Precambrian rocks

of the Fennoscandian Shield, in a

complex structural domain consisting

primarily of high-grade metamorphic

rocks of cut by slightly younger dikes and

igneous intrusions.

The proposed repository volume lies within a

volume of relatively low-strain rock (the ‘tectonic

lens’) surrounded by zones of high ductile strain

and past brittle deformation.

Substantial geological modeling is being

completed in support of repository design and site

licensure efforts. These models include the

classification of bedrock lithology (Rock Domain),

faults and ductile strain zones (DZ), and surficial

geology.

A discrete fracture network model (DFN) has also

been constructed as a component of the site

descriptive model (SDM) efforts. The GeoDFN

provides a statistical model of rock fracturing for

use in stochastic geomechanical and hydrologic

modeling, as a parameter in the estimation of

available repository volumes, and for the

evaluation of the seismic design safety case.

FFM02

FFM05

FFM04

FFM04

FFM03

FFM03

FFM02

FFM02

FFM04

FFM02

FFM03 AFM100201

AFM001265

AFM001264

AFM001244AFM001243

AFM001097

AFM000054

AFM000053

AFM001098

1630000

1630000

1631000

1631000

1632000

1632000

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±Legend

DZ_PFM_v22_2Dmodel

Forsmark Mapped Outcrops

Preliminary Fracture Domain Model

Fracture Domain

FFM02

FFM03

FFM04

FFM05

Candidate area

Above: Fracture domains within the version 2.2 simulation volume.

Stippled grey lines represent the surface traces of the Deformation Zone

(DZ) model

Fundamental to the version 2.2

revision of the geological DFN is

the concept of ‘fracture

domains’. A fracture domain is a

volume of rock, existing outside

of deformation zones (faults),

where the rock units

encountered during drilling

exhibited similar fracture

intensity patterns. /Olofsson and

others, 2006/.

The goal of casting DFN models

in the context of fracture

domains is to constrain the

spatial variability of fracturing to

relatively homogenous volume

constructs. It also brings the

DFN models in line with other

site geological models, and

allows for the representation of

the DFN in the site 3D volume

models.

Below: 3D view of the modeled fracture domains at Forsmark. FFM02

represents a zone of higher near-surface fracture intensity. FFM03

represents more highly fractured rocks in the hanging wall above several

large, shallow-dipping brittle-ductile structures (deformation zone ZFMA1

and ZFMA2 in the figure below)

0 1 2 3 4 50.5Meters

EOutcrop AFM001098 Linked Fracture Sets

Legend

WNW Strike: (70-130 / 250-310)

NW Strike: (130-180 / 310-360)

NNE Strike: (0-30 / 180-210)

NE Strike: (30-70 / 210-250)

Subhorizontal Fractures (Dip <= 50)

Fractures classified into sets by orientation and termination relationships

Fracture orientation sets are the

fundamental component of the site

geological models. All other model

parameters, such as size or intensity,

are packaged within the context of the

orientation sets.

A key goal of the DFN modeling effort

was to allow for the stochastic spatial

variation of set orientations. It is well-

recognized that, though general

patterns are seen in the outcrop

fracture patterns, there is a level of

spatial variability in the mean

orientations. Fracture set orientations

are modeled as univariate Fisher

spherical probability distributions. Orientation set parameters were calculated

from borehole fracture data and detailed

outcrop fracture maps. Spatial variability in

orientation sets was simulated assuming:

1) The location of the mean pole vector on

the sphere for an orientation set is NOT a

constant. Instead, it was assumed to follow a

second Fisher distribution, with a

concentration parameter (κmp) defined by the

clustering of the mean poles fit to individual

fracture sets.

2) The concentration parameter for each set

(Fisher’s κ) in a stochastic model is taken as

a random draw from a normal distribution.

The Shapiro- Wilk W-test was used to test the

hypothesis of normality.

N

S

EW

FRACTURE SET NAME

ENE [6]

EW [8]

NE [19]

NNE [3]

NS [16]

NW [18]

SH [20]

SH2 [1]

SH3 [2]

Equal Area

Lower Hemisphere

93 Poles

93 Entries

N

S

EW

FRACTURE SET NAME

ENE [6]

EW [8]

NE [19]

NNE [3]

NS [16]

NW [18]

SH [20]

SH2 [1]

SH3 [2]

Equal Area

Lower Hemisphere

93 Poles

93 Entries

Stereonet plot of mean pole vectors for orientation sets fit to

borehole and outcrop data. For a given set, the cluster of mean

poles is then used to estimate a new mean pole and Fisher K

Fracture size is one of the more

difficult parameters of a geological

DFN to characterize. Modelers

have limited access to size data,

and the data sources available are

often limited in scale or are subject

to truncations or sampling artifacts.

At Forsmark, two alternative size

models were carried through the

full parameterization:

1) The ‘tectonic continuum’ models

(TCM/TCMF): These models

assume coupled size-intensity

scaling, using power laws (Pareto

distribution) to simultaneously

describe fracture size at multiple

scales. The TCM/TCMF models

are parameterized using a mixture

of outcrop fractures, quasi-regional

magnetic lineaments (50 – 500 m

scale), and regional deformation

zones (> 1000 m scale).

2) The combined outcrop-scale

and ‘tectonic fault’ models

(OSM+TFM): These models treat

fractures in the DFN as belonging

to one of two populations; outcrop

fractures, which are hypothesized

to primarily be joints, and ‘tectonic

faults’, which are thought to be

shear features. The ‘tectonic fault’

model is cut off at a radius of 28 m;

the joint model has no formal

upper limit. In practice, however,

few joints larger than 100 m show

up in the stochastic models.

Plots representing the size range, in terms of measured trace length in

outcrop, of the OSM+TFM (left) and TCM/TCMF models (right). The

charts plot trace lengths in terms of a cumulative number, normalized

for sampling area. This allows for the direct comparison of data at

different scales

Analysis of data from cored boreholes at Forsmark

suggests that fracture intensity (P32, volumetric

fracture intensity, measured as the total fracture

area divided by the target volume) is a highly

variable parameter, even inside a single fracture

domain. As such, it was necessary to develop

predictive models for fracture intensity to present a

complete model of fracturing at Forsmark.

Statistical modeling, consisting primarily of

multivariate stepwise regression analysis, was used

to test the dependence of fracture intensity on

geological properties such as depth, lithology,

fracture morphology, and degree of host rock

alteration. Though several different factors, such as

measured depth and degree of alteration, showed

positive correlations with fracture intensity, host

lithology was the only truly useful parameter in

prediction. The modeling was performed based on

contiguous segments of nearly constant fracture

intensity (mechanical layers) identified using

cumulative fracture intensity plots.

The intensity model in the version 2.2 Forsmark

geological DFN consists of:

Example output from the multivariate

regressions, illustrating fracture intensity

as a function of lithology.

1) Correlation of fracture size to lithologies. The DFN model accounts for lithologic variation of

fracture intensity through a correction factor relative to the dominant rock type (granodiorite).

2) Fracture intensity (P32) specified as a gamma distribution in space at a specified scale for

individual fracture sets in each fracture domain. This allows modelers using geocellular or finite-

difference based models to more accurately simulate spatial variability

Cumulative Fracture Intensity (CFI) plot, summarizing mechanical layers exposed in cored boreholes. A break in

slope represents a change in the fracture frequency (P10); this generally correlates to a change in lithology or degree

of host rock alteration.

The authors would like to thank members of the Forsmark geological modeling team for their hard

work and support in building the underlying geologic framework that this geological DFN rests upon:

• Raymond Munier, Martin Stigsson, and Isabelle Olofsson - Svensk Kärnbränslehantering AB (SKB)

• Michael Stephens – Swedish Geological Survey (SGU)

http://www.fracman.com or http://www.skb.se

This poster concerns a study which was conducted for SKB. The conclusions and viewpoints presented in the report are

those of the authors and do not necessarily coincide with those of the client.

Isaksson, H., Pitkänen, T., and Thunehed, H., 2006a. Ground magnetic survey and lineament interpretation in an area northwest of

Bolundsfjärden. Forsmark site investigation, report P-06-85, Svensk Kärnbränslehantering AB (SKB), Stockholm, Sweden.

Olofsson, I., Simeonov, A., Stigsson, M., Stephens, M., Follin, S., Nilsson, A-C., Röshoff, K., Lindberg, U., Lanaro, F., Fredriksson, L.,

2007 (In press), A fracture domain concept as a basis for the statistical modeling of fractures and minor deformation zones, and

interdisciplinary coordination. Site descriptive modeling Forsmark, stage 2.2, report R-07-15, Svensk Kärnbränslehantering AB (SKB),

Stockholm, Sweden.

CFI Plot for All Sets

-1200

-1000

-800

-600

-400

-200

0

200

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Cumulative Fracture Intensity (normalized)

Su

bsea E

levati

on

(m

)

KFM01A

KFM01B

KFM01C

KFM01D

KFM02A

KFM03A

KFM03B

KFM04A

KFM05A

KFM06A

KFM06B

KFM06C

KFM07A

KFM07B

KFM07C

KFM08A

KFM08B

KFM08C

KFM09A

KFM09B

KFM10A