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P O S I V A O Y
O l k i l u o t o
F I -27160 EURAJOKI , F INLAND
Te l +358-2-8372 31
Fax +358-2-8372 3709
Hanna Remes
Har r i Kuu la
Pet te r i Somervuor i
Mat t i Haka la
August 2009
Work ing Repor t 2009 -55
ONKALO Rock Mechanics Model (RMM)Version 1.0
August 2009
Base maps: ©National Land Survey, permission 41/MML/09
Working Reports contain information on work in progress
or pending completion.
The conclusions and viewpoints presented in the report
are those of author(s) and do not necessarily
coincide with those of Posiva.
Hanna Remes
Harr i Kuu la
Pet te r i Somervuor i
WSP F in l and L td
Matt i Haka la
KMS Haka la Oy
Work ing Report 2009 -55
ONKALO Rock Mechanics Model (RMM)Version 1.0
ONKALO ROCK MECHANICS MODEL (RMM) - VERSION 1.0 ABSTRACT
The Rock Mechanics Model of the ONKALO rock volume is a description of the
significant features and parameters related to rock mechanics. The main objective is to
develop a tool to predict the rock quality and the potential for stress failure which can
then be used for continuing design of the ONKALO and the repository.
This is the first implementation of the Rock Mechanics Model and it includes sub-
models of the intact rock strength, rock mass spalling strength, in situ stress, potential
for stress failure, seismic velocities, thermal properties, major fracture sets, rock mass
quality and properties of the brittle deformation zones. Because of the varying quantities
of available data for the different parameters, the types of presentations also vary: some
data sets can be presented in the style of a 3D block model but, in other cases, a single
distribution represents the whole rock volume hosting the ONKALO.
Keywords: Rock mechanics model, Block model, Geostatistics, Rock mass quality, Rock strength, In situ stress, Spalling
ONKALON KALLIOMEKAANINEN MALLI (RMM) - VERSIO 1.0 TIIVISTELMÄ
ONKALOn kalliomekaaninen malli on kuvaus kalliomekaanisesti merkittävistä omi-
naisuuksista ja parametreista. Työn tavoitteena on ollut kehittää työkalu, jolla voidaan
arvioida kalliolaatua ja mahdollisia jännitystilasta aiheutuvia vaurioita edelleen hyödyn-
nettäviksi ONKALOn tilojen suunnittelussa.
Tämä on ensimmäinen versio ONKALOn kalliomekaanisesta mallista, jonka osamalleja
ovat: lohkomalli kalliolaadusta, hauraat deformaatiovyöhykkeet ja niiden ominaisuudet,
kiven lujuus, kalliomassan vaurioituminen (spalling), kallion in situ jännitys, kallion
seisminen nopeus ja kallion termiset ominaisuudet. Koska eri parametrien lähtö-
tietomäärät vaihtelevat, eräät parametrit on esitetty 3D-lohkomallimuodossa, kun taas
eräät on esitetty yksinä jakautumina käsittäen koko ONKALOn kalliotilavuuden.
Avainsanat: kalliomekaaninen malli, lohkomalli, geostatistiikka, kalliolaatu, kallion lujuus, kallion in situ jännitys, kallion hilseily.
1
TABLE OF CONTENTS
ABSTRACT
TIIVISTELMÄ
1 INTRODUCTION ................................................................................................. 3
2 INPUT DATA ....................................................................................................... 5
3 ROCK MASS QUALITY ....................................................................................... 7 3.1 Comparing pilot hole data with tunnel mapping data ................................... 9 3.2 Geostatistical analysis of the GSI and RQD rock mass classification indices ..................................................................................................... 13
3.2.1 Basic statistics ............................................................................... 14 3.2.2 Variogram modelling ..................................................................... 15
4 ROCK MECHANICS PROPERTIES OF MAJOR BRITTLE DEFORMATION ZONES (BDZS) .................................................................................................. 21
5 ROCK STRENGTH ............................................................................................ 25 5.1 Intact rock strength ................................................................................... 25 5.2 In Situ Spalling Strength ........................................................................... 26
6 IN SITU ROCK STRESS ................................................................................... 27
7 THERMAL PROPERTIES .................................................................................. 29
8 BLOCK MODEL ................................................................................................. 31 8.1 Block model dimensions and estimation method ...................................... 31 8.2 Block model results................................................................................... 33 8.3 Uncertainties in the RMM.......................................................................... 38
9 GEOPHYSICAL DATA....................................................................................... 41
10 SPALLING PREDICTION .................................................................................. 43 10.1 Spalling prediction for the ONKALO: Chainages 3117 - 4340 m ............... 43
11 FUTHER DEVELOPMENT AND PRELIMINARY IDEAS FOR VERSION 2 OF THE ROCK MECHANICS MODEL ..................................................................... 45
REFERENCES ........................................................................................................... 47
LIST OF APPENDICES .............................................................................................. 49
APPENDIX 1 .............................................................................................................. 51
APPENDIX 2 .............................................................................................................. 53
APPENDIX 3 .............................................................................................................. 57
APPENDIX 4 ............................................................................................................ 103
2
3
1 INTRODUCTION
The Rock Mechanics Model (RMM) is a description of significant features and
parameters related to rock mechanics. The main objective is to develop a tool to predict
rock quality and the potential for stress failure which can be used for the design of
ONKALO and repository facilities. This report provides an overview of the progress of
the RMM project.
The RMM is based on the current Geological Model (Mattila & al. 2007), i.e. no new
interpretation of the main geological features has been attempted. The adopted features
are the rock type domains, brittle deformation zones and the general orientation of
foliation. Other input data consist of the rock mass classification data from drillholes,
pilot holes and tunnels, rock mechanics laboratory and field test data, and geophysical
velocity and density data. The geometry is modelled using the Gemcom Surpac®
software and all data are stored in an Access database. Surpac supplies an interface for
the database and it is used as an interpretation and visualization tool.
Based on the input data, sub-models of intact rock strength, rock mass spalling strength,
in situ stress, stress failure, seismic velocities, thermal properties, major fracture sets,
rock mass quality, and the quality of the brittle deformation zones are made. Because of
the amount of available data and the nature of the parameter in question, the different
sub-models vary: some models can be presented in the style of a 3D block model; in
other cases one distribution represents the whole ONKALO volume.
The function of the model has also changed during the working period. In the beginning
of the project, the rock mass quality and the rock mass strength were considered as key
parameters but, during the development of the project, it transpired that the in situ
stress, spalling strength of the rock and the foliation are the most critical parameters. In
addition, the long fractures and brittle deformation zones are important and these can be
adopted from the geological model.
The phases in the RMM development project were
- Selection and evaluation of input data,
- Preparation of input data
- Geostatistical analysis of rock mass quality
- Block modelling
- Evaluation of block model results
- Spalling strength of rock, in situ state of stress
This is the first phase of the RMM development. In the next version, focus will be
placed on the prediction of stress damage/failure.
4
5
2 INPUT DATA
The geometry of the RMM is based on the current geological model. The adopted
geological features in the RMM 1.0 are the major brittle deformation zones. The
lithological units are included in the RMM but they are not used directly as rock
mechanics domains because the correlation between the rock mechanics data and the
rock types is not clear.
The input data for rock mass classification and the general orientation of foliation are
from surface drillholes (OL-KR1 – OL-KR40), pilot holes (OL-PH1 and ONK-PH2 –
ONK-PH8) and ONKALO tunnel mapping. These data include rock mass quality
indices (GSI, Q ) and the constituent Q parameters: RQD, Jn, Jr, and Ja. The tunnel
mapping was undertaken in 5 m or longer increments and the resultant data are
considered as the most reliable data for the rock mass quality. These tunnel mapping
data were available for this RMM 1.0 from the beginning of the ONKALO to chainage
3116 m.
Data from the surface drillholes were available from OL-KR1 – OL-KR40, representing
a total of 19840 m of logged core. The lengths of the logged sections in the surface
drillholes and ONKALO pilot holes varies from 0.05 m to 47.62 m, the average
sectional length being 5.9 m. Data from the pilot holes (OL-PH1 and ONK-PH2 –
ONK-PH7) are used only in the statistical studies to establish if the correlation factor
between drillhole data and tunnel mapping data could be determined. In the block model
estimation, data from OL-PH1 and ONK-PH2 – ONK-PH7 are not used because the
tunnel mapping data are available from the same locations.
These input data are stored in the RMM work database. RMM work database include
same data as in Posiva investigation database and some modified data tables. In
modified data tables brittle deformation zone intersections have been were filtered from
the data.
The intact rock strength data consist of point load tests from drillholes OL-KR1 – OL-
KR39 and laboratory test data from drillholes OL-KR1, OL-KR2, OL-KR4, OL-KR5,
OL-KR10, OL-KR12, OL-KR14 and OL-KR24.
The in situ stress field is taken directly from the interpretation as presented in Posiva’s
Site Report 2008 (Posiva 2009).
The thermal property data for the rocks at Olkiluoto consist of laboratory measurements
of drill cores (thermal conductivity, specific heat and density).
data from drillholes OL-KR1, OL-KR2, OL-KR4, OL-KR9, OL-KR11,
calculated thermal conductivity and specific heat from modal analysis of mineral
composition (OL-KR1, OL-KR2),
anisotropy of thermal conductivity from drill core samples (OL-KR1, OL-KR2),
thermal expansion properties of the Olkiluoto migmatitic gneiss estimated from mineral
composition (average) and in situ TERO measurements of thermal conductivity and
diffusivity (OL-KR2, and OL-KR14).
6
The thermal property model is built from interpretation presented in Posiva‟s Site
Report 2008.
Geophysical drillhole logging data include density as well as P- and S-wave velocity
data from drillholes OL-KR1 – OL-KR4, OL-KR6 – OL-KR20, OL-KR22 – OL-KR48,
OL-PH1, ONK-PH2 – ONK -PH4, and ONK- PH6 – ONK-PH9 (Öhman et al. 2008).
Petrophysical sample data of the P-velocity and density are available from OL-KR1 to
OL-KR39, OL-PH1 and ONK-PH2 – ONK-PH07 (Öhman et al. 2009). The VSP
reflection seismic data were compiled to tomographic P- and S-wave velocity models
from drillholes OL-KR1, OL-KR2, OL-KR4, OL-KR8, OL-KR10, OL-KR14, OL-
KR27 and OL-KR38 (Appendix 3). The correlation of this large scale seismic velocity
model was reviewed (Appendix 4) against more detailed drillhole and sample data, and
against lithological, alteration and deformation intersections in these drillholes
(Paulamäki et al. 2006).
7
3 ROCK MASS QUALITY
Rock mass quality is determined by using Q-classification (Barton et al. 1974; Grimstad
& Barton 1993) in the ONKALO access tunnel. Q- and Q´-value is calculated with the
following equations ((Barton et al. 1974; Grimstad & Barton 1993):
andSRF
J
J
J
J
RQDQ w
a
r
n
(3-1)
a
r
n J
J
J
RQDQ´ (3-2)
Where RQD = rock quality designation,
Jn = joint set number
Jr = joint roughness number
Ja = joint alteration number
Jw = joint water reduction number
SRF = stress reduction factor
The Geological Strenght Index (GSI), introduced by Hoek (1994) and Hoek, Kaiser and
Bawden (1995) provides a number which, when combined with the intact rock
properties, can be used for estimating the reduction in rock mass strength for different
geological conditions. This system for blocky rock masses is presented in Figure 3-1.
GSI can be estimated from Q‟-value with following equation (Hoek et al. 1995)
44)'ln(9 QGSI (3-3)
The GSI value varies between 0-100, 0 indicating bad and 100 indicating excellent rock
quality. When using equation 3-3 GSI may get values over 100 in exceptional good
rock.
8
Figure 3-1. General chart for GSI estimates from the geological observations (Hoek & Marinos, 2000)
9
3.1 Comparing pilot hole data with tunnel mapping data
The differences between drill core data and tunnel mapping data were studied to
determine whether the drill core data could be adjusted to be compatible with the tunnel
mapping data. This was done by comparing data from pilot holes (OL-PH1, ONK-PH2
to ONK-PH7) with the tunnel mapping data. For tunnel mapping data, the median
values of the mapped section for each parameter are used in comparison. The data from
ONK-PH8 were not used in the comparison because the tunnel mapping data in the
ONK-PH8 vicinity were not yet available.
The tunnel mapping data and pilot hole data were first compared using the original
logging sections (Figure 3-2). In this comparison, it is clearly seen that the observed
GSI-values vary more in the pilot holes. This is because the drill core logging sections
are defined based on changes of rock type, fracture frequency, deformation zones, etc,
and tunnel mapping is undertaken in 5 m or longer sections.
40
50
60
70
80
90
100
110
980 1030 1080 1130 1180
chainage
GS
I O
rig
ina
l
ONK-VT1
ONK-PH5
Figure 3-2. Example of the comparison of GSI-values from the pilot holes with the tunnel mapping values based on the original logging sections. The orange area represents the deformation zone intersection (ONK19C) observed in the tunnel; the grey area represents deformation zone intersection (ONK103) observed in ONK-PH5.
A second comparison was achieved by allocating the data from the pilot holes to
sections which were commensurate with the tunnel mapping, so that there would be a
more 1:1 sectional comparison. Parameters for rock mass quality (RQD, Jr and Ja) were
recalculated from the fracture logging data. Before recalculating the parameters,
fractures with high Jr and low Ja number (i.e. Jr = 3 or higher and Ja = 1 or lower) were
removed from the drillhole data, because these fractures are likely to be short ones. In
the newly constituted sections, the originally mapped values for the parameter Jn were
used. If two or more Jn values were originally logged in the newly constituted sections,
the Jn value from the longest section was used.
The comparisons between the ONK-PH5 and tunnel mapping results are presented in
Figures 3-3 to 3-7 as examples.
10
40
50
60
70
80
90
100
110
980 1030 1080 1130 1180chainage
GS
I
ONK-VT1
ONK-PH5
Figure 3-3. GSI values from ONK-PH5 and tunnel mapping. The data in ONK-PH5 have been reconstituted so that the ONK-PH5 sections match the tunnel mapping sections. The orange area represents the deformation zone intersection (ONK19C) observed in the tunnel; the grey area represents the deformation zone intersection (ONK103) observed in ONK-PH5.
70
75
80
85
90
95
100
980 1030 1080 1130 1180chainage
RQ
D
ONK-VT1
ONK-PH5
Figure 3-4. RQD values from ONK-PH5 and tunnel mapping. The data in ONK-PH5 have been reconstituted so that the ONK-PH5 sections match the tunnel mapping sections. The orange area represents the deformation zone intersection (ONK19C) observed in the tunnel; the grey area represent the deformation zone intersection (ONK103) observed in ONK-PH5.
11
0
1
2
3
4
5
6
7
8
9
980 1030 1080 1130 1180
chainage
Jn
ONK-VT1
ONK-PH5
Figure 3-5. Joint set number values from ONK-PH5 and tunnel mapping. The data in ONK-PH5 have been reconstituted so that the ONK-PH5 sections match the tunnel mapping sections. The orange area represents the deformation zone intersection (ONK19C) observed in the tunnel; the grey area represent the deformation zone intersection (ONK103) observed in ONK-PH5.
0
1
2
3
4
5
6
7
980 1030 1080 1130 1180Chainage
Ja
ONK-VT1
ONK-PH5
Figure 3-6. Joint alternation number values from ONK-PH5 and tunnel mapping. The data in ONK-PH5 have been reconstituted so that the ONK-PH5 sections match the tunnel mapping sections. The orange area represents the deformation zone intersection (ONK19C) observed in the tunnel; the grey area represent the deformation zone intersection (ONK103) observed in ONK-PH5.
12
0
1
2
3
4
5
980 1030 1080 1130 1180Chainage
Jr
ONK-VT1
ONK-PH5
Figure 3-7. Joint roughness number values from ONK-PH5 and tunnel mapping. The data in ONK-PH5 have been reconstituted so that the ONK-PH5 sections match the tunnel mapping sections. The orange area represents the deformation zone intersection (ONK19C) observed in the tunnel; the grey area represent the deformation zone intersection (ONK103) observed in ONK-PH5.
In Figure 3-8, the differences in the GSI values between the tunnel mapping and PH
core logging data are presented. 50 % of the data values exhibit a difference of around
seven GSI units and 25 % of the data a difference of 12 units.
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
Number of samples
GS
I T
un
ne
l -
GS
I P
H
25%50%
75%
Figure 3-8. Differences in GSI values between tunnel mapping data and pilot holes (One Q class corresponds to 8-12 units in the GSI value).
13
It is not possible to say which measurement type, tunnel mapping or drill core logging,
providers a better estimate of rock mass quality, although the variation in rock mass
quality measured from the drill cores is higher. Of the compared parameters, the RQD
correlates fairly well with the tunnel mapping data; however, the joint alteration number
(Ja) and joint roughness number (Jr) do not correlate as well.
Because no single trend as found, it is not possible to establish any „correction‟ factors
to adjust the drillhole data to match the tunnel mapping data. Nevertheless, it can be
stated as an overall conclusion that the rock mass quality from drill cores varies as ± one
rock mass quality class (Q class) compared to the tunnel mapping data.
3.2 Geostatistical analysis of the GSI and RQD rock mass classification indices
Geostatistical analyses are used to determine interpolation/extrapolation distances for
the RMM input data. Variograms were also used to determine search ellipsoid
parameters used in the block model calculation.
Statistical and geostatistical analyses for the rock mass quality, GSI and RQD indices
were made using 1 m composites. In compositing original samples, which may vary in
size and length, are divided/combined so that each composite sample represents equal
length of drillhole. The available data from tunnel mapping, surface drillholes and
ONK-PH8 were used. Before calculating the composites, brittle deformation zone
intersections were filtered from the data (Figure 3-9). This filtering prevents low GSI-
values observed in the brittle deformation zones from spreading out the results. Filtering
was done using geological interpretations of the deformation zone intersections.
Figure 3-9. The ONKALO volume and two of the major brittle deformation zones (OL-BFZ 018 and OL-BFZ080. Brittle deformation zone intersections were filtered from the logging data before the composites were calculated.
14
3.2.1 Basic statistics
To provide an overview of the available logging data, a basic statistical study was made
of the GSI and RQD values from tunnel mapping, surface drillholes and ONK-PH8
(Figure 3-10). Unlike GSI data from the tunnel, the drillhole data exhibit a bimodal
distribution. In terms of the RQD values, no major differences exist (Figure 3-11). The
data statistics are presented in Table 3-1.
Histogram GSI values
0
1000
2000
3000
4000
5000
6000
40 45 50 55 60 65 70 75 80 85 90 95 100 More
GSI
Nu
mb
er
Of
Sa
mp
les
0 %
20 %
40 %
60 %
80 %
100 %
120 %
Cu
mu
lati
ve
%
All data
Drill hole data
Tunnel data
Cumulative, all data
Cumulative, drill hole data
Cumulative, tunnel data
Figure 3-10. Histogram of GSI values from surface drillholes, ONK-PH8 and tunnel mapping data between chainage 0-3100.
Histogram RQD values
1
10
100
1000
10000
100000
10 20 30 40 50 60 70 80 90 100
RQD
Nu
mb
er
Of
Sa
mp
les
0 %
20 %
40 %
60 %
80 %
100 %
120 %
Cu
mu
lati
ve
%
All data
Drillhole data
Tunnel data
Cumulative, all data
Cumulative, drill hole data
Cumulative, tunnel data
Figure 3-11. Histogram of RQD values from surface drillholes, ONK-PH8 and tunnel mapping data between chainage 0-3116.
15
Table 3-1. Statistics of GSI and RQD input data.
GSI RQD
All Tunnel Drillholes
(surface + PH8)
All Tunnel Drillholes
(surface + PH8)
No of samples 22,783 3,189 19,594 22,773 3,179 19,594
Min 34 47 34 10 50 10
Max 109* 106* 109* 100 100 100
Mean 83.82 85.62 83.53 96.10 97.85 95.83
Median 79.21 85.45 79.00 99.34 100 99.14
Std deviation 13.20 13.15 13.19 9.88 5.58 10.39
* GSI value calculated from equation 3-3.
3.2.2 Variogram modelling
The variogram used in geostatistics is a graphical tool which can be used to describe
spatial continuity of data (Isaaks & Srivastava 1989). It is a measure of variability and
its value increases when samples become more dissimilar.
In mathematical terms, the variogram for lag distance, h, is defined as the mean squared
difference of values separated by distance h:
)(2
))()(()(
2
hN
hxyxyh , (3-4)
where h represents a distance vector between two spatial locations and N(h) is the
number of pairs for the lag distance h.
The variogram range is the maximum distance at which there is some correlation
between the values of a parameter at two points, e.g. the distance from a drillhole at
which the drillhole information no longer provides any information. In other words,
there is no statistical relation for values located farther apart than the range. The
variogram plateau at the range is termed the sill (Figure 3-12).
16
Figure 3-12.Characteristics of the Variogram.
Variograms were made using data from the surface drillholes and tunnel mapping both
together and separately. An omni-directional variogram, where data pairs are selected
based only on their separation distance and not their direction, is presented in Figure 3-
13. Although there is no clear plateau in this variogram, the effective range is about 30
m. Thus, on the average and for the measurements being considered here, knowledge of
the rock mechanics parameters at a given point cannot be reliably extrapolated for a
distance of more than 30 m.
Figure 3-13. Omni-directional variogram of GSI values for tunnel mapping and surface drillhole data, lag 3 m, the green line represents the variance of all the data.
17
In directional sample variograms, it is often observed that the range and sill change as
the direction of sampling changes. Typical observed variogram behaviours can be
trends, fluctuations, and anisotropic dependence. The type of anisotropy where the
range changes while the sill remains constant is known as geometric anisotropy. A case
where the sill changes with direction, while the range remains constant is called zonal
anisotropy. (Isaaks & Srivastava 1989). Examples of oriented variogram behaviour for
three different two-dimensional geological structures are presented in Figure 3-14.
Figure 3-14. Three different geological images with the corresponding directional variograms. Note the fluctuations, trends, geometric anisotropy and zonal anisotropy. The red lines represents horizontal sample variograms and the blue lines represents vertical sample variograms; the black horizontal line represents the variance of the whole model data. (Gringarten & Deutsch 1999).
18
The factor of anisotropy is significant for the rocks at Olkiluoto because they are
foliated, causing different mechanical properties to be exhibited parallel and
perpendicular to the foliation, and at the various orientations between these two end
cases. To study this anisotropy variograms were produced from data in the horizontal
plane and in the plane of foliation. For the overall foliation orientation, a dip of 32º and
dip direction of 138º were used (Mattila & al WR-92-2007), based on stereograms of
the foliation directions which indicate the average foliation orientations within the block
model area. In the plane of foliation, correlation is better compared to the direction
perpendicular to the foliation (Figure 3-15). In plane of foliation no clear anisotropy
direction, where range would be greater, were obtained.
Figure 3-15. Variograms of the GSI values in the direction of foliation (top) and perpendicular to the foliation (bottom). The red lines represent the best fit variogram models in the direction 32/138. Tunnel mapping and surface drillhole data; lag 7 m; the green lines represent the variance of all data.
19
The top variogram in Figure 3-15, representing values in the direction of foliation,
provides better correlation as the distance between samples increases; whereas, the
variogram representing distances perpendicular to the foliation, the lower variogram in
Figure 3-15 bottom graph increases more quickly to the average variance, providing less
correlation as the distances between samples increases. The predictability is good for an
interpolation/extrapolation distance of 10 m or less in all directions. With an
interpolation/extrapolation distance of 30 m, predictability is good in the direction of
foliation but not in the direction perpendicular to the foliation.
20
21
4 ROCK MECHANICS PROPERTIES OF MAJOR BRITTLE DEFORMATION ZONES (BDZs)
In the RMM, brittle deformation zones (BDZs) are visualized and presented as 3D
planes adopted directly from the Geological model (Figure 4-1).
Figure 4-1. 3D view of the ONKALO with the major brittle deformation zones coloured based on their GSI-values.
Estimation of the mechanical properties of the BDZs is based on data from the Olkiluoto area drillholes and the ONKALO tunnel mapping. For this report, ten fracture zones have been analysed. Identification of the location and size of the zones is described in Kemppainen et al. (2007). As described in that report, each zone has been checked and described from those drillholes penetrating the zone being considered. All the intersection points are connected to each other using geophysical and hydrogeological information and, from those points, a 3D plane (to the upper and lower boundaries of the BDZ) is created using the Gemcom Surpac® program. Units have been modelled to date from the information in Mattila et al. 2007, Kemppainen et al. 2007, and Posiva 2007.
22
Figure 4-2. Method of sub-dividing the brittle deformation zone as used by SKB and for the numerical modelling in Glamheden et al. 2007.
BDZs can be divided into the core part and transition zones, here the transitional and core components (Figure 4-2). Seven BDZs intersect the ONKALO tunnel in the 0 -2400 m tunnel chainage range. From three of these zones (BFZ18, BFZ43 and BFZ100) core zone and transitional zones have been mapped. From zone, BFZ15, only the core has been mapped. From zones BFZ11, BFZ101 and BFZ118, only the logged Q´ median value for a 5 m long chainage increment is available (Table 4-1).
23
Table 4-1. Summary of the brittle deformation zones intersections.
Name of Brittle
Deformation ZoneOther name
Intersects ONKALO
tunnel at 0-2400
chainages
Mapped pre, core
and post zones
Intersections in drill
holes (number)
OL-BFZ11, OL-BFZ18,
OL-BFZ51ONK19A, HZ19C x - 22
OL-BFZ18, OL-BFZ60 ONK19C, HZ19C x x 20
OL-BFZ19, OL-BFZ21,
OL-BFZ98ONK20A, HZ20A - - 19
OL-BFZ98, OL-BFZ80,
OL-BFZ22ONK20B, HZ20B - - 17
OL-BFZ43 ONK43 x x 1
OL-BFZ10, OL-BFZ20,
OL-BFZ77, OL-BFZ84ONK56 - - 9
OL-BFZ100 ONK100 x x 6
OL-BFZ101 ONK101 x - 1 (pilot hole)
OL-BFZ15 ONK 103 x only core mapped 9
OL-BFZ118 ONK110 x - 1 (pilot hole)
The GSI values of the BDZs listed in Table 4-2 are based on either drill core logging or
tunnel mapping. In cases where the core has been established, the GSI value for the
BDZ is the value for the zone core. In cases where the GSI value (calculated from the
Q´ value) from tunnel mapping data is an average value for the mapped chainage, the
drillhole data are used. BDZs which do not intersect the tunnel are classified using the
drill hole logging information.
In the drillhole intersections, the GSI value is a lower quartile value of the mapped data.
Both approaches are conservative because the widths of the modelled zone are much
wider than the intersections. However, at this stage for rock mechanics modelling
purposes, it was decided to characterize the BDZs by the value of the weakest region
existing in the zone.
24
Table 4-2. Strength and deformability properties of brittle deformation zones.
OL-BFZ11 OL-BFZ18 OL-BFZ19 OL-BFZ22 OL-BFZ43 OL-BFZ10 OL-BFZ100 OL-BFZ101 OL-BFZ15 OL-BFZ118
OL-BFZ18 OL-BFZ60 OL-BFZ21 OL-BFZ80 OL-BFZ20
OL-BFZ51 OL-BFZ98 OL-BFZ98 OL-BFZ77
OL-BFZ84
(ONK19A) (ONK19C) (ONK20A) (ONK20B) (ONK43) (ONK56) (ONK100) (ONK101) (ONK103) (ONK110)
Width of fault zone core (m) - 0.2 - - 0.15 - 0.8 - 5.0 -
Hoek Brown Classification
GSI 53 64 53 54 61 51 41 44 71 64
1st quartile
of drill core
intersections
mapped core
value from
tunnel
intersection
1st quartile
of drill core
intersections
1st quartile
of drill core
intersections
mapped core
value from
tunnel
intersection
1st quartile
of drill core
intersections
mapped core
value from
tunnel
intersection
mapped from
one drill core
intersection
1st quartile
of drill core
intersections
mapped from
one drill core
intersection
sigci (MPa) 22 22 22 22 22 22 22 22 22 22
mi 9.99 9.99 9.99 9.99 9.99 9.99 9.99 9.99 9.99 9.99
D 0 0 0 0 0 0 0 0 0 0
Ei (GPa) 63 63 63 63 63 63 63 63 63 63
Hoek Brown Criterion
mb 1.86 2.76 1.86 1.93 2.48 1.74 1.21 1.35 3.55 2.76
s 0.0054 0.0183 0.0054 0.0060 0.0131 0.0043 0.0014 0.0020 0.0399 0.0183
a 0.50 0.50 0.50 0.50 0.50 0.51 0.51 0.51 0.50 0.50
Failure Envelope Range
Application Custom Custom Custom Custom Custom Custom Custom Custom Custom Custom
sig3max (MPa) 28.5 28.5 28.5 28.5 28.5 28.5 28.5 28.5 28.5 28.5
Mohr-Coulomb Fit
cohesion (MPa) 3.2 3.8 3.2 3.3 3.7 3.1 2.7 2.8 4.2 3.8
friction angle (°) 19.0 21.7 19.0 19.2 20.9 18.6 16.4 17.0 23.4 21.7
Rock Mass Parameters
sigt (MPa) -0.06 -0.15 -0.06 -0.07 -0.12 -0.05 -0.03 -0.03 -0.25 -0.15
sigc (MPa) 1.6 3.0 1.6 1.7 2.5 1.4 0.8 0.9 4.4 3.0
sigcm (MPa) 4.1 5.3 4.1 4.2 4.9 3.9 3.1 3.4 6.4 5.3
Erm (GPa) 23.1 38.4 23.1 24.4 34.2 20.5 10.8 13.2 47.3 38.4
Kn = E / width (GPa/m) * - 192.1 - - 227.9 - 13.5 - 9.5 -
G = E / 2 (1+n), n = 0.25 (GPa) - 15.4 - - 13.7 - 4.3 - 18.9 -
Ks = G / width (GPa/m) - 76.9 - - 91.2 - 5.4 - 3.8 -
*) Width of the zone core varies in the drill core intersections. That is why the stiffness parameters has not been determined.
25
5 ROCK STRENGTH
5.1 Intact rock strength
The Site Report 2008 (Posiva 2009) presents the distribution for the critical stress states
of the metamorphic rock types encountered at Olkiluoto (Figure 5-1). For the igneous
rock types, mainly pegmatite, the same critical stress states can be considered to apply
in compression, but the tensile strength is less.
Figure 5-1. Cumulative probability distributions for the normalised critical stress states for the ONKALO area metamorphic rock types.
0.0 %
12.5 %
25.0 %
37.5 %
50.0 %
62.5 %
75.0 %
87.5 %
100.0 %
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00
Normalized strength
Cu
mu
lati
ve
pro
ba
bil
ity
Peak strengthmean=115 MPa, n=77
Crack damage stressmean=99 MPa, n=82
Crack initiation stressmean=52 MPa, n=83
Point load Indexmean=118 MPa, n=972
Indirect tensile strengthmean=12 MPa, n=53
26
For the RMM, the information in Figure 5-1 is represented by a triangular distribution
between the measured cumulative probabilities of 5%, 50% and 95% (Table 5-1). In this
first version of the RMM, this strength distribution is applied for all rock types, and no
spatial variation is assumed.
Table 5-1. Triangular distribution for the critical compressive stress states for the ONKALO volume rock.
lower limit mean upper limit
Uniaxial compressive strength (MPa) 81 115 157
Crack damage stress (MPa) 64 98 149
Crack initiation stress (MPa) 36 50 69
Indirect tensile strength (MPa) 6.7 11.9 16.8
5.2 In Situ Spalling Strength
For the in situ spalling strength, a reduction factor of 0.57 is applied to the uniaxial
compressive strength of the intact rock (Table 5-2). The value of 0.57 comes from in
situ testexperiments executed in Äspö and URL (Martin & Christiansson 2008). No
spatial variation is assumed.
Table 5-2. Triangular distribution for the in situ spalling strength of the ONKALO volume rock types (values are 0.57 of the values in Table 5-1).
lower limit mean upper limit
In situ spalling strength (MPa) 46 65 90
27
6 IN SITU ROCK STRESS
The Site Report 2008 (Posiva 2009) presents values for the ONKALO volume in situ
state of stress in terms of the values of the three principal stresses as a function of depth
(Table 6-1). Similar to the characterization of the variability in rock strength presented
in Section 5, in the RMM and spalling analyses, a triangular representation is used for
the rock stress between the effective lower limit, mean and upper limit, i.e. the
cumulative probability for the lower limit is 0 %, 50 % for the mean value and 100 %
for the upper limit.
Table 6-1. Estimated mean, and lower and upper limits for the horizontal and vertical stress components at the Olkiluoto site for two stress domains corresponding to 0 to 300 m and 300 to 900 m vertical depth (Posiva 2009).
Range Stress component Vertical depth range [m]
H [MPa]
Mean zH 042.010
0 < z < 300 m
Lower limit zH 042.06
Upper limit zH 042.014
Orientation [°]
(mean; lower–upper) 10 (0 –20)
Mean zH 030.06.13
300 < z < 900 m
Lower limit zH 030.06.7
Upper limit zH 030.06.19
Orientation [°]
(mean; lower–upper) 90 (70 –110)
h [MPa]
Mean zh 0265.06
0 < z < 300 m Lower limit zh 0265.03
Upper limit zh 0265.09
Mean zh 015.045.9
300 < z < 900 m Lower limit zh 015.045.5
Upper limit zh 015.045.13
v [MPa]
Mean zv 0265.0
0 < z < 900 m Lower limit zv 0239.0
Upper limit zv 0292.0
z = depth below ground surface [m]
28
29
7 THERMAL PROPERTIES
The thermal properties investigated at Olkiluoto include thermal conductivity, specific
heat capacity, thermal diffusivity, density and thermal expansion coefficient. Site
Report 2008 presents the information for the Olkiluoto rocks. The samples used in the
laboratory measurements were mainly of veined gneiss, but also included granodiorite,
granite and pegmatite. In the RMM, the thermal properties of the veined gneiss are
assigned to the whole model.
As stated in the Site Report 2008 (Posiva 2009), the thermal expansion property of the
Olkiluoto migmatitic gneiss was studied with theoretical estimators, using the weighted
arithmetic mean of the mineral properties from literature data, as well as from numerical
modelling (Huotari & Kukkonen 2004). In the migmatitic gneiss, quartz and biotite
have the highest thermal expansion values. Theoretical estimations suggest linear
thermal expansion coefficient values in the range of 7 – 10 ·10-6
C-1
at 20-60º C, in
agreement with published data for similar rock types. For the RMM, a conservative
assumption of 9.5 10-6
C-1
is used.
The temperature-corrected mean thermal conductivity and diffusivity data for Olkiluoto
veined gneiss are shown in Table 7-1.
Table 7-1. Temperature-corrected drillhole means and standard deviations for the thermal properties of the Olkiluoto veined gneiss. The number of samples is shown in parentheses after the conductivity value at 22 ºC. Posiva 2009.
OL-KR1 OL-KR2 OL-KR4 OL-KR9 OL-KR11 All samples
Conductivity (W m-1
K-1
)
22ºC 2.84±0.84 (11) 3.09±0.52(135)1
2.83± 0.40 (8) 2.78±0.47 (8) 2.32±0.35 (8) 3.01±0.53 (170)
60ºC 2.74 2.86 2.64 2.68 2.24 2.78
98.7ºC 2.62 2.57 2.61 2.57 2.14 2.50
Specific heat
capacity (J kg-1
K-1
)
22ºC 738 732 730 746 738 737
60ºC 786 779 777 794 785 784
98.7ºC 834 ±26 827±15 825±12 842±15 833±16 832±19
Diffusivity (10-6
m2 s
-1)
Uncorrected 1.24±0.22 1.37±0.241
1.25±0.18 1.20±0.22 1.02±0.16 1.37±0.24
22ºC 1.40 1.56 1.42 1.35 1.14 1.50
60ºC 1.27 1.36 1.24 1.22 1.04 1.31
98.7ºC 1.14 1.15 1.16 1.11 0.93 1.11
Density (kg m-3) 2748±46 2707±51 2735± 26 2756±26 2753±21 2715±51
1) Includes samples in all measured directions; averages in direction of hole axis are 2.85 ± 0.81 W m-1
K-1
and 1.26 ± 0.19 10-6
m2 s
-1
30
31
8 BLOCK MODEL
8.1 Block model dimensions and estimation method
A block model covering the ONKALO volume has been developed (Figure 8-1). The
dimensions of the model are:
Y (m) X (m) Z (m)
Min co-ordinates 6791750 1525150 -600
Max co-ordinates 6792500 1526500 20
The basic block size is 10 x 10 x 10 m and the sub-block size is 2.5 x 2.5 x 2.5 m.
Figure 8-1. Location of the block model dimensions showing the ONKALO ramp and the drillholes from which the GSI and RQD input data estimations were made.
The GSI and RQD values in the block model were estimated using an inverse distance
with a power of 2 as the calculation method. Estimated values are based on the weighted
values of data points closest to the each block centroid. The weighting is the inverse of
distance of the data point from the block centroid raised to a power of two. No
lithological boundaries were used when estimating the block values.
The block model was calculated using 1 m composites determined from the surface
drillholes and tunnel mapping data. To avoid the lower RQD and GSI values from the
BDZ intersection zones for „spreading‟ to the surrounding rock mass, the BDZ
intersections were filtered from the data before calculating the values for the
composites. As a consequence, the estimated GSI and RQD values in the block model
represent the rock mass between the BDZs.
Calculations were made in three steps with different search parameters to classify the
level of confidence. In the first two steps, a spherical search was used when calculating
32
the block values: the search radius in step one (class 1) was 10 m; and, in step two (class
2), it was 30 m. The minimum number of samples selected in steps one and two was 3,
and the maximum was 15. In the third step (class 3), all blocks which were not allocated
an estimated value in the first two steps were assigned a value. A search ellipsoid
oriented in the plane of foliation (32º/138º) was used. The search radius in the foliation
plane was set to infinity to make sure all the blocks would be estimated. In the direction
perpendicular to the plane of foliation, the maximum search distance was limited to 30
m. The maximum number of samples was increased to 50.
Attributes for in situ stress, intact rock strength and spalling strength were added to the
block model according to the data described in previous sections of this report. The
depth below ground surface, which is used when calculating the in situ stress
component, is calculated from the block centroid. Thermal properties (conductivity,
specific heat capacity and diffusivity) are added to the block model. Lithological units
were assigned to the blocks using 3D solid models from the lithology model. A
summary of the main attributes in the block model is presented in Table 8-1.
Table 8-1. Main attributes in the RM block model.
Attribute name Description
Block_depth Depth from ground level to block centroid
Classification GSI and RQD estimation class
Crack_damage, mean Mean crack damage stress for ONKALO area rock types
Crack_initiation, mean Mean crack initiation stress for ONKALO area rock types
Indirect_tensile, mean Mean indirect tensile strength for ONKALO area rock
types
Rock type Rock type
RQD Estimated RQD value
Sigma_hmax,_mean σH, mean horizontal stress component at Olkiluoto site
Sigma_hmax,_bearing Bearing for σH, mean horizontal stress component at
Olkiluoto site
Sigma_hmin,_mean σh, mean horizontal stress component at Olkiluoto site
Sigma_v,_mean σv, mean vertical stress component at Olkiluoto site
Uniaxial_compressive
strength, mean
Mean uniaxial compressive strength for ONKALO area
rock types
Spalling_strength Mean spalling strength for ONKALO area rock types
Conductivity_22 Conductivity at 22 ºC
Heat_capacity_22 Specific heat capacity at 22 ºC
Diffusivity_22 Diffusivity at 22 ºC
Th_exp Thermal expansion coefficient
33
8.2 Block model results
The estimated GSI values are presented in Figures 8-2 to 8-4. Because the widths of the
major BDZs are small compared to the block size, the BDZs are presented as planes in
the model (see Section 4). The estimated GSI values vary from 50 to 110 (see Section 3)
which correspond to rock mass qualities of Poor to Exceptionally Good in the Q´
classification. Inside the tunnel blocks after about depth Z = -100 m (chainage 1100 m),
the rock mass quality (GSI value) increases. The mean GSI value inside the tunnel and
above Z = -100 is 73 (Good). Below Z = -100, the mean GSI value is 86 (extremely
Good) (Figure 8-2 and Figure 8-3). The mean RQD value inside the tunnel and above Z
= -100 is 93.8%. Below Z = -100, the mean RQD value is 97.6% (Figure 8-5 and Figure
8-6). The rock types and mean in situ stress component magnitudes are visualized in
Figures 8-7 to 8-11. In Appendix 1 and 2 more examples of the RMM visualization are
presented.
Figure 8-2. Block model with estimated GSI values inside the ONKALO tunnel.
50
70
90
110
-450-400-350-300-250-200-150-100-500
Z
GS
I
Figure 8-3. Estimated GSI values inside the ONKALO tunnel.
34
The data presentation in Figures 8-2 and 8-3 show a clear cut trend in the rock quality –
as indicated by the GSI rock mass classification index: for depths of 0-125 m, the GSI
values are generally between 60 and 90; below 125 m, the GSI values are above 75.
This indicates that the rock mass near the surface, i.e. for depths 0-125 m, has been
reduced in mechanical quality, probably by the action of repeated glacial influence,
whereas the rock mass below 125 m depth has been unaffected and has a relatively
constant mean quality and variation with depth.
Similarly, and with reference to Figure 8-5, the mean RQD value inside tunnel above Z
= -100 m is 93.8% whereas below Z = -100 m, the mean RQD- value is 97.6%.
Figure 8-4. Estimated GSI values and major brittle deformation zones (the colours of the BDZs indicates their GSI-values).
35
Figure 8-5. Estimated RQD value inside the ONKALO tunnel.
Figure 8-6. Estimated RQD values in the block model. The values represent the general rock mass (here the BDZs are not included in the blocks).
36
Figure 8-7. Rock types inside the ONKALO tunnel ramp.
Figure 8-8. Assigned rock types in the block model.
37
Figure 8-9. Mean horizontal in situ stress component σH around the ONKALO tunnel.
Figure 8-10. Mean horizontal in situ stress component σh around the ONKALO tunnel.
Figure 8-11. Mean vertical in situ stress component σv around the ONKALO tunnel.
38
8.3 Uncertainties in the RMM
One of the main uncertainties in the model is incomplete coverage using the available
data. Although the average rock mass quality is well known, the estimation of local
anomalies is difficult. Based on the variogram studies, the maximum interpolation/
extrapolation distance for the GSI and RQD values is of the order of 30 m. Beyond this
distance, the level of confidence decreases. Block values estimated in the lowest class of
level of confidence are significantly averaged. Because the distances between drillholes
are relatively large compared to the block model dimensions, 92.8% of the block model
volume falls in the lowest class of level of confidence (Figures 8-12 and 8-13) –
although this refers to the blocks some distance away from the ONKALO tunnel.
Figure 8-12. Search radius in different calculation phases. Grey line presents drillholes i.e. input data.
Figure 8-13. Search radius in different calculation phases in tunnel blocks.
39
In Figures 8-14 to 8-16 are presented GSI values inside the tunnel blocks compared to
the original tunnel mapping data. The estimation of block values was made using data
only from the surface drillholes and pilot holes. The maximum search distance was 30
m, which corresponds to the first two classes in level of confidence. The accuracy of the
drillhole estimates of rock mass quality is ± one Q class.
Figure 8-14. Estimated GSI values from drillholes (left) compared to the originally mapped rock mass quality in the tunnel section (right) between chainage 0-760 m. The grey area represents areas where no sufficiently reliable geostatistical prediction can be made.
Figure 8-15. Estimated GSI values from drillholes (left) compared to the originally mapped rock mass quality in the tunnel section (right) between chainages 760-1820 m. The grey area represents areas where no sufficiently reliable geostatistical prediction can be made.
40
Figure 8-16. Estimated GSI values from drill hole data (left) compared to the originally mapped rock mass quality in the tunnel section (right) between chainages 1740-3000 m. The grey area represents areas where no sufficiently reliable geostatistical prediction can be made.
41
9 GEOPHYSICAL DATA
It was suggested that the seismic travel times from existing 3D VSP investigations
(surface drillhole reflection) conducted during years 1989-2005 could be used in
compiling a travel time tomographic representation of the near-ONKALO volume. The
target was set to estimate whether 5…20 m scale rock properties can be characterized
from the seismic travel time tomographic results.
The VSP data reprocessing includes gathering of first arrival times, inversion
computation with developed 2D bend tray tracing method and the tomographic
computations. In order to review the results against the drillhole geophysical data and
geological logging data the tomograms were transferred into Gemcom Surpac®
software. (Appendix 3.)
Conclusions based on the tentative processing phase of the VSP traveltime tomography
is that the technique is feasible for displaying medium scale (20-50 m thick layer)
variation in the rock mass, mostly due to lithological variation. Interpretation of velocity
anomalies in the model and an associated report are presented in Appendix 4.
At this stage, the geophysical data are not used in the RMM because more studies of the
correlation between rock mass quality and P-wave velocity need to be undertaken.
42
43
10 SPALLING PREDICTION
The spalling prediction is based on methodology presented by Martin & Christianson
(2008) and Hakala et al. (2008). The distribution for the maximum tangential stress
around the excavation is calculated based on the distribution of in situ stress, excavation
geometry, orientation and depth location. The probability of spalling is presented in the
form of the probability of the Factor of Safety (FOS) values. The FOS is calculated via
equation 10-1:
FOS = Factor of Safety = SPALLING / (10-1)
where SPALLING = spalling strength of the rock
= maximum tangential stress
The probability of spalling is the probability of FOS<1, and it is defined by three
categories based on the mean value of the FOS:
FOS Probability of spalling
>1.25 No
1.25-1.0 Minor or No
< 1.0 Extensive
The severity of spalling is defined by the spalling depth, which can be estimated by the
empirical equation 10-2 or from the results of numerical simulation by equation 10-3.
For simplicity, equation 10-2 is used in the RMM v 1.0.
Sd = a (0.5 ( / SPALLING) – 0.52) (10-2)
where Sd = spalling depth
a = average radius of excavation profile
Sd = depth where ( 1 - 3) > SPALLING (10-3)
where 1 = major principal stress
= minor principal stress
10.1 Spalling prediction for the ONKALO: Chainages 3117 - 4340 m
For the RMM version 1.0, the spalling prediction was made only for chainages 3117-
4340 m. The chainages considered were divided into five sections (Table 10-1). The
elastic tangential stresses around the excavations were calculated with Examine3D, a
three dimensional boundary element code. The spalling probability and depth
calculation for each tunnel section were obtained by Monte Carlo simulation with
20,000 realisations.
44
The results indicate minor spalling for the turn and the narrow profiles after the turn
(Table 10-1 and Figure 10-1).
Table 10-1. Tunnel sections for spalling estimation and the resulting values.
Profile from Depth
(m)
to Depth
(m) mean FOS PoS* Sd (m)
normal -295 -365 1.37 6 % 0.10
niche -295 -365 1.48 2 % 0.07
turn -365 -373 1.14 22 % 0.13
normal -373 -416 1.23 14 % 0.12
niche -373 -416 1.29 8 % 0.10
* PoS = probability of spalling
Figure 10-1. Spalling prediction for ONKALO chainages 3117-4340 m, i.e. contract phase TU4.
45
11 FUTHER DEVELOPMENT AND PRELIMINARY IDEAS FOR VERSION 2 OF THE ROCK MECHANICS MODEL
A fully featured and functioning RMM Version 2.0 should include more extensive
estimates of the secondary stresses and spalling around the excavations. Also, the
foliation information should be used in more detail. Moreover, efforts should be made
to use the rock type, foliation and geophysical information to see if the spatial variation
of rock strength can be estimated through these parameters.
The geometry and rock mechanics properties of the major BDZs should be updated. The
BDZ intersections in drillholes and the ONKALO tunnel should be further interpreted
also from the rock mechanics point of view.
In future, the RMM will be extended to the final disposal repository volume. In these
areas, the rock mechanics properties and the effect of smaller scale BDZs and long
fractures (TCF=Tunnel Crosscutting Fractures) should also be considered.
The geophysical tomography data will be studied more carefully and, if possible and
depending on the current studies, it will be used in the RMM Version 2.
Further detailed geostatistical analyses of rock mass quality will be considered. For
example, statistical analysis in the different rock mechanics domains could be
considered.
Thermal property model will be updated when more data is available. Possible
anisotropy should also be considered in RMM.
46
47
REFERENCES
Barton, N. Lien & R. Lunde, J. 1974. Engineering classification of rock masses for the
design of tunnel support. Rock Mechanics, Vol 6, No 4, p. 189-236.
Glamheden, R., Hansen, L. M., Fredriksson, A., Bergkvist, L., Markström, I., Elfström,
M. 2007. Mechanical modelling of the Singö deformation zone. SKB report R-07-06,
Stockholm. 85 p.
Grimstad, E. & barton, N., 1993. Updating of the Q-system for NMT. Proceedings of
the International Symposium of Sprayed Concrete, 18-21 December 1993. Fagernäs,
Norway. Kompen, E. Opsahl & Berg. Norwegian Concrete Association, pp. 46-66.
Gringarten, E. & Deutsch, C. V., 1999. Methodology for Variogram Interpretation and
Modelling for Improved Reservoir Characterization, Paper SPE 56654 presented at the
SPE Annual Technical Conference and Exhibition held in Houston, Texas, Oct 3-6
1999, 13 p.
Hakala, M., Hudson, J.A., Harrison, J.P. & Johansson, E. 2008. Assessment of the
Potential for Rock Spalling at the Olkiluoto Site. Posiva Working Report 2008-83.
Hoek, E., 1994. Strenght of rock and rock masses, ISRM News Journal, 2(2), 4-16
Hoek, E., Kaiser, P. K. & Bawden, W.F. 1995. Support of Underground Excavations in
Hard Rock. Balkema, Rotterdam, 215 p.
Hoek, E. & Marinos, P., 2000. GSI: a geologically friendly tool for rock mass strength
estimation. In: Proceedings of the GeoEng2000, international conference on
geotechnical and geological engineering, Melbourne, Technomic publishers, Lancaster,
pp 1422–1446
Huotari, T. & Kukkonen, I. 2004. Thermal expansion properties of rocks: Literature survey and estimation of thermal expansion coefficient for Olkiluoto mica gneiss. Posiva Oy, Working Report 2004-04.
Isaaks, E. H. & Srivastava, R. M. 1989. An Introduction to Applied Geostatistics.
Oxford University press, New York, 561 p.
Kemppainen, K., Ahokas,T., Ahokas, H., Paulamäki, S., Paananen, M., Gehör, S. &
Front, K. 2007. The Onkalo Area Model, version 1. Working Report 2007-71. Posiva
Oy, Eurajoki, 141 p.
Martin, C. D. & Christiansson, R. 2008. Estimating the potential for spalling around a deep nuclear waste repository in crystalline rock. Int. J. Rock Mech. Min. Sci., 46, pp. 219-228
48
Mattila, J., Aaltonen, I., Kemppainen, K., Wikström, L., Paananen, M., Paulamäki, S.,
Front, K., Gehör, S., Kärki, A. & Ahokas, T. 2007. Geological Model of the Olkiluoto
Site, Version 1.0. Working Report 2007-92. Posiva Oy, Eurajoki. 514 p.
Paulamäki, S., Paananen, M., Gehör, S., Kärki, A., Front, K., Aaltonen, I., Ahokas, T.,
Kemppainen, K., Mattila, J., Wikström, L., 2006. Geological Model of the Olkiluoto
site, Version 0, Working Report 2006-37. Posiva Oy, Eurajoki, 355 p.
Posiva 2007. Olkiluoto Site Description 2006, Posiva Report 2007-03
Posiva 2009. Olkiluoto Site Description 2008, Posiva Report 2009-01
Öhman, I., Heikkinen, E., Säävuori, H., Vuorinen, S., Paulamäki, S., Aaltonen, I. 2009.
Summary of petrophysical analysis of Olkiluoto core samples 1990 - 2008. Working
Report 2009-11. Posiva Oy, Eurajoki. 212 p.
Öhman, I., Palmén, J. & Heikkinen, E. 2008. Unification of acoustic drillhole logging
data. Working Report 2008-18. Posiva Oy, Eurajoki. 57 p.
49
LIST OF APPENDICES
APPENDIX 1
GSI and RQD values in a defined sub-horizontal section
APPENDIX 2
Search radii and GSI and RQD values in a defined vertical section
APPENDIX 3
Cosma, C., Cozma, M. & Enescu. N. 2008. P- and S-wave 3D velocity inversion of
VSP data collected at Olkiluoto. A memorandum.
APPENDIX 4
Heikkinen, E. 2009. Review of Seismic 3D Velocity Model Based On 3D-VSP Data. A
memorandum.
50
51
APPENDIX 1
Figure A1-0-1. Defined sub-horizontal section
Figure A1-0-2. Search radius in different calculation phases (left) and estimated GSI-values and major brittle deformation zones (right) at defined section. Small dots in the left figure represent drillhole intersections.
52
Figure A1-0-3. Estimated RQD values (left) and assigned rock types (right) at defined section. Major brittle deformation zones are not shown in the Figures.
53
APPENDIX 2
Figure A2-0-1. Defined vertical section.
Figure A2-0-2. Search radii in the different model calculation phases.
54
Figure A1-0-3. Estimated GSI values and major brittle deformation zones at defined vertical section.
Figure A1-0-4. Estimated RQD values at defined vertical section. Major brittle deformation zones are not shown in the Figure.
55
Figure A2-0-5. Assigned rock types at defined vertical section.
56
57
VIBROMETRIC
APPENDIX 3
P- and S-wave 3D velocity inversion
of VSP data collected at Olkiluoto
Vibrometric Oy
2008
58
VIBROMETRIC
59
VIBROMETRIC
TABLE OF CONTENTS
1 THE PURPOSE OF THE STUDY ...................................................................... 61
2 THE EQUIPMENT ............................................................................................. 63 2.1 The seismic source ................................................................................... 63 2.2 The seismic receivers ............................................................................... 64
2.2.1 The Recording station ................................................................... 65
3 THE TOMOGRAPHIC INVERSION METHOD ................................................... 67
4 THE ELASTIC PARAMETERS EQUATIONS .................................................... 73
5 CONCLUSIONS ................................................................................................ 75
APPENDIX 1 – SUMMARY OF THE VSP BOREHOLES USED FOR THE CURRENT STUDY ....................................................................................................................... 77
APPENDIX 2 – 3D VSP TOMOGRAMS...................................................................... 79 P wave tomograms ............................................................................................ 79 S wave tomograms ............................................................................................ 87 Tomograms obtained by simultaneous inversion of VSP data from several boreholes ........................................................................................................... 91
APPENDIX 3 ............................................................................................................ 103
60
VIBROMETRIC
61
VIBROMETRIC
1 THE PURPOSE OF THE STUDY
The objective of this study has been to determine the distribution of the P and S seismic
velocities within a rock volume covered by VSP measurements and to use them to infer
elastic parameters. Eight boreholes investigated by VSP have been selected based on
their location and data quality. The measurements were carried out between years 1990
and 2005, by Vibrometric Oy.
Figure A3- 1. Location of the drillholes (labeled blue triangles) and shot points (red circles). The projection of the Onkalo tunnel is represented by the black polygon.
62
VIBROMETRIC
63
VIBROMETRIC
2 THE EQUIPMENT
2.1 The seismic source
Surveys conducted prior to the year 2000 used dynamite charges of typically 125 or 250
grams. After that, the VIBSIST-1000, a time-distributed multi-impact source, replaced
the dynamite, see Table A3- 2. The VIBSIST-1000 seismic source was mounted on a
wheeled or tracked excavator equipped with hydraulic breaker (Figure A3-2).
The dynamite charges were fired in boreholes, directly coupled to the rock, whilst the
VIBSIST-1000 source was coupled through the overburden. The different coupling lead
to differences in the signal characteristics, e.g. slightly delayed arrivals with the
VIBSIST-1000. These were compensated in order to make the measurements
comparable.
Figure A3- 2. The Vibsist-1000 seismic source used at Olkiluoto.
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VIBROMETRIC
2.2 The seismic receivers
Several versions of the R8-XYZ-C geophone chain were used through time with the
VSP surveys at Olkiluoto (Figure A3-3). However, the essential specifications remained
largely the same, all versions consisting of eight 3-component modules with the Z-
component directed along the hole and the X- and Y-components perpendicular to the
Z-component and to each other. Each module is equipped with a side arm for clamping,
contains three 28-Hz geophones and down-hole preamplifiers. The frequency response
extends upwards to 1000 Hz, which is largely sufficient for both dynamite- and
VIBSIST- generated signals.
Figure A3- 3. The R8-XYZ-C geophone receiver chain.
65
VIBROMETRIC
2.2.1 The Recording station
A PC-based acquisition system (Figure A3-4) has been used, equipped initially with 16-
bit A/D converters and later upgraded to 24-bit converters in the year 2000.
Figure A3- 4. The acquisition station used for the surveys at Olkiluoto.
66
VIBROMETRIC
67
VIBROMETRIC
3 THE TOMOGRAPHIC INVERSION METHOD
A well-conditioned tomographic velocity-inversion exercise would require the rock to
be probed evenly and from view angles as diverse as possible. 3D inversion can in
principle be achieved by conducting multiple cross-hole surveys among densely drilled
set of boreholes. However, only two cross-hole tomographic surveys have been
performed at Olkiluoto: in 2002, between boreholes KR14 and KR15 and in 2003,
between boreholes KR04 and KR10. High resolution tomographic images of the P-wave
fields were produced, in local scale.
The current attempt to re-use the VSP data collected previously, as a part of the site
characterization programme, proves the feasibility of comprehensive mapping of the
velocity fields, hence of the elastic parameters, at the entire site scale, even if the
resolution is poorer than the one obtained by cross-hole tomographic inversion, as it can
be seen in Figure A3- 5.
Figure A3- 5. 3D P-wave velocity field computed now from the KR04 VSP data, together with the cross-hole tomography panel between boreholes KR04 and KR10, as calculated in 2003. View from East. Velocity range: 5650 m/s (blue)-5850 m/s (red).
68
VIBROMETRIC
The analysis done within this study consisted essentially of the following:
1) Independent tomographic inversions for each VSP shot gather, in the plane
defined by the respective borehole and the respective shot. This approach
produced fairly realistic velocity distributions with a good match of the synthetic
travel times computed on these distributions and the actual travel times (Figure -
A3- 7). However, the velocity distributions obtained independently for each shot
do not produce the same solution along the borehole. Therefore, such solution is
deemed unsatisfactory.
Figure A3- 6. Typical setup for tomographic inversion from VSP data. Example from KR08, six shot points.
Figure A3- 7. Reduced velocity shot gathers from borehole KR08 with P wave first arrivals (red line) modeled on the inverted velocity models computed independently on each shot gather.
69
VIBROMETRIC
The VSP geometry produces ill conditioned tomographic images because each inversion
panel is a single shot-gather, the result being essentially a velocity vs. depth chart. This
representation is nonetheless a valid expression of the velocity distribution within the
plane determined by the source and the receiver array. The validity of this
representation is demonstrated by plotting synthetic arrival times computed on the
velocity model against the data profiles as presented in Figure A3- 7. It can be noticed
that the synthetic first arrivals line up very closely with the real data, which gives
confidence to both computed ray pattern and velocity solution. The cell size (horizontal
x vertical) for this step was equal to 10 m x 5 m, for both P and S waves.
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VIBROMETRIC
Figure A3- 8. P-wave field reconstructed from the KR01 VSP data – before (up) and after (down) adding the sonic logs processed data. View from South. Velocity ranges from 5000 m/s (blue) to 6000 m/s (red).
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VIBROMETRIC
2) Simultaneous tomographic inversions for all VSP shot gathers measured in the
same borehole. This leads to a 3D tomographic solution consisting of several
planes, one for each shot point. At this stage the solutions obtained for different
shot points become consistent with each other, as they are obtained by
simultaneous inversion. However, the first arrivals computed on this velocity
model became less consistent with the actual first arrivals, when compared with
Step 1. The cause is related to shot-specific time delays produced either by local
overburden conditions and/or by the different definition of the zero-time for the
measurements done with explosive sources and the VIBSIST-1000. The
subtraction of the shot-specific time delays deemed as „static‟ corrections‟
improves the quality of the solution (Figure A3- 8). Sonic log data has been used
to aid with the computation of these delays, the rationale being that along the
borehole both VSP and sonic data should produce similar velocity distributions,
while the causes of the delays are confined near the surface. The cell size
(horizontal x vertical) for this step was equal to 20 m x 10 m, for both P and S
waves.
3) Simultaneous tomographic inversion of VSP data from all the eight holes, with
delay-reductions similar to Step 2. The cell size (horizontal x vertical) was also
equal to the one used in Step 2, i.e. 20 m x 10 m for both P and S waves. This
approach produces the most stable solution, although, somehow unexpectedly,
also the smoothest. A tentative conclusion is that more local velocity variations
are in fact possible measuring artefacts and that a consistent and reliable image
obtainable from the VSP measurements is in fact quite smooth (Figure A3- 9).
Figure A3- 9. 3D P-wave velocity field derived by simultaneous tomographic inversion of VSP data from all the eight holes.
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VIBROMETRIC
The data processing steps are described in Table A3- 1.
Table A3- 1. The data processing steps.
Item Processing step
1 Data transfer to Vibrometric format. Data conditioning & coordinates
2 Band-pass filtering 20 – 200 Hz
3 Rotate the X, Y, Z components to the Axial, Radial and Transversal
components
4 P wave arrival times picking on the Axial component
5 S wave arrival times picking on the Transversal component
6 Integrate the sonic logs P arrival times with the seismic ones
7 3D ray tracing based on the arrivals calculated at step 6
8 P wave tomographic inversion for each borehole, with ray paths computed
at Step 7
9 P wave tomographic inversion for all the boreholes, with modelled arrival
times computed at Step 8
10-12 Repeat steps 7 - 9 for the S waves arrival times
13 Calculate the elastic parameters based on the P and S waves tomograms
14 Model the elastic parameters as arrival times
15 Run the inversion for elastic parameters
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VIBROMETRIC
4 THE ELASTIC PARAMETERS EQUATIONS
Poisson‟s ratio: )(2
2
22
22
SP
SP
vv
vv
Shear modulus: 2
Sv
Young‟s modulus: )1(2E
For the Shear and Young‟s modulus, a constant density ( ) value of 2780 kg/m3 has
been used.
74
VIBROMETRIC
75
VIBROMETRIC
5 CONCLUSIONS
It is possible to use the VSP data for obtaining 3D tomographic maps of the P- and S-
wave velocities and of the corresponding elastic parameters. The volume considered in
this study is approximately 1000 m x 1000 m x 800 m, see Figure A3 -1. The site-
integrated results are presented in Appendix 2, in Figure A3- 22, Figure A3- 25, Figure
A3- 28, Figure A3- 30 and Figure A3- 32.
Main facts:
1) Difficulties: the main problem of this approach was represented by the relative
low 3D cube coverage by seismic signals ray paths, see paragraph “The
tomographic inversion method”.
2) Resolution: cell size (horizontal x vertical) was equal to 10 m x 5 m for the
processing step no. 7 and 20 m x 10 m for the processing steps no. 8 and 9.
3) Reliability: the synthetic arrival times computed on the velocity model line up
very closely with the real data, increasing the approach level of confidence, see
Figure A3- 7.
4) Sonic log information: proved to be useful but not mandatory, see Figure A3- 8.
Applied tomography algorithm: modified SIRT.
Recommended further works: a total of 19 boreholes have been used for VSP
measurements in the Olkiluoto area. The current study has focused on 8 boreholes in the
region of the ONKALO tunnel, see Figure A3- 1. The future work should integrate
some more boreholes in the already obtained velocity and elastic parameters model,
increasing both the scale and the resolution of the model.
76
76
77
VIBROMETRIC
APPENDIX 1 – SUMMARY OF THE VSP BOREHOLES USED FOR THE CURRENT STUDY
Table A3- 2. Summary of the VSP boreholes used for the current study.
No. BoreholeYear(s)
measuredSource type
P wave
tomo
S wave
tomo
Poisson's
ratio tomo
no. of
sources
no. of
detectors
min.
depth (m)
max.
depth (m)
Top borehole
elevation (m)
1 KR01 1990 Dynamite Yes No No 5 176 40 915 9.91
2 KR02 1990-1995 Dynamite Yes No No 7 200 30 1025 8.62
3 KR04 1990-1995 Dynamite Yes Yes Yes 7 172 40 895 9.15
4 KR08 1995-2005 Dynamite & Vibsist Yes Yes Yes 7 64 300 615 10.56
5 KR10 1996 Dynamite Yes Yes Yes 7 114 40 605 9.32
6 KR14 2002 Vibsist Yes Yes Yes 10 88 40 475 8.27
7 KR27 2005 Vibsist Yes No No 7 96 25 500 7.25
8 KR38 2005 Vibsist Yes Yes Yes 7 96 30 505 9.58
Total 8 8 5 5 57 1006
All the results are reported in a reduced coordinate system, as shown below:
X Y Z Northing Easting Elevation
1525540.5 6792362.4 9.91 = y - 6700000= x - 1500000 = z
92362.42 25540.54 9.91
Original coordinates Reduced coordinates
77
78
VIB
RO
ME
TR
IC
78
79
VIBROMETRIC
APPENDIX 2 – 3D VSP TOMOGRAMS
P wave tomograms
Figure A3- 10. KR01 – P wave tomograms.
79
80
VIBROMETRIC
Figure A3- 11. KR02 - P wave tomograms.
80
81
VIBROMETRIC
Figure A3- 12. KR04 - P wave tomograms.
81
82
VIBROMETRIC
Figure A3- 13. KR08 - P wave tomograms.
82
83
VIBROMETRIC
Figure A3- 14. KR10 - P wave tomograms.
83
84
VIBROMETRIC
Figure A3- 15. KR14 - P wave tomograms.
84
85
VIBROMETRIC
Figure A3- 16. KR27 - P wave tomograms.
85
86
VIBROMETRIC
Figure A3- 17. KR38 - P wave tomograms.
86
87
VIBROMETRIC
S wave tomograms
Figure A3- 18. KR04 – S wave tomograms.
87
88
VIBROMETRIC
Figure A3- 19. KR10 – S wave tomograms.
88
89
VIBROMETRIC
Figure A3- 20. KR14 – S wave tomograms.
89
90
VIBROMETRIC
Figure A3- 21. KR38 – S wave tomograms.
90
91
VIBROMETRIC
Tomograms obtained by simultaneous inversion of VSP data from several boreholes
Figure A3- 22. All site – P wave tomograms after processing step 9.
91
92
VIBROMETRIC
Figure A3- 23. All site – P wave tomograms after processing step 9 – rotated 180 degrees.
92
93
VIBROMETRIC
Figure A3- 24. All site – P wave tomograms after processing step 9 – top view.
93
94
VIBROMETRIC
Figure A3- 25. All site – S wave tomograms after processing step 12.
94
95
VIBROMETRIC
Figure A3- 26. All site – S wave tomograms after processing step 12 – rotated 90 degrees clockwise.
95
96
VIBROMETRIC
Figure A3- 27. All site – S wave tomograms after processing step 12 – top view.
96
97
VIBROMETRIC
Figure A3- 28. All site – Poisson’s ratio tomograms after processing step 13.
97
98
VIBROMETRIC
Figure A3- 29. All site – Poisson’s ratio tomograms after processing step 13 – rotated 90 degrees clockwise.
98
99
VIBROMETRIC
Figure A3- 30. All site – Young’s modulus tomograms after processing step 13.
99
100
VIBROMETRIC
Figure A3- 31. All site – Young’s modulus tomograms after processing step 13 – rotated 90 degrees clockwise.
100
101
VIBROMETRIC
Figure A3- 32. All site – Shear modulus tomograms after processing step 13.
101
102
VIBROMETRIC
Figure A3- 33. All site – Shear modulus tomograms after processing step 13 – rotated 90 degrees clockwise.
102
103
APPENDIX 4
Memo
REVIEW OF SEISMIC 3D VELOCITY MODEL BASED ON 3D-VSP DATA
ABSTRACT .............................................................................................................. 105
1 GENERAL ....................................................................................................... 107
2 ASSESSMENT OF INFORMATION INCLUDED IN SEISMIC DATA ............... 109 2.1 Sample comparisons .............................................................................. 110 2.2 Comparisons of samples to drillhole logging ........................................... 110 2.3 Comparison of velocity model to drillhole data ........................................ 111
3 REVIEW OF RESULTS ................................................................................... 115
4 DISCUSSION ON METHODOLOGY ............................................................... 125
5 CONCLUSIONS .............................................................................................. 127
REFERENCES ......................................................................................................... 129
Pöyry Environment Oy
P.O.Box 50 (Jaakonkatu 2) FI-01621 Vantaa Finland Domicile Helsinki, Finland Business ID. 0196118-8 Tel. +358 10 3311 Fax +358 10 33 26761 www.environment.poyry.fi Date Jun. 23, 2009 Ref. No Project 67070638.BGF
Eero Heikkinen Tel. +358 10 33 26751 [email protected] Page 103 (21)
104
105
ABSTRACT
A model of P and S wave velocities in Olkiluoto has been compiled on basis of 3D VSP
surveys during 1990 – 2005. Model uses arrival time and drillhole deviation data from 8
drillholes, OL-KR01, OL-KR02, OL-KR04, OL-KR08, OL-KR10, OL-KR14, OL-
KR27 and OL-KR38 located near ONKALO.
The model indicates that the velocities increase with depth. Model is highly averaging.
The velocities in the model are ranging at 5280 - 6120 m/s for P-wave (average 5500
m/s) and at 2620 - 3420 m/s (average 3100 m/s) for S-wave. Lowest velocities are
encountered at shallowest 0 - 100...200 m depth interval.
Apart of the depth trend there are observed also zones where velocity is varying from
the average, high velocities indicating rock type variation e.g. locations occupied by
granite pegmatite, TGG gneiss or competent mafic rocks, and lower (<5300 m/s or
<3000 m/s) velocities indicating locations of alteration or deformation in rock mass.
Most indications of these zones are associated to depth intervals 300-380 m, 500 m and
600-800 m.
The explanation for velocity variation in the drillholes is found from more detailed
petrophysical sample and drillhole logging data. Variations arise from lithology,
alteration and deformation. Fresh (non altered, non deformed) rock types have
characteristic velocities, granite pegmatite 5900 - 6100 m/s, some of the mafic gneisses
6000 - 6500 m/s, and diatexitic, veined, mica and tonalitic gneiss, partly also mafic
gneiss rather varying 5300 - 5900 m/s. The velocity of gneisses will depend on
leucosome content, and is decreasing according to increasing density, i.e. according to
increase of mafic silicates (biotite and amphibole), which is associated also with texture
of the rock mass.
High banding intensity is associated to lowered velocity. Velocities decreasing below
5100 - 5300 m/s start to indicate increase in degree of alteration or deformation due to
increased fracturing or porosity. Decrease of velocity below 4500 - 5100 m/s is
indicating presence of significant deformation or alteration. Tomography averages out
this low values, as minimum directly observable layer thickness is order of 20 – 50 m.
Drillhole logging data of P and S velocity can be used to delineate location and
thickness of anomalous velocity zones. Velocity model can be used to estimate
continuity, orientation and extent of these zones between and outside of drillholes. The
seismic reflection interpretations from VSP surveys and from 3D surface reflection
surveys provide tools to delineate the boundaries of these anomalous velocity zones.
106
107
1 GENERAL
Seismic P and S wave velocity data and reflection features can be used for rock mass
characterization.
The reflecting features are indicating fairly accurately location of elastic contrasts
(impedance, consisting of velocity and density). Reflection measurements do not
indicate directly the rock mass properties.
Transmission measurements and attribute interpretations can provide averaged rock
mass properties of P and S wave velocity and Poisson‟s ratio. These properties are
indirectly associated to lithology, fracture frequency, deformation and alteration of the
rock mass. The properties are linked in varying manner over different scales of
observation.
Vibrometric Oy has reprocessed the 3D VSP survey data from eight drillholes in
Olkiluoto in order to create tomographic scans. Purpose was to produce P and S wave
velocity data to be used for rock mechanical modelling of Olkiluoto.
This memorandum represents the comparison of results to available drillhole data.
108
109
2 ASSESSMENT OF INFORMATION INCLUDED IN SEISMIC DATA
The P and S wave velocities are affected by mineral content, texture, porosity and
fracturing, deformation and alteration of the rock mass as well as by stress field.
Seismic measurement data on velocity has been obtained in following scales and
geometries:
- Petrophysical samples on 50-100 mm sample scale, which can be
compared to other sample data, and to some extent to core logging and
drillhole measurement data (Öhman et al. 2009a)
- Sonic logging in all drillholes in Olkiluoto, a continuous measurement of
P and S wave velocities immediately in the drillhole wall over 1 m
interval (Öhman et al. 2009b).
- Seismic refraction survey in most of the Olkiluoto area at 50 m line
interval (bidirectional). The velocity is describing the surface part of
bedrock down to 20-30 m depth level (Lehtimäki, 2002).
- Seismic refraction survey in tunnel (Cosma et al. 2008)
- Crosshole tomographic surveys in KR14-KR15 and KR4-KR10 (Enescu
et al. 2003, 2004).
Velocity model or constant velocity has been used in processing of 3D VSP surveys
during 1990 – 2005 and in surface based 3D reflection surveys 2006 and 2007 (Juhlin &
Cosma 2007, Cosma et al. 2008). The processing velocity does not adequately
characterize the rock properties.
There would be limited possibilities to process more detailed velocity variation from the
3D seismic survey data, either with pre-stack amplitude vs. offset analysis and dip
moveout modeling (DMO) or using inversion modeling in the stacked reflection cube.
Purpose of the currently presented work is to create a velocity model from 3D VSP
travel times.
The available sample and drillhole logging data, as well as geological data, are from
different scale and from different acquisition geometry as the tomographic results. The
source of velocity variation cannot be explained by direct comparison, which would
easily lead to erroneous deductions. Results from OL-KR01 were used to explain the
velocity variation. Validation chain used below is the following:
1) Velocity sample measurement data (“petrophysics”, Öhman et al. 2009a) is
compared to lithology and alteration of the samples, and to other physical
properties (density, porosity, resistivity) from same location or same
samples.
2) Velocity sample measurement data is compared to drillhole sonic logging
data (Öhman et al. 2009b) from same locations
3) Drillhole sonic logging data is compared to lithology, fracture frequency,
deformation and alteration of the core logging (Paulamäki et al. 2006) from
same locations.
110
4) Drillhole sonic logging data is compared to the tomographic velocity at the
drillhole location.
Comparisons have been carried out also between the scales to demonstrate possible
correlation.
2.1 Sample comparisons
From 1350 samples (Öhman et al. 2009a) where porosity and P-wave velocity has been
measured, a general trend of decreasing velocity with increasing porosity is seen.
Correlation coefficient is -0.42. Correlation is more distinct in gneisses (-0.43 - -0.51)
than in pegmatite granite (-0.18). Correlation does not exist or is reverse in TGG, MDB,
QGN and KPF.
P wave velocity in the samples correlates weakly with lithology (coefficient 0.24).
Review was performed by giving the rock types the median of velocity in the rock type
as numerical value.
Correlation of P wave velocity in the samples is very weak on deformation and
alteration (-0.09, velocity is slightly decreasing in altered samples). Review was made
by assigning the samples “1” when belonging to alteration or deformation interval, “2”
when belonging to both, and “0” when not belonging to either of these.
Correlation of P wave velocity and density is weakly negative (-0.11). Velocity is
decreasing with increasing density in DGN, KFP, PGR and SGN (correlation -0.11 -
.0.24, probably due to melanosome content), in MFGN and QGN velocity increases
with increasing density (correlation 0.33 – 0.69). In MGN, TGG and VGN there is only
slight or no correlation between density and velocity.
Correlation of P wave velocity and resistivity is weak, 0.15. Resistivity and P wave
velocity is increasing while porosity is decreasing, especially in DGN, MGN, MFGN
and VGN (0.21 – 0.27). Correlation is very weak in TGG (0.12), does not exist in PGR
and SGN and is reverse in MDB, KFP and QGN (resistivity is decreasing as velocity
increases).
2.2 Comparisons of samples to drillhole logging
The 95 petrophysical samples from OL-KR01 were compared to P wave velocity
logging data from the same drillhole location. There is a visible trend of increasing
velocity. Correlation is weak 0.15. There are clear differences in drillhole logging and
petrophysical samples, which may be explained with:
- averaging of logging data over interval
- positioning errors
- measurement errors on both data sets
- stress field effect on rock mass in in situ measurement.
Rejecting most distinct outliers from comparison will increase correlation to 0.46.
111
Comparison of drillhole logging to geological core data
Geophysical P wave velocity logging has a rather distinct correlation to fracture
frequency (-0.51). Correlation to core loss and Ri zone indications is slightly weaker (-
0.37). Correlation to alteration zone occurrences is also -0.38. Highest correlation is
observed to kaolinite and illite alteration (-0.32 – -0.37), and lower to sulphide alteration
(-0.16). Most effect has the pervasive kaolinite alteration (-0.31). Correlation to
deformation zones is slightly lower, -0.26. Combined correlation of alteration and
deformation is -0.39.
The velocity decreases as fracture frequency increases, and in presence of fracture
zones, alteration or deformation.
Correlation to lithology is higher than for the samples, 0.34. The median of the
velocities for rock types is different for petrophysical samples and for logging, which is
caused by stress field and orientation effects on rock mass different in laboratory and in
situ, and due to fact that laboratory samples do not contain fractures whereas the
logging will encounter both rock mass and fracturing.
The presented correlations were reviewed in OL-KR01 only and can be different in
other drillholes and other parts of the area.
2.3 Comparison of velocity model to drillhole data
The velocity model data from different steps of tomographic inversion were compared
with lithology, alteration, deformation, fracture frequency and sonic logging data.
Values of tomogram were picked at the drillhole path.
The most obvious correlation should occur between the P wave velocity from sonic
logging and tomography. The single slice tomography (an example from source S1
above the drillhole) in OL-KR01 has correlation 0.42. The complete hole tomogram of
OL-KR01 compared to logging in OL-KR01 has correlation of 0.394. The complete
area tomogram in OL-KR01 receives correlation 0.388. Different tomographic step
results (slice, whole drillhole and whole area) correlate 0.68 – 0.73.
Comparison to petrophysical data from the same depth level leads to somewhat
confusing result. Velocities have a negative correlation of -0.27 – -0.31. This is
probably due to different scale and the directional and in situ effects of tomographic
survey. Petrophysical data is highly scattered when compared to imaging, and cannot be
directly compared to the image results.
Correlation of seismic measurements between different scales is not perfect, as the
scales are different. Also the tomographic image describes the rock mass from larger
interval and also from the volume outside of the drillhole. Features may also image to
slightly different position due to dip or other geometric reasons.
112
On the tomographic imaging scale lithology on drillhole logging does not correlate to
seismic P wave velocity (-0.05 – 0.09). Shifting the tomographic result some 25 m
downwards the correlation gets sligthly better, 0.15 - 0.22.
Fracture frequency has correlation of – 0.12 - -0.17 (shifted downwards -0.16 - -0.21).
This indicates the P wave velocity is expected to decrease when fracture frequency
increases.
Fracture zone or core loss indications (which are rather narrow) have a correlation of -
0.1.
Deformation zone indications alone explains fairly little (-0.1), including the influence
zones may have some effect.
The alteration indications (typically from rather wide section) indicate correlation of c. -
0.22 to seismic P wave velocity from tomogram. According to this, the alteration has
some reducing effect on seismic velocities in this scale. Strongest correlation (-0.31) has
kaolinite alteration. Also sulphide alteration has a slight effect (-0.23).
Comparison of seismic P velocities from petrophysical samples, logging and different
tomographic processing steps in OL-KR01 is shown in Figure A4- 1.
113
Figure A4- 1. Geological information from OL-KR01 (Paulamäki et al. 2006) and seismic P wave velocity data in different scales.
114
115
3 REVIEW OF RESULTS
Results were compared to drillhole lithological and acoustic logging results. The near
offset shot point has produced fairly similar results as the sonic logging does, but the
variation is displaying the largest zones of both high and low velocities (Figure A4- 2).
Figure A4- 2. Tomogram of near offset shot in OL-KR2. Velocity range 5100 – 5900 m/s (blue-red). Velocity high and low positions are fairly similar. TGG (yellow) and PGR (red) indicate higher velocities than DGN and VGN gneiss (light and dark blue).
116
The single slice from one shot point to one drillhole is often describing continuity or
termination and apparent angles of low and high velocity zones met in the drillhole. Part
of this information may suffer from geometric or processing artifacts.
Velocity distribution consists typically of low velocity zone at the surface (Figure A4-
3).
Figure A4- 3. Low velocity at top part of rock, OL-KR10 shot point towards the east. View from S.
117
Another low velocity zone is seen in all drillholes at 200-400 m depth level (HZ20).
Yet another low velocity zone is seen at c. 500 m depth level, see Figure A4- 4 below.
Figure A4- 4.Low velocity zones at 300 – 400 m and 500 m, shot point towards the east, view from S.
118
One more low velocity zone is met at 600 – 800 m depth level (HZ21), Figure A4- 5.
Figure A4- 5. Low velocity zone at 500 – 600 m, OL-KR01 shot point towards the north, view from E.
119
High velocity zones are met on several profiles in OL-KR1 and OL-KR2 at upper part
of rock mass in the north and west. The zone seems to dip towards south and east and to
terminate before reaching the drillhole OL-KR1. (Figure A4- 6).
Figure A4- 6. High velocity zone at 200 – 300 m, OL-KR01 shot point towards northeast, view from E.
120
Possibly the higher velocity is explained by TGG gneiss in OL-KR2 and OL-KR14, and
also in OL-KR20 (not included into survey). Rock type TGG is typically sparsely
fractured. Another zone of higher velocity is observed in mid part of OL-KR10. Results
rendered from the whole drillhole results show the directions and continuity of the low
velocity zones, as compared with the drillhole observations (Figure A4- 7).
Figure A4- 7. E – W view of OL-KR01 tomogram with seismic P-wave velocity (blue) and density (green) along the drillhole. High and low velocities in logging and in tomograms are matching. Velocity model shows discontinuous features
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The imaging results applying all data simultaneously smooth out the minor details.
Results indicate that the low velocity zone in the surface extents deeper (-300 m level)
in the north and also in the east (OL-KR27). The HZ20 and HZ21 zones are seen also in
this result as extensive low velocity areas (Figure A4- 8).
Figure A4- 8. Section along Easting = 1525.880 (KKJ1). Velocity low is seen to reach deeper in the north (up to -300 m level). Low velocity zones are observed at 300 – 500 m and 600 – 700 m levels.
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Figure A4- 9. Section along Northing = 6792.200 (KKJ1). Velocity low are seen at the top down to 200 – 300 m, ta c 400 m and at 600 – 700 m.
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It is possible to compare the tomographic results also with other modeling or seismic
reflection data. Below is presented a reflection image from 3D survey data, overlain
with VSP tomographic velocity slice (Figure A4- 10).
Figure A4- 10.The seismic 3D reflection result and intersection of VSP tomographic result from OL-KR1. Red is high velocity and blue velocity. Green and black indicate high reflectivity.
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4 DISCUSSION ON METHODOLOGY
Seismic P and S wave velocity model was constructed on base of 3D VSP surface to
drillhole measurements. The model is focused near to the ONKALO volume.
Model is sparse, consisting of sub-planar slices from single sources to drillhole. Slices
(5-7 for each drillhole) are arranged in a star-shaped pattern. Coverage deeper down in
rock mass is concentrated in a narrow area near drillholes. Results leave gaps between
different profiles and drillholes.
Modelling was performed in three steps:
1) tomographic inversion of one source and all receivers (slice)
2) inversion of all slices for one drillhole, harmonising the velocities for
drillhole.
3) inversion of all drillholes, regularizing the result over all directions.
The initial modeling step produces the highest resolution and best correlation to
drillhole mapping and logging data but is prone to processing artifacts. Rock properties
are projected to the plane of image and may represent apparent dips rather than real
orientations of anomalies. Result would be still useful in delineation of velocity
contrasts. Features which can be tentatively detected in this material are possible
velocity high and low areas, and potential discontinuities in these areas or even vertical
displacements.
Single drillhole model is maintaining most of the details of the individual slices, and
joining the velocity levels comparable. The result starts to smooth out minor details.
The whole site model is averaging the velocity strongly. Velocity levels are consistent
over the site volume. Result is indicating the most significant and continuous velocity
low and high areas.
There are several opportunities to continue and improve in this work, if found
necessary.
Coverage:
- now eight drillholes and 60 profiles near ONKALO were selected for
processing of P-waves and half of this (four drillholes/30 profiles) for S-
waves.
- adding up with remaining measured drillholes would enhance coverage;
these are OL-KR03, OL-KR05, OL-KR06, OL-KR07, OL-KR09, OL-
KR11, OL-KR12, OL-KR13, OL-KR19 and OL-KR29.
- adding up with moving source VSP (WVSP) in OL-KR04, OL-KR08, OL-
KR10 and OL-KR14.
- other drillholes in the near volume which have not been measured with VSP
can be used to supplement the image in ONKALO area (OL-KR15, OL-
KR20, OL-KR22, OL-KR23, OL-KR25, OL-KR28, OL-KR37, OL-KR39)
and the drillholes in the east-northeast part area can be used to extent the
image (OL-KR40 – OL-KR42, OL-KR44 – OL-KR50).
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Enhancing the measurement technique:
- surface to drillhole reflection technique (3D VSP) has not been designed for
tomographic imaging but was used with success.
- new measurements should take into account a precise timing and signal
consistency to avoid problems in processing
- several source stations would be useful to be placed on each azimuth, at
several offset distances. Overlapping traces would enhance imaging.
- the same source stations are useful to be used for several drillholes, to make
the traces spatially overlapping and to allow interferometry.
- several drillholes can be measured in a surface to drillhole profile, which
would bring in continuity in a 2D plane.
New geometries to be considered:
- apart of surface to drillhole and crosshole geometries, other possibilities for
tomography are available
- tunnel excavation blast travel times in microseismic network have a good
coverage and will construct over time
- profiles between tunnel sections and ground surface (to image vertical
structures)
- profiles between tunnel sections
- profiles between tunnel sections and drillholes.
Processing tecniques:
- using the traveltime difference from a same source to receivers in two
drillholes (at a time) together with path between receivers will produce
interferometric imaging, and will assist in filling gaps between drillholes
and in horizontal direction.
- imaging can be used as a starting model for numerical inversion, which
would produce sharper model boundaries. This kind of modeling would
require denser coverage and constrains taken from drillhole logging.
- application of attribute analysis to existing surface 3D reflection results, to
obtain velocity field (the accuracy of stacking velocities is inadequate for
this purpose).
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5 CONCLUSIONS
A model of P and S wave velocities was compiled using 3D VSP surface to drillhole
reflection data. Measurements have been designed for reflection, and techniques applied
vary from dynamite source to engineered impact series source. This has caused
problems for travel time picking and processing which need to be overcome.
The model is representing well the velocity distribution in Olkiluoto area. Velocity
ranges are conforming with the previously known levels from petrophysics, sonic
logging and crosshole tomographic results. Velocity field is smooth. Imaging using all
drillhole data simultaneously is averaging the model, but showing correct velocity levels
and indicating the most significant low velocity volumes in the area. These are
potentially linked to alteration and deformation features.
The single hole images and images created between single source station and one
drillhole are representing in more detailed manner the local velocity variation, and may
display the low and high velocity zones in more detail. The results are not on exact
velocity level regionally, and may contain processing and geometric artefacts.
The results compare well with drillhole sonic logging and large scale geological features
(alteration and to some degree lithology). Results do not compare well with sample data
or detailed geological information from core. However as the sonic logging does
correlate with detailed information, this provides a link with which the observations can
be explained.
Some part of low and high velocity zones are explained with lithological or textural
variation. Significant lowering of velocity is explained with deformation and alteration
zones, mainly with increase of fracturing and porosity. Features which are seen as
velocity anomalies in tomography need to be large and continuous.
The velocities range at 5100 – 6100 m/s (P) and 2900 – 3300 m/s (S). Lowering of
velocities below 5300 m/s (P) and 3000 m/s (S) is indicating some degree of
deformation or alteration.
The near surface fracturing intensity and effect of increasing stress are seen as
increasing velocity according to depth. The S wave velocity is more sensitive to this
effect. Locally the low velocity zones can be distinguished from this trend. Most
significant low velocity areas extent from surface to 100…300 m depth (deepest in the
north part of area), and between 300-400 m (HZ20), at 500 m and between 600-800 m
(HZ21). One velocity low area is met at bottom of drillhole OL-KR08.
The technique has proven the potential to characterize the rock mass with seismic P and
S wave velocities. The velocities are depending on lithology, texture, porosity,
fracturing, deformation, alteration and stress field. When using the results it is necessary
to explain the source of observed anomalies with drillhole data. Results are showing
lateral variation of velocity outside of drillhole covered areas. There is a possibility to
use the results in delimiting anomalous volumes (best together with reflection data).
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Tomography will allow assigning the material properties to rock mass. Priority for the
material property presentation is the P and S wave velocities and Poisson ratio which
are directly obtained from data. There are some possibilities to use the results to
characterize alteration, fracturing and lithology, but correlations from core mapping
scale are rather weak.
It would be useful to apply the tomographic images together with reflection data where
the boundaries are seen more accurately.
The tomographic model may be supplemented with existing data if found necessary. It
is also possible to design possible future 3D VSP measurements in a way the processing
would be more readily obtainable.
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