EDZ Study Area In ONK-TKU-3620: Petrophysical, Rock Mechanics … › files › 5087 › WR_2017-56_web.pdf · 2019-11-18 · EDZ STUDY AREA IN ONK-TKU-3620: PETROPHYSICAL, ROCK MECHANICS

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FI-27160 EURAJOKI, F INLAND

Phone (02) 8372 31 (nat. ) , (+358-2-) 8372 31 ( int. )

Fax (02) 8372 3809 (nat. ) , (+358-2-) 8372 3809 ( int. )

Working Report 2017-56

EDZ Study Area In ONK-TKU-3620: Petrophysical, Rock Mechanics and Petrographic

Testing and Analysis Conducted on Drill Core Specimens between 2014 and 2016

Risto Kiuru, Eero Heikkinen, Lars Jacobsson, Dorottya Kovacs

November 2019

ONKALO is a registered trademark of Posiva Oy

Working Reports contain information on work in progress

or pending completion.

Working Report 2017-56

EDZ Study Area In ONK-TKU-3620: Petrophysical, Rock Mechanics and Petrographic

Testing and Analysis Conducted on Drill Core Specimens between 2014 and 2016

Risto Kiuru

Geofcon

Eero Heikkinen

Pöyry Finland Oy

Lars Jacobsson

RISE Research Inst i tutes of Sweden

Dorottya Kovacs

University of Szeged

November 2019

EDZ STUDY AREA IN ONK-TKU-3620: PETROPHYSICAL, ROCKMECHANICS AND PETROGRAPHIC TESTING AND ANALYSISCONDUCTED ON DRILL CORE SPECIMENS BETWEEN 2014 AND 2016

ABSTRACT

Physical and mechanical properties of drill core specimens were determined as a part ofinvestigations into excavation damage in the dedicated study area in ONK-TKU-3620.Additionally, microfractures in 16 specimens from drillholes ONK-SH32 and ONK-SH39 were analysed and used as a basis for fractals based discrete fracture networkmodelling.

Best indicators for excavation damage, based on this study, would appear to be resistivity,S-wave velocity, and elastic parameters that are derived from S-wave velocity. Theeffects seemed to be mostly limited to pegmatoid specimens. Same properties alsodistinguished anomalous specimens, typically from shallow depths, some of which couldbe directly linked to visible EDZ features.

Comparing the crack initiation stress values to prior data from specimens saturated withnon-saline water, similar results were obtained. This suggests that whatever chemicalcorrosion the saline water may have inflicted on the specimens has happened prior tosampling in situ, and that the decrease in strength compared to dry specimens is mainlydue to water content, rather than the type of water.

It was concluded that the difference in resistivity between pegmatoid and gneissicspecimens of similar porosity was likely due to differences in the types of microfractures.This hypothesis was later confirmed from the thin sections and simulation: fractures inVGN were short and mostly in one preferred orientation, whereas the fractures in PGRwere longer and had two preferred orientations. This suggests that VGN and PGR maysuffer different types of excavation damage. Based on simulation results, the number oflarge fracture clusters and maximum number of fractures per cluster seem to increase withincreasing volumetric PGR content. The interconnectivity of microfractures howeverseems to be negligible.

No depth dependencies in geometric parameters of the microfractures were observed,while differences between VGN and PGR were observed. This suggests that excavationdoes not cause detectable disturbance of the intact rocks’ microfracture porosity, and thatthe disturbed layer observed by geophysical methods may be caused by larger scalefractures not present in the relatively intact specimens.

Keywords: ONKALO EDZ study area, petrophysics, rock mechanics, micro scalediscrete fracture network model, laboratory testing, statistical analysis, simulation,ultrasonics, Brazilian test, UCS test

EDZ-TUTKIMUSALUE ONK-TKU3620:SSÄ: KAIRANÄYTTEILLE VUOSINA2014-2016 SUORITETUT PETROFYSIKAALISET, KALLIOMEKAANISET JAPETROGRAFISET MITTAUKSET JA ANALYYSI

TIIVISTELMÄ

Kairasydännäytteiden fysikaalisia ja mekaanisia ominaisuuksia tutkittiin osanalouhintavauriotutkimuksia ONKALOn louhintavaurion tutkimusalueella (ONK-TKU-3620). Lisäksi kuudestatoista kairarei’istä ONK-SH32 ja ONK-SH39 otetusta näytteestäanalysoitiin mikroraot ja havaittuja parametreja käytettiin rakoverkkomallinnuksenlähtötietona.

Tämä tutkimuksen perusteella parhaat louhintavaurion indikaattorit näyttäisivät olevanominaisvastus, S-aallon nopeus sekä elastiset parametrit, jotka riippuvat S-aallonnopeudesta. Havaittu vaikutus näyttäisi rajoittuvan lähinnä pegmatoidinäytteisiin.Samojen ominaisuuksien perusteella voidaan erottaa anomaalisia näytteitä, jotka olityypillisesti otettu läheltä pintaa, ja joista osassa oli visuaalisesti havaittu EDZ-piirteitä.

Rakoilun alkamisjännitykset vastasivat aiempaa, ei-suolaisella vedellä saturoiduistanäytteistä mitattua, aineistoa. Vaikuttaisi siltä, että mahdollinen suolaisen vedenaiheuttama kemiallinen korroosio on tapahtunut ennen näytteiden ottamista in situ tilasta,ja että heikentävä vaikutus (suhteessa kuiviin näytteisiin) aiheutuu lähinnävesipitoisuudesta, ei niinkään veden laadusta.

Huokoisuudeltaan samankaltaisten pegmatoidi- ja gneissinäytteiden ominaisvastuksessahavaittujen erojen pääteltiin johtuvan todennäköisesti eroista rakoilutyypissä. Tämävahvistettiin myöhemmin ohuthieistä ja simulaatioissa: raot VGN:ssä olivatpääsääntöisesti lyhyitä ja yhdessä suunnassa, kun taas PGR:ssä oli suhteellisesti pidempiärakoja kahdessa suunnassa. Tämä viittaa siihen, että VGN ja PGR saattavat kärsiäerityyppisestä louhintavauriosta. Simulaatioiden perusteella suurten rakoparvien määrä,ja rakojen maksimimäärä rakoparvea kohti vaikuttaisi kasvavan PGR:n tilavuusosuudenkasvaessa. Kuitenkin mikrotason rakojen väliset yhteydet olivat koko aineistossavedenjohtavuuden kannalta merkityksettömiä.

Mikrotason rakoparametreissa ei havaittu syvyysriippuvuuksia, mutta VGN ja PGRerottuivat toisistaan. Tämä viittaa siihen, ettei louhinta aiheuta havaittavissa oleviahäiriöitä ehjän kiven mikrotason huokoisuuteen, ja että geofysikaalisilla menetelmillähavaittu ominaisuuksiltaan anomaalinen kerros saattaa olla suuremman mittakaavanrakojen aiheuttamaa, eikä siis havaittavissa suhteellisen ehyistä näytteistä.

Avainsanat: ONKALO EDZ-tutkimusalue, petrofysiikka, kalliomekaniikka, mikrotasonrakoverkkomalli, laboratoriomittaukset, tilastollinen analyysi, simulaatio, ultrasoniikka,Brazilian-koe, UCS-koe

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TABLE OF CONTENTS

ABSTRACT

TIIVISTELMÄ

1 INTRODUCTION ................................................................................................... 31.1 Excavation damaged zone and investigations in the study area ..................... 31.2 Site geology .................................................................................................... 5

2 SPECIMENS AND PREPARATION ...................................................................... 72.1 Overview of the campaign .............................................................................. 72.2 Specimen selection ......................................................................................... 92.3 Overview of the specimens ........................................................................... 102.4 Specimen preparation ................................................................................... 11

3 PETROPHYSICAL TESTING .............................................................................. 153.1 Overview....................................................................................................... 153.2 Key results .................................................................................................... 16

4 ROCK MECHANICS TESTING ........................................................................... 234.1 Overview....................................................................................................... 234.2 Key results .................................................................................................... 23

5 STATISTICAL ANALYSIS ................................................................................... 255.1 Overview....................................................................................................... 255.2 Key results .................................................................................................... 27

6 DISCRETE MICROFRACTURE NETWORK ANALYSIS ..................................... 336.1 Overview....................................................................................................... 336.2 Key results .................................................................................................... 36

7 DISCUSSION AND IMPLICATIONS ................................................................... 397.1 Specimens and preparation .......................................................................... 397.2 Petrophysical testing ..................................................................................... 407.3 Rock mechanics testing ................................................................................ 427.4 Statistical analysis ........................................................................................ 457.5 Discrete microfracture network analysis ........................................................ 457.6 Responses from the data .............................................................................. 46

8 CONCLUSIONS .................................................................................................. 49REFERENCES ........................................................................................................... 51

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1 INTRODUCTION

Posiva Oy is responsible for the final disposal of the spent nuclear fuel of its ownersTeollisuuden Voima Oy and Fortum Power & Heat Oy. As part of the disposal process,investigations have been carried out in the ONKALO research facility in Olkiluoto,Western Finland. The chosen disposal method is deep geological disposal with multiple-barriers method KBS-3, originally developed by the Swedish Nuclear Fuel and WasteManagement Company SKB, and more precisely its vertical variant KBS-3V. Spent fuelwill be isolated from the environment with multiple engineered barriers; the fuel pellet,the fuel rod, a cast iron canister insert, a copper overpack, bentonite buffer, tunnelbackfill, and finally several hundred meters of bedrock. The disposal method is describedin Posiva Working Report 2012-66 (Palomäki and Ristimäki (eds.), 2013). This study isa part of the extensive on-going investigations of excavation damaged zone caused by thedrill & blast excavation method.

1.1 Excavation damaged zone and investigations in the study area

Transport of radionuclides with water in fractured bedrock has been identified as one ofthe key risks to long-term safety of the final disposal (Mustonen et al., 2010). Excavationof the disposal tunnels with drill and blast method causes damage to the surrounding rockmass, seen as the Excavation Damaged Zone (EDZ). Characterisation of the EDZ isnecessary for understanding the fluid transport properties of the damaged rock mass nearthe excavation profile of the tunnels.

Posiva has committed a set of research work in ONK-TKU-3620 between 2012 and 2016to characterise EDZ. Research work consisted of geological, geophysical, hydrologicaland rock mechanical studies and modelling. Results of the study programme will bepublished in reports listed in Table 1-1. Reports are in different publication phase, butthey all will be published in Posiva's web site (www.posiva.fi) when complete.

This report summarises the work done on drill core specimens from the EDZ study areain ONK-TKU-3620 between 2014 and 2016. It includes short summaries of the individualsub-studies: petrophysical testing, rock mechanical testing, statistical analysis andmicrofracture network analysis. Key results of the different sub-campaigns are analysedtogether and discussed in larger context. For details on the specific parts, see Kiuru(2016), Jacobsson et al. (2016b) and Kovács et al. (2016).

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Table 1-1: All research work reports from studies committed in ONK-TKU-3620. Currentreport is highlighted.

Name Author(s)

EDZ Study Area in ONK-TKU-3620:Hydrological measurements, phase I

Stephan Rohs / Golder Associates OySascha Lenné / Golder Associates OyJörg Böhner / Golder Associates Oy

EDZ Study Area in ONK-TKU-3620:Hydrological measurements, phase II and III

Stephan Rohs / Golder Associates OySascha Lenné / Golder Associates OyJörg Böhner / Golder Associates OySven Follin / Golder Associates Oy

EDZ Study Area in ONK-TKU-3620:Geophysical tests conducted between 2012 and 2014

Eero Heikkinen / Pöyry Finland OyPekka Kantia / GeofconRisto Kiuru / Pöyry Finland OyNoora Riihiluoma / Posiva Oy

EDZ Study Area in ONK-TKU-3620:Block Sawing Experiment

Mats Olsson / EDZ Consulting ABNoora Riihiluoma / Posiva OySanna Mustonen / Posiva Oy

EDZ Study Area in ONK-TKU-3620:GPR and GPR EDZ method performance tests

Eetu Pussinen / GeofconPekka Kantia / GeofconEero Heikkinen / Pöyry Finland Oy

EDZ Study Area in ONK-TKU-3620:Theoretical review and numerical modelling of GPR andGPR EDZ method

Eero Heikkinen / Pöyry Finland OyRani Hamrouche / Roadscanners OyEetu Pussinen / GeofconPekka Kantia / GeofconPekka Maijala / Roadscanners Oy

EDZ Study Area in ONK-TKU-3620:Seismic cross-hole tomography

Juan Reyes-Montes / Applied Seismology Consultants ltd.William Flynn / Applied Seismology Consultants ltd.

EDZ Study Area in ONK-TKU-3620:Louhintavauriotutkimukset ONK-TKU-3620:ssa liittyenEDZ tutkimusalueen lohkosahauksiin

Pekka Kantia / GeofconEetu Pussinen / GeofconEero Heikkinen / Pöyry Finland Oy

EDZ Study Area in ONK-TKU-3620:Demonstraatioalueen louhintavauriotutkimukset EDZGPR -menetelmällä vuosina 2010-2015

Pekka Kantia / Geofcon

EDZ Study Area in ONK-TKU-3620:Association Analysis of Petrophysical and RockMechanics Data

Risto Kiuru / Pöyry Finland Oy

EDZ Study Area in ONK-TKU-3620:Determination of Seismic Wave Velocities at Six LoadLevels, Petrophysical and Rock Mechanical Properties ofDrill Core Specimens

Lars Jacobsson / SP Technical Research Institute of SwedenGunnar Kjell / SP Technical Research Institute of SwedenLinus Brander / SP Technical Research Institute of SwedenRisto Kiuru / Pöyry Finland Oy

EDZ Study Area in ONK-TKU-3620:Discrete Fracture Network Based Modelling ofMicrocrack System in Drill Core Specimens andComparisons with Petrophysical Measurements

Dorottya Kovács / University of SzegedGergely Dabi / University of SzegedTivadar M. Toth / University of SzegedLars Jacobsson / SP Technical Research Institute of SwedenRisto Kiuru / Pöyry Finland Oy

EDZ Study Area in ONK-TKU-3620:Petrophysical, rock mechanics and petrographictesting and analysis conducted on dril l corespecimens between 2014 and 2016

Risto Kiuru / Pöyry Finland OyEero Heikkinen / Pöyry Finland OyLars Jacobsson / SP Technical Research Institute of SwedenDorottya Kovács / University of Szeged

EDZ Study Area in ONK-TKU-3620:Flow simulations

Sven Follin / Golder Associates OyMark Cottrell / Golder Associates OyThomas Bym / Golder Associates OyRita Kamera / Golder Associates Oy

EDZ Study Area in ONK-TKU-3620:Integrated modelling

Noora Riihiluoma / Posiva OySven Follin / Golder Associates OyJohannes Suikkanen / Posiva OyEero Heikkinen / Pöyry Finland OyPekka Kantia / GeofconSanna Mustonen / Posiva Oy

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1.2 Site geology

The excavation damaged zone study area in ONK-TKU-3620 is located at a depth ofapproximately 345 meters along the ONKALO access tunnel (Figure 1-1). ONK-TKU-3620 is approximately 50 m long by 10 m wide, oriented north-south and tilted upwardsat 5 % angle (Mustonen et al., 2010).

Figure 1-1: Location of ONK-TKU-3620 in ONKALO marked with red circle. Viewedfrom south. Image courtesy of Posiva Oy.

The study area at the south end of ONK-TKU-3620 is approximately 9 meters by 2 metersin size and contains 30 vertical drill holes, on average 1.3 m deep, grouped in a regularsquared lattice each ca. 1 m from the nearest neighbouring holes. In addition, 14 moredrill holes varying in length between approximately 1.2 and 1.8 m were drilled indirections selected based on local geology. The location of the study area in ONK-TKU-3620, and the locations of the drill holes sampled in this investigation are shown in Figure1-2. In Figures 1-2 and 1-3 the modelled volume extends approximately 0.5 m beyondthe study area volume in the horizontal directions, and to approximately twice the depth.

The south end of the study area is dominated by gneissic specimens, mostly veined gneiss(VGN) with some inclusions of diatexitic gneiss (DGN). The north end of the study areaconsists mainly of granitic pegmatoid (PGR) (Koittola, 2014). Based on the 3Dlithological model of Koittola (2014), the gneissic rocks make up approximately 63 % ofthe volume, whereas the pegmatoids account for the remaining 37 %. The rock mass ingeneral was considered structurally sound, with only minor natural fracturing (Koittola,2014), making it suitable for studying the excavation induced damage. Lithological modelof the study area and its vicinity with the locations of all drill holes is shown in Figure 1-3.

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Figure 1-2: Location of the excavation damaged zone study area in ONK-TKU-3620 inONKALO. Locations of the drill holes are marked in orange (Set 1a), grey (Set 1b) andpurple (Set 2). Red volume is granitic pegmatoid, light blue is veined gneiss. Dimensionsare in meters. Note that the modelled volume extends approximately 0.5 m beyond thestudy area volume in the horizontal directions, and to approximately twice the depth.Image courtesy of Jouni Valli, RMCF Oy.

Figure 1-3: Lithological model of the excavation damaged zone study area in ONK-TKU-3620 in ONKALO. Red is granitic pegmatoid, light blue is veined gneiss. Paths of drillholes are marked with orange (Set 1), purple (Set 2) and black (not sampled in this study).Dimensions are in meters. Note that the modelled volume extends approximately 0.5 mbeyond the study area volume in the horizontal directions, and to approximately twice thedepth. Image courtesy of Jouni Valli, RMCF Oy.

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2 SPECIMENS AND PREPARATION

Three sets of specimens were taken from the EDZ study area in ONK-TKU-3620. Twospecimen sets were further divided into subsets 1a/2a and 1b/2b. Set 1 specimens weresubjected to basic petrophysical analysis, and for subset 1a S-wave velocity was alsomeasured. Set 2 specimens were subjected to rock mechanical testing, as well as somebasic petrophysical tests. Set 3 specimens were subjected to basic petrophysical analysis,and in addition resistivity and P-wave velocity were measured with tap water saturatedspecimens, and from dry specimens to study the effects of saturation water salinity.Microfracture network analysis was carried out on 16 out of 20 Set 1a specimens.

2.1 Overview of the campaign

All Sets 1, 2a and 3 specimens were tested for basic petrophysics including measurementsof density, porosity, electrical resistivity, magnetic susceptibility, remanentmagnetisation and unloaded P-wave velocity. For all of the Sets 1a and 2a specimens and53 of the 60 Set 1b specimens, relative permittivity was also measured. For Set 3specimens, resistivity and P-wave velocity were additionally determined after saturationin fresh tap water, and from dry specimens.

Set 1a specimens were sent to further analysis for determination of unloaded S-wavevelocity. Density, porosity and P-wave velocity were remeasured. 16 of the Set 1aspecimens were analysed to determine microfracture network parameters.

For Set 2a specimens, Brazilian test for indirect tensile strength was conducted, and inconjunction with the test density and porosity were remeasured. One of the specimens,EDZ KM4 BR broke during the second preparation cycle and is missing the relativepermittivity and Brazilian test results.

Due to the requirements of the UCS testing, Set 2b specimens were too large for some ofthe basic petrophysics. For Set 2b specimens, density, porosity and relative permittivitywere determined, while electrical resistivity, magnetic susceptibility and remanentmagnetisation were not. In addition, P- and S-wave velocities were measured in threeorthogonal directions under uniaxial load levels of 0.1 MPa (nearly unloaded), 2.5 MPa,5.0 MPa, 10.0 MPa, 15.0 MPa and 20.0 MPa covering the expected in situ pressures downto disposal level, while ensuring the specimens are not physically damaged prior tomechanical testing. After the non-destructive tests, Set 2b specimens were subjected touniaxial compressive strength test.

A summary of the tests carried out and properties calculated, on each specimen subset isshown in Table 2-1. Full listing of specimens with tested properties, as well as figures onthe spatial distribution of the specimens can be found in Kiuru (2016).

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Table 2-1: Tested and calculated properties for each specimen set, and relevant referenceto the other reports on the campaign.

Set 3PROPERTIES MEASURED 1a 1b 2a 2b 3 ReferenceDensity X X X X X Kiuru, 2016; Jacobsson et al., 2016bPorosity X X X X X Kiuru, 2016; Jacobsson et al., 2016bMagnetic susceptibility X X X X Kiuru, 2016Remanent magnetisation X X X X Kiuru, 2016Electrical resistivity, R0.1, R10 and R500* X X X X1 Kiuru, 2016Relative permittivity X X2 X3 X Kiuru, 2016P-wave velocity, unloaded X X X X X1 Kiuru, 2016; Jacobsson et al., 2016bS-wave velocity, unloaded X X Kiuru, 2016; Jacobsson et al., 2016bP- and S-wave velocities, loaded X Jacobsson et al., 2016b; Kiuru, 2016Crack initiation stress X4 Jacobsson et al., 2016b; Kiuru, 2016Crack damage stress X5 Jacobsson et al., 2016b; Kiuru, 2016Uniaxial compressive strength X Jacobsson et al., 2016b; Kiuru, 2016Poisson’s ratio (from UCS) X Kiuru, 2016; Jacobsson et al., 2016bYoung’s modulus (from UCS) X Kiuru, 2016; Jacobsson et al., 2016bTensile strength X3 Jacobsson et al., 2016b; Kiuru, 2016Fracture geometry X6 Kovacs et al., 2016PROPERTIES CALCULATEDQ-ratio X X X X Kiuru, 2016IP estimates, PL and PT** X X X X1 Kiuru, 2016Theoretical radar velocity X X2 X3 X Kiuru, 2016P/S-ratio, unloaded X X Kiuru, 2016Acoustic impedance, unloaded X X X X X1 Kiuru, 2016Shear impedance, unloaded X X Kiuru, 2016Poisson impedance, unloaded X X Kiuru, 2016Poisson’s ratio, unloaded X X Kiuru, 2016Lame’s first parameter, unloaded X X Kiuru, 2016Shear modulus, unloaded X X Kiuru, 2016P-wave modulus, unloaded X X X X X1 Kiuru, 2016Bulk modulus, unloaded X X Kiuru, 2016Young’s modulus, unloaded X X Kiuru, 2016Elastic parameters***, loaded X Kiuru, 2016; Jacobsson et al., 2016bElastic parameters***, from UCS X Kiuru, 2016; Jacobsson et al., 2016bP- and S-wave velocities, from UCS X Kiuru, 2016CI/UCS –ratio X4 Kiuru, 2016CD/UCS –ratio X5 Kiuru, 2016

Set 1 Set 2

1Also saturated in tap water, and on dry specimens2Not specimens EDZ101-EDZ1073Not specimen KM4 BR4Not specimen KM26 UCS5Not specimens KM15 UCS, KM23 UCS and KM26 UCS6Not specimens EDZ111, EDZ113, EDZ115 and EDZ160*R0.1 is measurement at 0.1 Hz, R10 at 10 Hz and R500 at 500 Hz**PL and PT values corresponding to resistivities at frequencies 0.1 Hz, 10 Hz and 500 Hz***In this by, elastic parameters means the combination of P/S ratio, Acoustic impedance, Shear impedance, Poisson impedance, Poisson's ratio,Lame's first parameters, Shear modulus, P-wave modulus, Bulk modulus and Young's modulus

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2.2 Specimen selection

1) Specimens should be from as close as possible to the tunnel floor in order to assessthe excavation damage effect

2) Specimens should represent the rock types as well as possible3) Distribution of rock types should represent the geology of the study area4) Specimens from each hole should create a sequence as uniform as possible,

extending to a reasonable depth below the tunnel floor (beyond the expected reachof EDZ)

Between 5 and 10 intact specimens with minimal fracturing were taken from each holeand their centre depths varied between 0.054 m and 0.79 m from the tunnel floor. Thebest sequences of veined gneiss (10 pcs) and granitic pegmatoid (10 pcs) were selectedfor Set 1a, bearing in mind the geological representativeness. Sampling of Set 1specimens is illustrated in Figure 2-1 a.

Set 2 specimens were selected to represent veined gneiss and granitic pegmatoid as theseare the dominant rock types in the study area. Ideally, specimens were chosen so that twosub specimens (2a for Brazilian test and 2b for UCS test) could be taken from the samepiece of the core. Gneissic specimens were selected so that half of them were taken alongthe foliation and the other half perpendicular to it. Between 1 and 3 pairs of specimenswere taken from each hole, and their centre depths varied between 0.225 m and 1.64 mfrom the study area surface along the drillhole. Sampling of Set 2 specimens is illustratedin Figure 2-1 b and c.

Set 3 specimens were selected to include the two dominant rock types in the study areaand chosen so that they complement the specimens selected for Set 1. Sampling of Set 3specimens is illustrated in Figure 2-1 a.

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Figure 2-1: Sampling logic used in this study. a) Set 1 specimens were selected asuniformly as possible starting from the top but avoiding core loss and large fractures(illustrated by red lightning bolts). Set 3 specimens were selected to continue the sequenceof Set 1 specimens deeper. b) Ideally, Set 2 specimens were taken from one uniform pieceof core. c) If this was not possible, Set 2a (50 mm) and 2b (180 mm) specimens were takenfrom as close as possible to each other.

2.3 Overview of the specimens

Specimen set 1 contains 80 specimens from 12 drill cores and was further divided intosubsets 1a of 20 specimens, and 1b of 60 specimens. Drill holes included were ONK-SH31 to ONK-SH39, ONK-SH44, ONK-SH45 and ONK-SH49, and their lengths variedfrom 1.27 m to 1.33 m. The rock types present in the specimens are veined gneiss (39pcs, 49 %), diatexitic gneiss (13 pcs, 16 %) and granitic pegmatoid (28 pcs, 35 %).

Specimen set 2 contains 52 specimens from 14 drill cores and was further divided intosubsets 2a of 26 specimens, and 2b of 26 specimens. Drill holes included were ONK-SH228 to ONK-SH233 and ONK-SH235 to ONK-SH242, and their lengths varied from1.21 m to 1.82 m. Rock types present in the specimens are veined gneiss (20+20 pcs, 77%) and granitic pegmatoid (6+6 pcs, 23 %).

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Specimen set 3 contains 9 specimens from 4 drill cores. Drill holes included were ONK-SH32, ONK-SH37, ONK-SH45 and ONK-SH49, and their lengths varied from 1.27 to1.30 meters. Rock types present in the specimens were veined gneiss (6 pcs, 67 %) andgranitic pegmatoid (3 pcs, 33 %). Spatial distribution of the sampled drill holes is shownin Figure 2-2.

Figure 2-2: Horizontal spatial distribution of sampled drill holes (ONK-SH[NUMBER])in the EDZ study area. Drill holes are marked with orange (Set 1), purple (Set 2) andgrey (not sampled in this study). Dimensions are in meters. Image courtesy of Jouni Valli,RMCF Oy.

2.4 Specimen preparation

Specimens were initially cut to the correct length at the Finnish Geological Survey(GTK), to approximately 50 mm long for sets 1, 2a and 3, and to approximately 180 mmlong for Set 2b. Ideally, Set 2a and 2b specimens would have been prepared from one 230mm long specimen. Due to rock fracturing this was not always possible, and twospecimens of 50 mm and 180 mm were taken as close to each other as possible. Thenominal diameter of the specimens was 68 mm.

The ends of the specimens were grinded to ensure good contacts for electrical resistivityand P-wave velocity measurements. After initial preparations at GTK, specimens werephotographed and foliation angles measured. Set 2 specimens were prepared again at SPTechnical Research Institute of Sweden (SP) to meet the higher standards of theInternational Society of Rock Mechanics (ISRM) Suggested Methods for Brazilian(ISRM, 1978) and UCS tests (ISRM, 1979), as well as to ensure good contact for the S-wave velocity measurements. Details of the tolerances as specified by ISRM can be foundin Kiuru (2016) and Jacobsson et al. (2016b). Set 2 specimens were photographed by SPthree times: on arrival, after preparation and after testing. Sampling and the resultingspecimens are illustrated in Figures 2-3 and 2-4.

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Figure 2-3: Sampling of Set 1 and Set 3 specimens, example shown for specimen EDZ155.Sampling here corresponds to Figure 2-1 a). A: Core selected for specimens EDZ155-159 with markings for the individual specimens. B: Specimen EDZ155.

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Figure 2-4: Sampling of Set 2 specimens, example shown for specimen KM2. Samplinghere corresponds to that of Figure 2-1 b). A: Core selected for specimen KM2 withmarkings for the corresponding specimens KM2 BR (2a) and KM2 UCS (2b). B:Specimen KM2 BR. C: Specimen KM2 UCS.

In a deviation from the ISRM suggested methods, all specimens were saturated in dilutedsaline water (collected from ONK-KR16 in ONKALO) instead of typical tap or distilledwater. The reason for this deviation was to try and get a better estimate of the physicalproperties of the rock mass in situ to support geophysical interpretation. The ONKALOwater was diluted with ion-exchanged water to an electrical conductivity value ofapproximately 1240 mS/m, corresponding to typical measured salinity values atOlkiluoto, and a TDS content of approximately 7.56 g/l (based on the Olkiluotogroundwater salinity model by Ruotsalainen et al. (2000)). Specimens were fullysubmerged in the saturation water for a period of at least two weeks to ensure fullsaturation. Saturation was done in normal laboratory temperature and pressure conditions.

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3 PETROPHYSICAL TESTING

The specimens were subjected to extensive petrophysical testing as described in 2.1.Density, porosity and unloaded P-wave velocity were determined several times by twoindependent operators, GTK in Finland and SP in Sweden. Resistivity, relativepermittivity, magnetic susceptibility and remanent magnetisation were measured. P- andS-wave velocities were determined not only unloaded, but also at six levels of axial stressand in three dimensions. From the measured parameters, a wide array of derivedparameters was calculated, including elastic parameters and different indicative ratios.This report summarises only the key details of the measurements and results. For detailsof which specimen groups were subjected to each type of test, see Table 2-1. For moredetails on the measurements and results see Kiuru (2016) and Jacobsson et al. (2016b).

3.1 Overview

Density and porosity were determined by two different individual operators as describedabove. Method used was based on Archimedes’ principle, and uses three weightings ofthe specimens (dry in air, saturated in air, saturated in water) to determine the pore spaceand bulk volumes, which are then used to calculate the density and porosity when densityof the saturation fluid is known. For details, see Kiuru (2016).

Electrical resistivity of the specimens was measured by GTK using their house-builtgalvanic 2-point measurement system with wet electrodes and saturated specimens.Measurements were done in three frequencies: 0.1 Hz, 10 Hz and 500 Hz. For details, seeKiuru (2016).

Relative dielectric permittivity was measured using an Adek Percometer v.7 with asurface probe. Both the top and the bottom of the specimen were measured three times,averaged out in the calculation, and corrected with a standard measurement. For details,see Kiuru (2016).

Unloaded P-wave velocity was measured by both GTK and SP, unloaded S-wave velocityby SP only. All velocities were measured in the axial direction of the specimens. Theunloaded P-wave velocity measurements at GTK were done using sonar elements with apulse frequency of approximately 1 MHz. The saturated specimens were measuredsubmerged. For details, see Kiuru (2016) and Jacobsson et al. (2016b).

SP measurements of P- and S-wave velocities were conducted according to ASTMInternational standard D 2845-00 (ASTM International, 2004). Pulse generator, samplingdevice and transducers were manufactured by GCTS. The transducers had a resonantfrequency of 200 kHz and included both P- and S-crystals. Pulse used was a 130 V / 5 µssquare wave. Velocities were measured in three orthogonal directions at axial loads of 0.1MPa, 2.5 MPa, 5 MPa, 10 MPa, 15 MPa and 20 MPa generated by an Instron hydraulictesting rig. For details, see Jacobsson et al. (2016b).

16

3.2 Key results

The key results of the petrophysical testing are presented here as descriptive statistics foreach specimen set. The process involved is shortly described in Section 5.1. Set 1a resultscan be found in Table 3-1, Set 1b results in Table 3-2, Set 2a results in Table 3-3, Set 2bresults in Tables 3-4 and 3-5, and Set 3 results in Table 3-6. Measured values werecompared to previous data from Olkiluoto (results prior to 2009 summarised in Aaltonenet al., 2009) when possible, and found to be generally in good agreement. An example isshown in Figure 3-1, and all plots can be found in Kiuru (2016).

Hardly any depth dependencies were present in the measured data: these were mostlylimited to resistivity, S-wave velocity, and elastic parameters that are derived from S-wave velocity. The effects seemed to be mostly limited to pegmatoid specimens.

The theory behind the calculated properties, including static and dynamic elasticparameters, as well as full results for each specimen, can be found in Kiuru (2016).Results of tests done with different levels of axial loading and in different directions arenot included in this summary and can be found in Kiuru (2016).

Figure 3-1: Example of comparisons of petrophysical dataset to earlier results fromOlkiluoto. Cross plot of density and magnetic susceptibility. Previous data with larger,mostly transparent symbols. Current dataset with smaller, opaque symbols. Colourrepresents rock type: red is PGR, light blue is VGN and darker blue is DGN. Adaptedfrom Kiuru (2016).

17

Table 3-1: Descriptive statistics of the tested and calculated properties for Set 1aspecimens. Min is minimum value, Q1 is first quartile value, Med is median value, Q3 isthird quartile value, Max is maximum value, IQR is interquartile range, SD is standarddeviation and N is sample size. Rock types as described earlier.

PGR: N = 10, VGN: N = 10PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2608 2710 2617 2723 2622 2734 2622 2732Porosity [%] 0.37 0.27 0.41 0.39 0.44 0.43 0.47 0.48Magnetic susceptibility [10 -6 SI] 20 121 32 244 33 287 36 277Remanent magnetisation [mA/m] 7 8 12 17 20 20 18 20Q-ratio [-] 5.2 0.6 9.3 1.3 11.1 1.9 13.4 2.0Resistivity, R0.1 [Ωm] 1810 5310 5810 9893 6810 10600 6568 11371Resistivity, R10 [Ωm] 1850 4870 5715 8575 6750 10300 6486 10605Resistivity, R500 [Ωm] 1800 4210 5563 7413 6490 9685 6235 9669IP Estimate, PL [%] 0.0 2.8 0.2 4.7 1.2 6.8 1.2 7.3IP Estimate, PT [%] 0.0 8.4 3.8 10.5 4.9 15.0 4.3 16.1Relative permittivity [-] 5.3 5.4 5.5 6.0 5.6 6.4 5.7 6.5Theoretical radar velocity [m/µs] 121 110 125 113 127 119 126 118P-wave velocity [m/s] 5239 5255 5685 5440 5861 5530 5800 5592S-wave velocity [m/s] 3156 2646 3203 2959 3222 3195 3263 3161P/S-ratio [-] 1.55 1.65 1.77 1.67 1.80 1.72 1.78 1.78Acoustic Impedance [MPa∙s/m] 13.7 14.4 14.9 14.9 15.4 15.1 15.2 15.3Shear Impedance [MPa∙s/m] 8.3 7.2 8.4 8.1 8.4 8.7 8.6 8.6Poisson Impedance [MPa∙s/m] 1.2 2.3 3.1 2.4 3.3 2.7 3.1 3.1Poisson's ratio [-] 0.14 0.21 0.26 0.22 0.28 0.24 0.26 0.26Shear modulus [GPa] 26.2 19.1 27.0 24.1 27.2 27.9 27.9 27.6P-wave modulus [GPa] 71.7 75.9 84.9 81.2 90.0 83.4 88.4 85.6Bulk modulus [GPa] 31.8 41.9 49.6 44.3 52.6 48.4 51.1 48.8Young's modulus [GPa] 66.7 51.4 69.6 62.0 70.1 69.1 70.4 69.0Lame's first parameter [GPa] 11.8 22.9 31.7 25.6 34.3 27.0 32.5 30.4

PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2629 2746 2631 2748 12 24 8 15Porosity [%] 0.47 0.56 0.75 0.70 0.06 0.17 0.12 0.14Magnetic susceptibility [10 -6 SI] 44 347 53 355 13 103 11 78Remanent magnetisation [mA/m] 23 24 27 32 11 7 7 7Q-ratio [-] 18.0 2.3 29.1 4.0 8.7 1.0 7.6 1.0Resistivity, R0.1 [Ωm] 8383 13550 10500 19500 2573 3658 2923 4295Resistivity, R10 [Ωm] 8173 12775 10400 18300 2458 4200 2869 4160Resistivity, R500 [Ωm] 7785 11750 9910 16700 2223 4338 2716 3960IP Estimate, PL [%] 2.1 9.0 2.6 14.7 1.9 4.3 1.0 3.8IP Estimate, PT [%] 5.6 20.8 7.6 26.5 1.8 10.2 2.4 6.4Relative permittivity [-] 5.8 7.0 6.2 7.4 0.3 1.1 0.2 0.7Theoretical radar velocity [m/µs] 128 123 130 129 3 10 3 6P-wave velocity [m/s] 5970 5614 6060 6088 285 174 250 266S-wave velocity [m/s] 3318 3304 3434 3687 115 345 92 341P/S-ratio [-] 1.85 1.84 1.90 2.07 0.08 0.17 0.11 0.16Acoustic Impedance [MPa∙s/m] 15.7 15.3 15.9 16.7 0.7 0.4 0.7 0.7Shear Impedance [MPa∙s/m] 8.7 9.0 9.0 10.1 0.3 0.9 0.2 0.9Poisson Impedance [MPa∙s/m] 3.7 3.5 4.1 4.8 0.6 1.1 0.9 1.0Poisson's ratio [-] 0.29 0.29 0.31 0.35 0.03 0.07 0.05 0.05Shear modulus [GPa] 28.8 29.9 30.8 37.2 1.9 5.8 1.5 5.9P-wave modulus [GPa] 93.5 85.9 96.6 101.4 8.5 4.7 7.6 8.1Bulk modulus [GPa] 57.0 51.8 61.0 59.0 7.3 7.4 9.0 5.9Young's modulus [GPa] 71.7 73.1 73.7 90.1 2.1 11.1 2.1 12.4Lame's first parameter [GPa] 38.8 33.7 43.2 45.4 7.1 8.1 9.8 8.1

Min Q1 Med Mean

Q3 Max IQR SD

18

Table 3-2: Descriptive statistics of the tested and calculated properties for Set 1bspecimens. Min is minimum value, Q1 is first quartile value, Med is median value, Q3 isthird quartile value, Max is maximum value, IQR is interquartile range, SD is standarddeviation and N is sample size. Rock types as described earlier.

PGR: N = 18, VGN: N = 29, DGN: N = 13PROPERTY PGR VGN DGN PGR VGN DGN PGR VGN DGN PGR VGN DGNDensity [kg/m3] 2600 2690 2695 2628 2711 2706 2633 2732 2720 2638 2730 2724Porosity [%] 0.24 0.18 0.24 0.27 0.22 0.28 0.32 0.24 0.41 0.33 0.32 0.40Magnetic susceptibility [10-6 SI] 7 166 203 40 232 244 48 302 258 67 292 282Remanent magnetisation [mA/m] 7 4 5 17 15 16 20 20 23 18 21 23Q-ratio [-] 1.68 0.26 0.33 4.03 1.23 1.19 8.98 1.61 1.85 13.69 1.85 2.19Resistivity, R0.1 [Ωm] 4780 7190 9620 5628 13500 14700 6785 18300 17500 7342 23958 19632Resistivity, R10 [Ωm] 4640 6990 9010 5493 12300 12800 6625 17000 17100 7221 21816 18147Resistivity, R500 [Ωm] 4500 6640 8270 5330 10700 10700 6435 15300 15800 6965 18712 15964IP Estimate, PL [%] 0.0 0.0 2.3 1.6 5.0 6.3 2.1 7.4 7.3 2.0 7.6 7.8IP Estimate, PT [%] 0.0 4.7 9.7 4.5 13.1 14.3 5.1 17.3 17.3 5.1 18.4 18.4Relative permittivity [-] 5.2 5.6 5.6 5.4 6.0 5.7 5.5 6.2 5.8 5.6 6.2 5.9Theoretical radar velocity [m/µs] 118 113 119 125 119 123 127 120 124 127 120 124P-wave velocity [m/s] 5463 5491 5477 5820 5673 5549 5909 5805 5670 5876 5763 5672Acoustic Impedance [MPa∙s/m] 14.6 15.1 14.9 15.2 15.5 15.2 15.6 15.8 15.4 15.5 15.7 15.5P-wave modulus [GPa] 81.0 82.9 81.6 88.8 87.4 84.3 92.0 91.5 86.7 91.2 90.8 87.7

PROPERTY PGR VGN DGN PGR VGN DGN PGR VGN DGN PGR VGN DGNDensity [kg/m3] 2640 2746 2738 2714 2775 2777 12 35 32 26 23 22Porosity [%] 0.39 0.39 0.49 0.50 0.81 0.64 0.13 0.17 0.21 0.08 0.15 0.13Magnetic susceptibility [10-6 SI] 64 333 319 259 401 431 24 101 75 64 69 64Remanent magnetisation [mA/m] 22 24 27 24 42 48 5 9 11 5 9 12Q-ratio [-] 12.88 2.29 2.74 75.72 6.11 5.02 8.85 1.06 1.55 17.81 1.10 1.38Resistivity, R0.1 [Ωm] 8013 34600 23700 14600 80300 36300 2385 21100 9000 2416 15175 7586Resistivity, R10 [Ωm] 7898 31300 22700 14300 68600 33700 2405 19000 9900 2319 12999 7164Resistivity, R500 [Ωm] 7645 25800 19800 13700 50400 28000 2315 15100 9100 2184 9851 6021IP Estimate, PL [%] 2.8 9.2 9.7 4.0 16.0 16.3 1.2 4.2 3.4 1.2 4.0 3.6

IP Estimate, PT [%] 6.3 22.9 21.6 10.0 37.2 30.1 1.7 9.8 7.2 2.6 7.9 5.4Relative permittivity [-] 5.7 6.4 5.9 6.5 7.0 6.4 0.3 0.4 0.2 0.3 0.4 0.3Theoretical radar velocity [m/µs] 129 123 126 131 127 126 4 4 2 3 3 3P-wave velocity [m/s] 5951 5855 5769 6262 6030 5931 131 182 220 204 146 138Acoustic Impedance [MPa∙s/m] 15.6 16.0 15.6 16.4 16.5 16.1 0.4 0.5 0.4 0.5 0.4 0.3P-wave modulus [GPa] 92.8 93.6 89.9 103.0 99.3 95.5 4.1 6.3 5.6 6.0 4.7 4.1

Min Q1 Med Mean

Q3 Max IQR SD

19

Table 3-3: Descriptive statistics of the tested and calculated properties for Set 2aspecimens. Min is minimum value, Q1 is first quartile value, Med is median value, Q3 isthird quartile value, Max is maximum value, IQR is interquartile range, SD is standarddeviation and N is sample size. Rock types as described earlier.

PGR: N = 6, VGN: N = 20PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2595 2658 2607 2698 2615 2712 2620 2714Porosity [%] 0.33 0.22 0.37 0.28 0.43 0.33 0.40 0.34Magnetic susceptibility [10 -6 SI] 20 204 25 268 37 301 35 313Remanent magnetisation [mA/m] 12 4 13 12 18 22 18 22Q-ratio [-] 6.4 0.3 9.5 0.8 10.0 1.8 15.1 1.8Resistivity, R0.1 [Ωm] 6870 5620 7213 10650 7855 17100 8075 28646Resistivity, R10 [Ωm] 6650 5050 6983 9820 7530 15750 7753 24841Resistivity, R500 [Ωm] 6430 4480 6728 8908 7190 14200 7442 19836IP Estimate, PL [%] 3.1 4.9 3.3 7.3 3.8 9.4 3.9 10.2IP Estimate, PT [%] 6.4 10.6 6.7 15.4 7.5 20.2 7.7 22.6Relative permittivity [-] 5.2 5.4 5.3 5.5 5.3 5.6 5.3 5.8Theoretical radar velocity [m/µs] 129 117 130 123 130 126 130 125P-wave velocity [m/s] 5764 5504 5807 5565 5830 5700 5856 5740Acoustic Impedance [MPa∙s/m] 15.0 14.8 15.2 15.3 15.4 15.4 15.3 15.6P-wave modulus [GPa] 86.5 81.4 88.3 85.2 89.9 87.8 89.9 89.5

PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2619 2724 2670 2771 12 26 26 29Porosity [%] 0.44 0.39 0.45 0.63 0.07 0.11 0.05 0.09Magnetic susceptibility [10 -6 SI] 45 360 47 431 20 92 12 67Remanent magnetisation [mA/m] 23 30 24 62 9 18 5 14Q-ratio [-] 21.1 2.5 29.9 4.2 11.5 1.7 9.7 1.1Resistivity, R0.1 [Ωm] 8775 41600 9790 101000 1563 30950 1149 28064Resistivity, R10 [Ωm] 8415 36700 9300 87600 1433 26880 1045 22991Resistivity, R500 [Ωm] 8065 28850 8910 63000 1338 19943 980 15966IP Estimate, PL [%] 4.5 11.8 5.0 22.3 1.2 4.4 0.8 4.2IP Estimate, PT [%] 8.7 28.1 9.5 45.5 1.9 12.7 1.3 9.8Relative permittivity [-] 5.3 6.0 5.4 6.6 0.1 0.4 0.1 0.4Theoretical radar velocity [m/µs] 131 128 131 129 1 5 1 4P-wave velocity [m/s] 5899 5867 5993 6099 93 302 85 195Acoustic Impedance [MPa∙s/m] 15.5 15.9 15.6 16.5 0.3 0.6 0.2 0.5P-wave modulus [GPa] 91.4 93.3 93.2 100.9 3.1 8.1 2.5 5.9

Mean

Q3 Max IQR SD

Min Q1 Med

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Table 3-4: Descriptive statistics of the tested and calculated properties for Set 2bspecimens. Min is minimum value, Q1 is first quartile value, Med is median value, Q3 isthird quartile value, Max is maximum value, IQR is interquartile range, SD is standarddeviation and N is sample size. Rock types as described earlier.

PGR: N = 6, VGN: N = 20PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2622 2722 2624 2727 2629 2747 2629 2745Porosity [%] 0.30 0.22 0.33 0.26 0.35 0.28 0.36 0.33Relative permittivity [-] 5.0 5.4 5.2 5.9 5.2 6.3 5.2 6.4Theoretical radar velocity [m/µs] 129 108 130 116 131 119 131 119P-wave velocity [m/s] 5407 5173 5447 5304 5497 5465 5477 5470S-wave velocity [m/s] 3049 2842 3061 3043 3071 3140 3078 3134P/S-ratio [-] 1.73 1.71 1.76 1.73 1.79 1.74 1.78 1.75Acoustic Impedance [MPa∙s/m] 14.2 14.2 14.3 14.6 14.4 15.0 14.4 15.0Shear Impedance [MPa∙s/m] 8.0 7.7 8.1 8.4 8.1 8.6 8.1 8.6Poisson Impedance [MPa∙s/m] 2.6 2.5 2.9 2.8 3.1 2.8 3.0 2.9Poisson's ratio [-] 0.25 0.24 0.26 0.25 0.27 0.25 0.27 0.26Shear modulus [GPa] 24.4 22.0 24.7 25.5 24.8 27.0 24.9 27.0P-wave modulus [GPa] 76.8 73.4 78.1 77.5 79.4 81.8 78.9 82.2Bulk modulus [GPa] 43.1 40.0 44.2 44.4 46.3 45.8 45.6 46.2Young's modulus [GPa] 62.4 56.6 62.8 63.3 62.9 67.7 63.2 67.8Lame's first parameter [GPa] 25.7 23.1 27.7 27.2 29.8 28.0 29.0 28.2

PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2633 2753 2637 2784 8 27 6 17Porosity [%] 0.39 0.33 0.41 0.77 0.06 0.07 0.04 0.14Relative permittivity [-] 5.3 6.7 5.4 7.7 0.1 0.7 0.2 0.7Theoretical radar velocity [m/µs] 132 123 134 129 2 7 2 6P-wave velocity [m/s] 5509 5622 5517 5816 62 319 46 197S-wave velocity [m/s] 3075 3232 3145 3316 14 189 34 122P/S-ratio [-] 1.80 1.75 1.81 1.83 0.04 0.02 0.03 0.03Acoustic Impedance [MPa∙s/m] 14.5 15.4 14.5 16.1 0.1 0.8 0.1 0.6Shear Impedance [MPa∙s/m] 8.1 8.9 8.3 9.2 0.0 0.5 0.1 0.4Poisson Impedance [MPa∙s/m] 3.1 3.0 3.2 3.2 0.3 0.2 0.2 0.2Poisson's ratio [-] 0.28 0.26 0.28 0.29 0.01 0.01 0.01 0.01Shear modulus [GPa] 24.9 28.6 26.1 30.5 0.2 3.1 0.6 2.1P-wave modulus [GPa] 79.8 86.8 79.9 93.9 1.7 9.3 1.3 6.1Bulk modulus [GPa] 47.0 48.7 47.3 53.2 2.8 4.2 1.8 3.6Young's modulus [GPa] 63.2 71.7 65.1 76.8 0.4 8.4 1.0 5.2Lame's first parameter [GPa] 30.6 29.5 31.1 32.9 2.9 2.3 2.2 2.6

Q3 Max IQR SD

Min Q1 Med Mean

21

Table 3-5: Descriptive statistics of the properties derived from UCS tests for Set 2bspecimens. Min is minimum value, Q1 is first quartile value, Med is median value, Q3 isthird quartile value, Max is maximum value, IQR is interquartile range, SD is standarddeviation and N is sample size. Rock types as described earlier.

PGR: N = 6, VGN: N = 20PROPERTY (FROM UCS) PGR VGN PGR VGN PGR VGN PGR VGNP-wave velocity [m/s] 5450 4654 5488 5402 5616 5578 5638 5670S-wave velocity [m/s] 3041 2514 3095 2732 3106 2758 3111 2795P/S-ratio [-] 1.73 1.65 1.76 1.91 1.82 2.02 1.81 2.03Acoustic Impedance [MPa∙s/m] 14.3 12.7 14.4 14.8 14.7 15.4 14.8 15.6Shear Impedance [MPa∙s/m] 8.0 6.9 8.1 7.5 8.2 7.6 8.2 7.7Poisson Impedance [MPa∙s/m] 2.7 2.0 2.9 3.9 3.3 4.7 3.3 4.7Poisson's ratio [-] 0.25 0.21 0.26 0.31 0.28 0.34 0.28 0.33Shear modulus [GPa] 24.3 17.2 25.2 20.5 25.4 20.9 25.5 21.5P-wave modulus [GPa] 78.3 59.1 79.0 80.1 82.8 85.7 83.6 88.8Bulk modulus [GPa] 43.4 34.9 45.7 50.4 48.7 58.1 49.7 60.2Young's modulus [GPa] 61.9 44.6 64.9 54.9 65.7 56.1 65.1 57.1Lame's first parameter [GPa] 26.0 18.1 28.5 36.1 32.3 44.4 32.7 45.9

PROPERTY (FROM UCS) PGR VGN PGR VGN PGR VGN PGR VGNP-wave velocity [m/s] 5774 6001 5872 6779 287 599 177 471S-wave velocity [m/s] 3140 2846 3172 3086 45 114 46 146P/S-ratio [-] 1.86 2.20 1.89 2.35 0.10 0.29 0.06 0.19Acoustic Impedance [MPa∙s/m] 15.2 16.4 15.5 18.7 0.8 1.6 0.5 1.3Shear Impedance [MPa∙s/m] 8.3 7.9 8.3 8.4 0.1 0.4 0.1 0.4Poisson Impedance [MPa∙s/m] 3.7 6.0 3.9 7.3 0.8 2.1 0.5 1.4Poisson's ratio [-] 0.30 0.37 0.31 0.39 0.03 0.06 0.02 0.05Shear modulus [GPa] 26.0 22.4 26.5 25.9 0.8 1.8 0.8 2.2P-wave modulus [GPa] 87.6 98.7 90.8 126.5 8.6 18.5 5.3 15.1Bulk modulus [GPa] 53.9 71.9 57.0 95.2 8.2 21.5 5.5 15.3Young's modulus [GPa] 66.2 60.7 66.5 65.7 1.3 5.8 1.7 5.0Lame's first parameter [GPa] 37.0 58.3 40.0 79.5 8.5 22.3 5.6 15.7

Q3 Max IQR SD

Min Q1 Med Mean

22

Table 3-6: Descriptive statistics of the tested and calculated properties for Set 3specimens. Min is minimum value, Q1 is first quartile value, Med is median value, Q3 isthird quartile value, Max is maximum value, IQR is interquartile range, SD is standarddeviation and N is sample size. Rock types as described earlier.

PGR: N = 3, VGN: N = 6PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2606 2727 2614 2735 2621 2738 2645 2741Porosity [%] 0.36 0.28 0.39 0.37 0.41 0.41 0.45 0.42Magnetic susceptibility [10 -6 SI] 17 278 19 302 21 315 84 316Remanent magnetisation [mA/m] 6 9 14 12 21 15 17 18Q-ratio [-] 2.4 0.7 4.8 0.9 7.1 1.3 14.5 1.4Resistivity, R0.1 [Ωm] 4620 4420 4715 5053 4810 7850 4753 7662Resistivity, R10 [Ωm] 4460 4130 4485 4660 4510 7120 4510 7030Resistivity, R500 [Ωm] 4270 3760 4300 4203 4330 6390 4317 6292Resistivity, tap water, R0.1 [Ωm] 13400 18600 16100 19875 18800 23900 17033 24883Resistivity, tap water, R10 [Ωm] 12600 16400 15100 18100 17600 21400 16167 22233Resistivity, tap water, R500 [Ωm] 11800 14500 14050 16575 16300 18900 15100 19567IP Estimate, PL [%] 3.5 6.6 4.3 7.0 5.2 7.7 5.1 8.1IP Estimate, PT [%] 6.3 13.8 7.9 16.8 9.6 17.5 9.1 17.5IP Estimate, tap water, PL [%] 3.2 8.1 4.6 9.2 6.0 11.0 5.2 10.6IP Estimate, tap water, PT [%] 9.0 15.1 10.5 19.4 11.9 22.9 11.4 21.0P-wave velocity [m/s] 5380 5226 5542 5289 5703 5425 5620 5395Acoustic Impedance [MPa∙s/m] 14.6 14.3 14.8 14.5 14.9 14.9 14.9 14.8P-wave modulus [GPa] 78.3 74.8 81.8 76.8 85.3 80.8 83.5 79.8P-wave velocity, tap water [m/s] 5420 5250 5615 5413 5810 5510 5700 5495Acoustic Impedance, tap water [MPa∙s/m] 14.7 14.4 14.9 14.8 15.2 15.1 15.1 15.1P-wave modulus, tap water [GPa] 79.5 75.5 84.0 80.4 88.5 83.1 85.9 82.8

PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2664 2747 2706 2758 50 12 54 11Porosity [%] 0.49 0.43 0.57 0.63 0.11 0.06 0.11 0.12Magnetic susceptibility [10 -6 SI] 118 330 215 352 99 27 113 26Remanent magnetisation [mA/m] 22 23 23 29 9 11 9 8Q-ratio [-] 20.6 1.8 34.0 2.2 15.8 0.9 17.1 0.6Resistivity, R0.1 [Ωm] 4820 9410 4830 11800 105 4358 116 2974Resistivity, R10 [Ωm] 4535 8508 4560 11000 50 3848 50 2723Resistivity, R500 [Ωm] 4340 7528 4350 9830 40 3325 42 2399Resistivity, tap water, R0.1 [Ωm] 18850 26725 18900 36700 2750 6850 3147 6718Resistivity, tap water, R10 [Ωm] 17950 23650 18300 32800 2850 5550 3109 5964Resistivity, tap water, R500 [Ωm] 16750 20475 17200 28300 2700 3900 2893 4880IP Estimate, PL [%] 5.9 8.9 6.6 10.5 1.6 2.0 1.6 1.5IP Estimate, PT [%] 10.6 19.1 11.6 20.1 2.7 2.3 2.7 2.3IP Estimate, tap water, PL [%] 6.2 11.9 6.4 12.8 1.6 2.8 1.7 1.9IP Estimate, tap water, PT [%] 12.6 23.2 13.3 23.5 2.2 3.9 2.2 3.5P-wave velocity [m/s] 5741 5482 5778 5549 199 193 211 130Acoustic Impedance [MPa∙s/m] 15.0 15.1 15.1 15.1 0.3 0.5 0.3 0.3P-wave modulus [GPa] 86.1 82.4 87.0 84.0 4.3 5.6 4.6 3.7P-wave velocity, tap water [m/s] 5840 5623 5870 5660 225 210 244 161Acoustic Impedance, tap water [MPa∙s/m] 15.3 15.4 15.3 15.5 0.3 0.6 0.3 0.4P-wave modulus, tap water [GPa] 89.1 87.0 89.8 87.4 5.2 6.6 5.6 4.8

Min Q1 Med Mean

Q3 Max IQR SD

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4 ROCK MECHANICS TESTING

In conjunction with the petrophysical testing, Set 2 specimens were subjected to rockmechanical testing. Set 2a specimens were subjected to Brazilian test for indirectdetermination of tensile strength and Set 2b specimens to uniaxial compressive strength(UCS) testing. The loading rig used for UCS testing was a GCTS load frame and differentfrom the one used with the measurements of P- and S-wave velocities under six levels ofaxial loading.

4.1 Overview

UCS testing was carried out by SP following ISRM suggested method (ISRM, 1979).Specimens were loaded in axial direction up to failure at loading rates of 0.5 MPa/s (or0.8 MPa/s for specimens EDZ KM1 UCS and KM2 UCS), using a GCTS hydraulictesting rig. Strain monitoring was done using four Kyowa strain gauge rosettes. Uniaxialcompressive strength was determined from the point of failure. Young’s modulus andPoisson’s ratio were determined as the secant values of the axial stress-strain curves andthe negative of the ratio of the slopes of the axial and diametric stress-strain curves,respectively, calculated between 40 % and 60 % of ultimate strength. Crack initiation (CI)and crack damage (CD) stresses were determined from acoustic emission measurements.For more details, see Jacobsson et al. (2016b).

Tensile strength testing was carried out by SP following ISRM suggested method (ISRM,1978). Specimens were loaded in the lateral direction at a rate of 200 N/s, using a Sintech20/D electro-mechanical test machine. For gneissic specimens, loading direction wasselected along foliation when possible. Monitoring was done using Kyowa strain gaugerosettes. Tensile strength was determined from the point of failure. All mechanical testswere carried out on saline water saturated specimens. For more details of the rockmechanics testing, see Jacobsson et al. (2016b).

4.2 Key results

The key results of the mechanical testing are presented here as descriptive statistics foreach specimen set. Brazilian test results for Set 2a specimens can be found in Table 4-1,and UCS test results including results derived from acoustic emission in Table 4-2.Visualisations, relevant theory and full results for each specimen can be found inJacobsson et al. (2016b) and Kiuru (2016).

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Table 4-1: Descriptive statistics for the mechanical tests done on Set 2a specimens. Minis minimum value, Q1 is first quartile value, Med is median value, Q3 is third quartilevalue, Max is maximum value, IQR is interquartile range, SD is standard deviation andN is sample size. Rock types as described earlier.

Table 4-2: Descriptive statistics for the mechanical tests done on Set 2b specimens. Minis minimum value, Q1 is first quartile value, Med is median value, Q3 is third quartilevalue, Max is maximum value, IQR is interquartile range, SD is standard deviation andN is sample size. Rock types as described earlier.

PGR: N = 6, VGN: N = 20PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2595 2658 2607 2698 2615 2712 2620 2714Porosity [%] 0.33 0.22 0.37 0.28 0.43 0.33 0.40 0.34Indirect tensile strength [MPa] 7.8 6.6 9.1 8.2 10.7 9.3 10.5 9.8

PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2619 2724 2670 2771 12 26 26 29Porosity [%] 0.44 0.39 0.45 0.63 0.07 0.11 0.05 0.09Indirect tensile strength [MPa] 11.6 11.0 13.4 15.3 2.6 2.8 2.1 2.3

Med MeanMin Q1

Q3 Max IQR SD

PGR: N = 6, VGN: N = 20PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2622 2722 2624 2727 2629 2747 2629 2745Porosity [%] 0.30 0.22 0.33 0.26 0.35 0.28 0.36 0.33Crack initiation stress [MPa] 41 24 45 36 50 51 60 46Crack damage stress [MPa] 117 66 134 77 140 83 137 87Uniaxial compressive strength [MPa] 135.2 74.6 139.6 88.7 152.3 99.6 155.1 99.8CI/UCS ratio [-] 0.29 0.20 0.30 0.38 0.33 0.49 0.37 0.47CD/UCS ratio [-] 0.86 0.67 0.87 0.87 0.89 0.89 0.89 0.87

PROPERTY PGR VGN PGR VGN PGR VGN PGR VGNDensity [kg/m3] 2633 2753 2637 2784 8 27 6 17Porosity [%] 0.39 0.33 0.41 0.77 0.06 0.07 0.04 0.14Crack initiation stress [MPa] 72 55 90 63 27 19 21 12Crack damage stress [MPa] 143 99 152 116 9 22 15 15Uniaxial compressive strength [MPa] 168.5 111.2 181.8 125.8 28.9 22.5 19.1 15.3CI/UCS ratio [-] 0.42 0.59 0.50 0.67 0.11 0.21 0.09 0.13CD/UCS ratio [-] 0.91 0.92 0.93 0.94 0.04 0.06 0.03 0.08

IQR SD

Min Q1 Med Mean

Q3 Max

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5 STATISTICAL ANALYSIS

In addition to the descriptive statistics presented in Section 4, petrophysical and rockmechanics data were analysed to compare the shapes and characteristics of distributionsbetween different samples. The data were also analysed together as cross plots betweenall pairs of variables, in order to identify anomalous specimens, which could be linked toexcavation damage. Additionally, association analysis was performed to find associationsbetween different properties and the depth of the specimen from the study area surface,to isolate the effects of excavation damage. Similar analysis was performed on the entiredataset, to find possible associations between other properties. Rock mechanics testing ismore resource intensive than many geophysical or petrophysical methods and findinglinks between the two would allow for faster, more cost-effective ways to estimate thestrength properties of rock mass.

As Set 2b specimens were too long for full petrophysical testing, results from adjacentSet 2a specimens were compared with the rock mechanical data from Set 2b specimens,to get an estimate of possible links. Such results were treated as “pseudo” data as they donot strictly represent the same specimen but are nevertheless from within severalcentimetres of each other.

5.1 Overview

For different specimen sets descriptive statistics, shown in the previous Sections’ results,were calculated. These included minimum, maximum, first and third quartile, and medianvalues (plotted), as well as standard deviation, interquartile distance (IQR) and samplesize N. The former were plotted as boxplots to help compare different samples. Alongwith the boxplots, kernel density estimates were plotted to illustrate the distribution shapeand to help compare between samples. Calculations were carried out in R using thealgorithms min, max, quantile, median, sd and density.default, plotting was done usingthe R library ggplot2. An example of the plots is shown in Figure 5-1. For more details,see Kiuru (2016).

Statistical analysis was carried out on samples comprised of subsets of different specimensets and their combinations. Different rock types were treated individually, and whenpossible, data were separated to depth groups at 0.2 m intervals. In some cases, the samplesize N would have been less than 4, and groups had to be combined. When applicable,data were separated by the level of applied load. When available, corresponding valuesfor each rock type were calculated based on previous Olkiluoto data and plotted similarlyfor comparison. It must be noted that some of the sample sizes are very small and havethe possibility to contain high relative errors. No plots were produced for all datacombined, as the rock types would likely mask any other effect.

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Figure 5-1: Example of visualisations made for the descriptive statistics. Densitystatistics separated by group. LEFT: Boxplots, box shows quartiles, line shows min andmax values, diamond shows a distance of 2σ from nearest quartile. RIGHT: Scaled kerneldensity estimates, rug plot shows individual observations. TOP TWO ROWS: colourrepresents rock type, red is PGR, light blue is VGN and darker blue is DGN. BOTTOMTWO ROWS: colour represents depth from study area surface, dark orange is 0.0-0.2 m,light orange is 0.2-0.4 m, lime is 0.4-0.6 m, bright green is 0.6-0.8 m, green is 0.8-1.2 mand dark green is 1.2+ m. Adapted from Kiuru (2016).

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To test for associations, Spearman rank correlation coefficients were calculated for allpairs of variables. The Spearman method is a non-parametric test of association that doesnot require a linear dependency but considers monotonic trend. It is less sensitive tooutliers than the typically used Pearson method, and better suited to test for associationsthat may be complex or non-linear. The testing was done for all possible samples,regardless of whether any trends were expected to be visible. This included all datacombined, separated by rock type, level of loading, depth, direction of measurement,specimen set and the combinations of these when the sample size was adequate.

For each test, correlation matrices were formed, and the Spearman rho values wererounded up to two decimals. From the matrices the values corresponding to trivial andknown dependencies were removed. To determine the significance of the associations,critical values for Spearman rho were calculated. The critical values were then formedinto a separate matrix, similar to the correlation matrices. The remainder of eachcorrelation matrix was then compared against the critical values, and associations that didnot exceed the critical value were removed. This left us with correlation matrices thatonly had unique, non-trivial associations that exceed the corresponding critical value.

These pairs of variables were then plotted as scatter plots. Due to the volume of data andinterest of the study, plotting was further limited to depth from surface and porosity asexplanatory variables, and to mechanical properties derived from the UCS and Braziliantests as explained variables. Dynamic and static variants of a parameter were plottedagainst each other when applicable, regardless of the level of association. The resultingscatter plots were visually inspected and selected for interpretation if one of the followingcriteria was met: 1) the data showed a clear trend for the entire dataset or one of the rocktypes; or 2) the data showed shallow specimens as clearly anomalous. In addition, it wasdemanded that the data did not only separate rock types. For more details of the statisticalanalysis, including the assumptions and restrictions, as well as all data associated withthe analysis, and the full plots, see Kiuru (2016).

5.2 Key results

An example of the scatterplots resulting from the analysis described shortly in Section5.1 can be seen in Figure 5-2. Most analysed pairs of variables showed general trendswith a wide spread, resulting in it being impossible to create quantitative models for theassociations. In most cases, the most useful observations turned out to be clearlyanomalous specimens that stand out from the rest of the data. These were typically fromshallow depths and could in some cases be linked to specimens with observed visibleEDZ features, such as induced fractures. An example is shown in Figures 5-2 and 5-3.

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Figure 5-2: Example of the created scatterplots. S-velocity, P/S-ratio, Poisson’s ratio,shear impedance and Poisson impedance in respect to depth from the tunnel surface, andacoustic impedance in respect to porosity. Light blue is VGN, darker blue is DGN andred is PGR. Larger symbols represent specimens closer to the surface. SpecimensEDZ109 and EDZ110 marked with red circles.

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Figure 5-3: Specimens EDZ109 and EDZ110 showing visible fracturing in theleucosome. The specimens appear as anomalous in plots of S-velocity, P/S –ratio,Poisson’s ratio, shear impedance and Poisson impedance in respect to depth from thestudy area surface as shown in Figure 5-2.

No clear trends were observed between static and dynamic elastic parameters.Associations between physical properties followed the known trends reported in Aaltonenet al. (2009). As expected, salinity testing showed clearly lower resistivities withspecimens saturated with ONKALO water, as compared to those saturated in tap water(Table 3-6). This was also seen in the statistical analysis, where compared to previousvalues from Olkiluoto, resistivities of PGR specimens plotted on the low end of the range.This is illustrated in Figure 5-4. Similar effect was observed for porosity, illustrated inFigure 5-5. Several pairs of variables were found to separate rock types, mostly beingconsistent with previous data (such as IP estimate PL vs. porosity, Figure 5-6). Porosityplotted against resistivity also seemed to separate gneissic specimens from pegmatoids;an effect not seen in earlier data (Figure 5-7). For more details on the results of thiscampaign and select cross plots, see Kiuru (2016).

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Figure 5-4: Cross plot of density and resistivity. Previous data with larger mostlytransparent symbols, current dataset with smaller opaque symbols. Resistivity values forPGR (red) plot at the lower end of the previously observed range, similar effect is notseen for gneissic specimens (blues).

Figure 5-5: Cross plot of density and porosity. Previous data with larger mostlytransparent symbols, current dataset with smaller opaque symbols. Porosity values forPGR (red) plot at the lower end of the previously observed range, similar effect is notseen for gneissic specimens (blues).

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Figure 5-6: Cross plot of porosity and IP estimate (PL). Previous data with larger mostlytransparent symbols, current dataset with smaller opaque symbols. Gneissic (blues) andpegmatoid (red) populations are roughly separated both in previous and current data.Similar effect can be seen for example in Figure 3-1, and when plotting porosity andmagnetic susceptibility, as well as various other combinations.

Figure 5-7: Cross plot of porosity and resistivity. Previous data with larger mostlytransparent symbols, current dataset with smaller opaque symbols. In previous data,gneissic (blues) and pegmatoid (red) populations are mixed, but in current data they formmore distinct individual populations.

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6 DISCRETE MICROFRACTURE NETWORK ANALYSIS

Set 1a specimens were sent for further analysis of microfracture network parameters.From the 20 specimens, 16 were selected and prepared, then photographed and analysed.Based on the retrieved microfracture network parameters, simulations were run for 10 mx 10 m x 10 m blocks of varying compositions. The input for the large-scale block modelswas obtained from thin section analysis, in order to assess the scaling of microfracturenetwork properties of the intact rock matrix.

6.1 Overview

To get an idea of the typical compositions of PGR and VGN in the specimens,petrographic analysis was performed. Thin sections of 26 mm by 47 mm from specimensEDZ109, EDZ115, EDZ159, and EDZ163 were cut and thinned to a thickness of 30 µm(± 3 µm). The thin sections were attached to a glass sheet and stabilised using epoxy, andthen analysed using a polarising microscope. Details of the observed minerals and theiridentifying features are reported in Kovács et al. (2016).

For the microfracture network analysis, 5 mm thick disks were cut from the 16 specimens,and polished after impregnation with UV fluorescent epoxy. The specimens were thenilluminated with UV light and photographed. The images were processed using TrainableWeka Segmentation plug-in of the ImageJ based Fiji environment, which allowed theimages to be simplified and reduced to two types of pixels: fractures, and host rock. Theimages were further processed, and geometric parameters of the microfracture networksextracted from the images using a sequence of MATLAB algorithms. A basic overviewof the process work flow is shown in Figure 6-1. Examples of a fluorescent image,segmented image and skeletonised image are shown in Figure 6-2. For further details ofthe process and the extracted parameters, see Kovács et al. (2016).

The extracted microfracture parameters were used as the basis for modelling of 10 m x10 m x 10 m blocks of varying combinations of VGN and PGR. The approach is fractalsbased and assumes that the (statistical distribution of) parameters of the larger blocks areequal to the parameters of the microfracture networks, i.e. that the system is scalable. Themodelling was done using a fractal geometry based discrete fracture network (DFN)algorithm of the REPSIM software package. Several runs of each type of model wereconducted, yielding corresponding number of realisations of the model. One of themodels, consisting of veined gneiss and a diagonally cross-cutting pegmatoid feature, isshown in Figure 6-3. For more details of the simulation, and all results, see Kovács et al.(2016).

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Figure 6-1: The microfracture network parameterisation process work flow and stepstaken, including used algorithms. Adapted from Kovács et al. (2016).

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Figure 6-2: Example of a fluorescent image of the microfracture network in specimenEDZ159 (a), the resulting segmented image (b), and a close up of the skeletonised image(c). Modified based on Kovács et al. (2016).

Figure 6-3: Example of results for one of the models used in discrete fracture networkmodelling. X-Z cross-sections every two meters shown in (a) to (e), distribution of rocktypes within the model are shown in (f). Modified based on Kovács et al. (2016).

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6.2 Key results

The determined fractal dimensions of the microfracture networks and fracture midpointsshowed no dependence on the location on a specimen or on the depth of the specimenfrom the study area surface. No significant changes in the values were observed. Thefractal dimension of the fracture midpoints D clearly differentiates different rock types(Figure 6-4). Fracture length distribution parameters show more variance, but this is notsystematic. No dependence on specimen depth could be observed. From theseobservations, EDZ cannot be identified based on the changes in the parameters of themicrofracture networks. The results are summarised in Table 6-1. Full results and moredetails can be found in Kovács et al. (2016).

Figure 6-4: Density vs. the fractal dimension of the fracture midpoints, D. Red is PGR,light blue is VGN.

The simulations showed systematically more fractures and more connected fractures inPGR compared to VGN. Fractures in the gneiss were predominantly short and mostlyarranged into one preferred orientation. Fractures in pegmatoid had two preferredorientations, and they were longer. Both the number of large fracture clusters (> 100fractures), and the maximum number of fractures in one cluster increased as thevolumetric content of PGR increased. The results are shown in Tables 6-2 and 6-3, andFigures 6-5 and 6-6. However, the total number of connected microfracture clusters wasnegligible in both rock types. Descriptive parameters suggest that the studiedmicrofracture systems are stably below the percolation threshold of the rock matrix.

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Table 6-1: Determined fractal dimensions and fracture length distribution parameters ofthe microfracture networks. Df is the fractal dimension of the fracture network, D is thefractal dimension of the fracture-midpoints, E is the fracture length index and F is aconstant of the fracture length distribution model.

Table 6-2: Number of large fracture clusters (> 100 fractures) in the simulations. Eachmodel was run five times.

Table 6-3: Maximum number of fractures in one fracture cluster in the simulations. Eachmodel was run five times.

Specimen Rock type Density(kg/m3)

Porosity(%)

Depth(m)

Df D E F

EDZ109 VGN 2710 0.40 0.10 1.62 1.51 2.2447 18.180EDZ110 VGN 2737 0.27 0.16 1.78 1.49 2.2310 18.544EDZ112 VGN 2748 0.70 0.34 1.72 1.48 1.8480 16.768EDZ114 VGN 2747 0.53 0.45 1.61 1.48 2.0684 16.951EDZ180 VGN 2727 0.39 0.56 1.59 1.51 2.8248 21.056EDZ181 VGN 2721 0.38 0.74 1.64 1.49 2.5107 20.000EDZ182 VGN 2732 0.40 0.80 1.67 1.54 1.9042 15.896EDZ155 PGR 2612 0.75 0.09 1.66 1.55 2.1732 16.400EDZ156 PGR 2608 0.61 0.15 1.63 1.55 1.9174 15.467EDZ157 PGR 2616 0.43 0.29 1.63 1.61 1.9315 15.906EDZ158 PGR 2630 0.37 0.35 1.65 1.57 2.3484 18.237EDZ159 PGR 2629 0.41 0.40 1.63 1.56 1.6480 14.689EDZ161 PGR 2620 0.45 0.62 1.61 1.62 2.2392 17.676EDZ162 PGR 2631 0.45 0.68 1.62 1.58 2.2481 17.806EDZ163 PGR 2626 0.48 0.73 1.69 1.51 1.9592 16.797EDZ164 PGR 2621 0.37 0.79 1.66 1.57 1.9930 16.799

Model Run 1 Run 2 Run 3 Run 4 Run 5 MeanAll gneiss (0 % PGR) 0 0 1 1 0 0.4Diagonal band of PGR (19 % PGR) 11 11 11 7 10 10.0Vertical band of PGR (30 % PGR) 16 12 13 18 11 14.0Two horizontal bands of PGR (40 % PGR) 15 15 14 20 19 16.6All granite (100 % PGR) 47 52 47 52 43 48.2

Model Run 1 Run 2 Run 3 Run 4 Run 5 MeanAll gneiss (0 % PGR) 84 100 101 109 74 93.6Diagonal band of PGR (19 % PGR) 215 194 201 213 237 212.0Vertical band of PGR (30 % PGR) 208 150 220 225 203 201.2Two horizontal bands of PGR (40 % PGR) 252 179 204 193 217 209.0All granite (100 % PGR) 498 583 310 256 393 408.0

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Figure 6-5: PGR volume content vs. the number of large fracture clusters in the simulatedvolumes. Partly transparent symbols represent the individual simulations, and opaquesymbols the arithmetic means for each model.

Figure 6-6: PGR volume content vs. maximum number of fractures in one fracturecluster. Partly transparent symbols represent the individual simulations, and opaquesymbols the arithmetic means for each model.

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7 DISCUSSION AND IMPLICATIONS

Discussion into various aspects of the study is divided so that it roughly follows thesections of the report. Sections 7.1 to 7.5 focus on technical aspects of their respectivemeasurements and analysis. General observations are noted in 7.6.

7.1 Specimens and preparation

Most specimens in this study were selected based on four key criteria:

1) Specimens should be from as close to the surface as possible to see the excavationdamage effect

2) Specimens should represent the rock types as well as possible3) Distribution of rock types should represent the geology of the study area4) Specimens from each hole should create a sequence as uniform as possible,

extending to a reasonable depth (beyond the expected reach of EDZ)

The expected range of EDZ used in specimen selection was estimated based on previousresults from various geophysical methods (e.g. Emsley et al., 1997; Jonsson et al., 2009;Kiuru et al., 2015; Siren et al., 2015; Heikkinen et al., 2017; Pussinen et al., 2017), suchas electrical resistivity tomography, seismics and ground penetrating radar. Thesemethods are averaging by nature, and likely to detect changes in larger scale, but may notsee the micro scale changes this study aimed to characterize. It is possible that the microscale changes happen over meters or tens of meters, and that the change is too gradual todetect in the first tens of centimetres.

Geologically the rock types at Olkiluoto are not distinctly separate units, but more acontinuous sequence of the same parent rock, partially melted to different degrees duringmetamorphoses, resulting in a migmatite rock mass. Selecting the specimens to representthe rock types might cause the specimens to be “too pure”, introducing skewness to thedistribution. Due to the complex local bedrock, variation in the rock mass is high.Selecting the specimens to represent the distribution of rock types in the study area(mainly PGR, VGN and DGN) means that the sample is not representative of Olkiluotoor even ONKALO as a whole. Furthermore, as VGN and DGN have very similarpetrophysical properties, future studies might benefit from including other rock types aswell. Here the selection was limited by rock types present in the study area.

Spatial coverage of the sampled drill holes was found to be reasonable. Set 1 specimenscover a full centre line (N-S) across the EDZ study area and two additional holes furthereast, whereas Set 2 specimens are focused more on the south end of the study area andgrouped closer together. It would have been possible to get better coverage in the E-Wdirection at the cost of depth coverage, but depth coverage was deemed more important.Due to the geology of the study area, VGN specimens are over represented in Set 2, whichas a result is not as representative of the study area as a whole as Set 1. Set 2 specimenswere selected to include different foliation orientations in respect to the specimen axis,which caused the VGN specimens to be further over represented, compared to PGRspecimens. Additionally, selecting specimens with two foliation directions may mean that

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they do not accurately represent the natural foliation with varying directions. Set 3 wasselected to complement Set 1 and for initial testing only. It included only 9 specimensand should only be considered as an indication that the topic might benefit from furtherstudy.

More bias into the sample is introduced due to technical limitations. The most damagedsections cannot be included in the specimens, meaning that no macro scale fractures arepresent in the sample. The specimens are typically required to be relatively intact, and asa result may not be representative of macro scale EDZ. This is especially true for Set 2specimens, which due to the required longer core sections are from much further from thesurface than Set 1 specimens.

Considering the focus of the study is on the microfractures, it is possible that preparationof the specimens has a disturbing effect. The effect of drilling was studied by looking ata thin section that transects a specimen and found to be irrelevant: no difference inmicrofracture network properties were detected. However, it must be noted that this testwas limited to a single specimen, and for future studies more comprehensive testing mightbe in order. Cutting and grinding the specimen ends might have an effect, but as cut andground surfaces are always present their effect is impossible to distinguish. It is howeverlikely that for similar specimens the effect is similar and would therefore not affect theresults within a set to any significant degree.

Prior to the testing, all specimens were saturated in saline ONKALO water underatmospheric pressure. This differs from prior studies, in which specimens have beensaturated in tap water or distilled water. Due to the slow nature of the tests and practicallimits (e.g. vacation season) saturation times varied between 14-17 days, and 35-42 daysdepending on the specimen set. This may affect the results but based on prior experienceand a study by the Geological Survey of Finland (Kivekäs, 1994), after two weeks thespecimens should be fully saturated. Meaning any possible change in saturation after thattime should be irrelevant.

Specimens varied in size depending on the tests performed and their requirements. Sometests were performed on specimens of various sizes, and this may lead to differences dueto scaling. Additionally, scaling effects are likely to be present between properties derivedfrom laboratory scale testing, and those derived from tunnel or larger scale studies. Thisshould be kept in mind when comparing the results to prior studies.

7.2 Petrophysical testing

Density and porosity values initially showed differences between the two operators, GTKand SP, even though the measurement setups were supposedly identical. After cross-verification of standards and comprehensive testing, it was concluded that even smalldifferences in the determination of weights (e.g. sequence of actions, speed at which theactions are taken) may lead to significant differences in the observed porosity values. Thiseffect is due to the very small absolute porosity values in the specimens, which meansthat relative errors are high and quickly multiplied. This means that porosity values ingeneral for such low porosity rocks should be used with caution. Small differences

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between operators were observed in P-wave velocities, but it must be noted that the setupsdiffered (different transducer frequency and picking method).

P- and S-wave velocities were determined unloaded and under several levels of axial load(2.5 MPa, 5 MPa, 10 MPa, 15 MPa and 20 MPa). Measurements were conducted in threeorthogonal directions, axial and two perpendicular lateral directions. The specimens weresubjected to two loading cycles: first the wave velocities were measured in the axial andone lateral direction, then in the second lateral direction. As the load is practicallyuniaxial, less of an evolution in the velocities is expected in the lateral directions, andmore so in the axial direction. Therefore, no conclusions other than concerning lateralanisotropy should be drawn from the lateral measurements conducted under load. Inunloaded conditions, all three directions can be used to estimate three-dimensionalanisotropy of the measured specimen.

Shear waves travel through the solid matrix, and do not feel the differences in the porespace fluids. Compressional waves on the other hand travel through the entire volumeand feel changes in the fluids (such as liquid vs. gas, i.e. level of saturation). This meansthat changes in saturation should affect P-wave velocities, but not S-wave velocities. It ispossible that differences in the moisture content mask differences in porosity, includingpresence of microfractures, when observing only P-wave velocity. This would mean thateven short time drying might affect the observed velocities. To deal with the issue, stepswere taken to ensure the time specimens spent out of water was minimised, and re-saturations were conducted between measurements. However, the exact effect remainsunknown. For future studies, it would be beneficial to conduct tests on both dry and wetspecimens, ideally with multiple known levels of saturation.

It was expected that transversal anisotropy in the wave velocities would be present ingneissic specimens that have foliation along the specimen axis. No transversal anisotropywas expected for gneissic specimens with foliation against the specimen axis, and noanisotropy at all was expected for pegmatoid specimens. However, varying levels ofanisotropy were observed for most specimens, including PGR specimens. This means thatassumptions of isotropy or transversal isotropy are not accurate for these specimens. Ofthe two, transversal isotropy appears to be a better estimate for the gneissic specimens.From unloaded P- and S-wave velocity measurements, it is clear that foliation directionsare not strictly 0 or 90 degrees as determined a priori, but rather vary within thespecimens. Thus the assumption of perpendicular foliation directions is not entirelyaccurate. As a result, calculations based on these assumptions are not entirely accurate,but rather best estimates.

Velocity picking proved challenging, especially for S-wave as signal levels variedbetween specimens. Disturbances in the signal were observed, most likely due to couplingbetween the specimen and the transducer/receiver, the internal structure and texture ofthe specimens. Additionally, the lateral dimensions of the specimens were rather smallcompared to the signal wavelength, which may result in interference from reflecting andscattered waves. As a response and due to the large dataset, an automated picking methoddescribed in detail by Jacobsson et al. (2016b) was adopted. This method reached anaccuracy of approximately 95 %, the rest of the picks needing manual adjustment.

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Permittivity measurements are subject to suspect due to seemingly unreliable equipment.However, they seem reasonable compared to the results presented in Heikkinen et al.(2017). Differences in resistivity compared to previous data can likely be explained bythe introduction of saline pore space fluid, which decreases resistivity. The decreaseappeared more profound for PGR specimens, which plot at the lower end of previouslyobserved range (Figure 5-3). Gneissic specimens plot similarly to earlier data (Figure 5-3). One possible explanation is the presence of two types of PGR on the site, and that thisstudy would only have sampled one of them. It was however confirmed by Posivageologist that both the typical large grained PGR and fine grained nebulitic PGR arepresent in relatively equal numbers in the analysed specimens. This means that theobserved effect cannot be explained by a lack of one of the PGR types.

There was a notable difference in resistivity between pegmatoid and gneissic specimens,even though their porosities did not differ significantly. This suggests that for the sameporosity, pegmatoid specimens have less resistance for passing current. This suggests thatwhat is effective porosity for electric current is different from effective porosity for fluid(flow), likely due to electric current not needing direct connections, but rather just a pathof least resistance. This is likely due to differences in the type of microfractures, such astheir orientation and following interconnectivity, which while not creating direct passagefor fluid flow still decreases the electrical resistance through a specimen.

The effect could be studied in more detail by creating numerical models with varyingfracture frequencies and orientations based on observed properties of the rock types, anddetermining the average path of least resistance in each setup. Further possibilities wouldbe determining the anisotropy of resistivity from laboratory measurements and comparingthis with fracture and foliation orientations, or verifying the results of the simulationsusing e.g. x-ray tomography. It might also be beneficial to study the effects of loading onresistivity to get a better idea of the “real” characteristics of the rock matrix as a bulk.

7.3 Rock mechanics testing

Rock mechanics testing on the specimens was not expected to be consistent with previousdata from Olkiluoto due to the saturation of specimens in saline ONKALO water insteadof tap water prior to the testing. This approach was chosen to get a better understandingof the effective in situ conditions on rock mechanics properties. In addition, aspects thatmay affect the comparability of data include different measurement setups, biasedfoliation directions, differences in saturation, and two cycles of loading up to 20 MPaprior to mechanical tests for the determination of loaded P- and S-wave velocities. Thismaximum level of 20 MPa for the loading cycles, conducted prior to mechanical testing,was selected so that any damage to the specimens could be avoided while still reachingthe expected pressure levels at disposal level. Brazilian specimens were thinner thanspecified in the ISRM suggested method, which means the data is not directly comparablewith previous values.

Testing setups in general were different than previously applied in the tests for specimensfrom Olkiluoto (Eloranta, 2014; Eloranta; 2013). Earlier, strains were typically measuredusing a chain extensometer, whereas now strain gauges were used. Comparisons to earlier

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measurements using strain gauges may also not be entirely comparable due to differencesin the type of strain gauge and/or adhesive used, in addition to the specimens being testedin saturated conditions in saline ONKALO water. In the analysis of measurement data,strain gauges provide a strain averaging over the gauge length, in this case 20 mm. Inorder to measure the representative homogenised response for the material as a whole,large strain gauges would be needed in some cases due to the large materialinhomogeneities. This to avoid measuring local features instead of specimen matrixdeformation. As the Olkiluoto rock specimens are heterogeneous by nature, straindistribution is measured by the strain gauges instead of point like measurements. Theseaspects will likely contribute to the different observed results, such as higher thanpreviously typical Poisson’s ratio, though the possibility that we are seeing EDZ effectcannot be ruled out.

Another possible explanation for the atypical Poisson’s ratio values, especially in gneissicspecimens, is the level of bonding between specimen and strain gauge. This might beaffected by the strain gauges being attached after the initial saturation period, onto wetspecimens, re-saturation after the installation of strain gauges, and prolonged exposure tosaline water before testing. The effect was countered by selecting an adhesive and coatingthat should be able to handle the exposure to saline water and protect the strain gauge andits bond to the specimen. Additionally, the exposure was kept as short as possible at 5days at most.

Presence of fluid is known to decrease strength (Masuda, 2001; Simpson and Fergus,1968; Vasarhelyi and Van, 2006; Vasarhelyi, 2005), and there are indications that salinitymay play an additional role (Jacobsson and Bäckström, 2005), possibly via chemicalcorrosion (Rathnaweera et al., 2014). However, when comparing the results to prior data(Eloranta, 2014; Eloranta; 2013) from specimens saturated with non-saline water, theinterpreted crack initiation (CI) stress values show similar results (Figure 7-1). Crackinitiation (CI) stress can be considered the upper limit for elasticity, and the consistencyof CI results with previous tests suggests that whatever chemical corrosion the salinewater may have inflicted on the intact matrix of the specimens has happened prior tosampling in situ, and thus is present in all specimens regardless of the type of saturationwater used during testing. Furthermore, this seems to imply that the decrease in ultimatestrength compared to dry specimens is mainly due to water content, and that the type ofwater does not seem to affect it. CI values are backed up by indirect tensile strength, whileUCS and CD values show differences. However, the UCS and CD values are sensitivefor the applied test setup, and the measured UCS and CD difference is opposite forgneissic and pegmatoid specimens (UCS and CD values lower than previously forgneissic and higher than previously for pegmatoid specimens). This is likely to be due tothe new operator with new measurement equipment and evaluation method.

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Figure 7-1: Crack initiation stress plotted against density. Values seem generallyconsistent with earlier data regardless of the difference in saturation fluid.

Calculation of elastic properties both from petrophysical and rock mechanics dataassumes a linear elastic homogeneous (in the scale of reference) isotropic medium, anassumption that likely is not valid for the rock specimens. Additionally, the uniaxial stressstate of the measurements does not represent the real in situ stress conditions consistingof heterogeneous triaxial stress state. The Olkiluoto rock has been demonstrated todisplay unique response and highly increased strength in true triaxial conditions(Behrestaghi et al. 2016). High level of variation and heterogeneity between and withinspecimens means that the geologically mapped and averaged direction of foliation, usedto determine the axis of mechanical anisotropy a priori, may differ from the apparentmechanical anisotropy direction, as demonstrated in Jacobsson et al. (2016a). To get abetter estimate of the true elastic properties in situ, calculations should be conducted foranisotropic medium, the parameters of which could be estimated from velocitymeasurements in varying directions or from true triaxial tests such as ones reported inBehrestaghi et al. (2016).

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7.4 Statistical analysis

Statistical analysis conducted was more comprehensive and methods more sound thanpreviously conducted for Olkiluoto specimens (Aaltonen et al., 2009). Analysis includeda more comprehensive set of variables than before and the methods were more robust andchosen to better match the characteristics of geological data. Instead of the typically usedPearson method, the non-parametric Spearman method was used in the current analyses.This has the advantages of not assuming linear dependency or normality, and being lesssensitive to outliers compared to the Pearson method.

Some of the specimen sets were rather small and impossible to subset with reasonablelevel of confidence. When possible, subsetting was done based on rock type, depth andapplied load. No analysis for all data combined was done, as rock types would likely maskany other effect. No analysis based on specimen set was done, as no relevant differenceswere expected. Groups of sample size N < 4 were combined to the adjacent group, butthis still left some very small samples, which may results in high relative errors. Set 2aand 2b specimens together were treated as “pseudo” data in the analysis as they are notexactly the same specimen, but were from within a few centimetres of each other.

For more definitive results testing should be repeated with larger sample sizes, preferablyat least 30 specimens per subset (e.g. pegmatoid specimens from a certain depth interval).For truly representative, unbiased sample, it should be ensured that sampling is doneequally but randomly in each depth group, in all foliation directions and regardless offracturing in the specimen. If however sample size needs to be limited, focus should begiven to fewer rock types with large enough samples. Testing should focus onelectrical/electromagnetic and elastic properties, and at least resistivity, P- and S-wavevelocities, density and porosity should be measured. Once definitive results have beenestablished, they could be used to constrain non-destructive geophysical data, whichwould allow true characterisation of the effects of excavation damage in highly fracturedspecimens.

7.5 Discrete microfracture network analysis

Composition and microfracture network properties of 16 specimens were analysed viathin sections. Main difference between VGN and PGR in these specimens appeared to bethe presence or absence of significant quantities of biotite, which is largely responsiblefor the foliation. It must however be noted that only small subset of all PGR specimensin the study were analysed via thin sections.

Analysis of the geometric parameters from the thin sections should be a straight forwardprocess with not much room for error, and the process remained systematic. Possibleinfluence of drilling, cutting, grinding and polishing on the specimens was considered asdiscussed in Section 7.1, and are considered not to affect the results of individualspecimens to any significant degree.

The modelling was limited to 5 models of varying compositions and 5 runs per model.Models were built based on what were considered geologically reasonable geometries,and as such are not entirely systematic. For future work, composition and geometry in the

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models should be varied more systematically, and if possible, the effects of the stress fieldshould be studied. More runs per model would be beneficial for the robustness of thesimulation, and including additional rock types would increase understanding on thesubject.

The hypothesis of differences in the type of fracturing in pegmatoid and gneissic rocksbased on the results of petrophysical testing discussed in Section 7.3 receivedconfirmation from the analysed thin sections and simulations. Fractures in VGN wereshort and mostly in one preferred orientation, likely controlled by foliation, whereas thefractures in PGR were longer and had two preferred orientations.

More fractures in total and more connected fracture clusters with more fractures percluster in the simulations were observed in PGR compared to VGN. Based on the results,the number of large micro fracture clusters and maximum number of fractures per clusterseem to increase with increasing volumetric PGR content as shown in Figures 6-5 and 6-6. It must be noted however that the fractals based approach of the modelling assumesthat the statistical distribution of the parameters remains the same regardless of scale,which means that the number of fractures is based on the observations. The modelhowever suggests that only a very small amount of the fractures that start from PGR canreach the neighbouring cells. This should at least partially contribute to the observeddifference in the microfracture networks.

7.6 Responses from the data

The observed responses in the data could be caused by several things, such as:

· Mineralogy and/or geochemical composition· Texture· Preparation: drilling, cutting, grinding, polishing· Geological history: prior deformations and alteration· Differences in the microfractures

Based on density, magnetic susceptibility and remanent magnetisation not having depthdependencies, it can be argued that most likely there is no significant depth dependencyfor mineralogy or composition in our specimens. Texture is likely to cause somedifferences between rock types, and especially in gneissic specimens also within rocktype. The exact effect remains unknown and should be studied in more detail in the future.Possible influence of drilling, cutting, grinding and polishing on the specimens wasconsidered and concluded not to affect the results of individual specimens to anysignificant degree.

It is known that there have been multiple stages of deformation in the history of Olkiluotorocks, meaning multiple cycles of loading and unloading, formation of foliated structurescausing anisotropy and probable microfracturing. Alteration on the other hand mightcause changes in porosity, strength and mineral composition, and could be present innatural fractures. However, no significant amount of such features in these specimens waslogged by geologists.

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Differences in the microfracture properties may either be natural or caused by drill &blast excavation. Differentiating the effect fracturing has from the effect rock type has,when the type of fracturing seems to depend on rock type, is tricky. The clear differencebetween pegmatoid and gneissic specimens either suggests that pegmatoids are moreprone to fracturing (either natural or induced), or that the natural heterogeneity of gneissicspecimens exceeds the effects of excavation damage, disguising it.

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8 CONCLUSIONS

Physical and mechanical properties of drill core specimens were determined as a part ofinvestigations into excavation damage in the dedicated study area in ONK-TKU-3620.Additionally, microfractures in 16 specimens were analysed and used as a basis forfractals based discrete fracture network modelling.

Composition and microfracture network properties of 16 specimens were analysed viathin sections. Main difference between VGN and PGR in these specimens appeared to bethe presence or absence of significant quantities of biotite. Possible influence of drilling,cutting, grinding and polishing on the specimens was considered and concluded not toaffect the results of individual specimens to any significant degree.

Hardly any depth dependencies were present in the data: these were mostly limited toresistivity, S-wave velocity, and elastic parameters that are derived from S-wave velocity.The effects seemed to be mostly limited to pegmatoid specimens. Not seeing a depth trendin P-wave velocity, it was concluded that the differences in S-wave velocity are likelydue to differences in the mechanical coupling of fracture surfaces. Same properties alsodistinguished anomalous specimens, typically from shallow depths, some of which couldbe directly linked to visible EDZ features.

There was a notable difference in resistivity between pegmatoid and gneissic specimens,even though their porosities did not differ significantly. This is likely due to differencesin the type of microfractures, such as their orientation and following interconnectivity.This hypothesis was later supported by the analysed thin sections and simulations.Fractures in VGN were short and mostly in one preferred orientation, likely controlled byfoliation, whereas the fractures in PGR were longer and had two preferred orientations.This suggests that VGN and PGR may suffer different types of excavation damage.

No systematic changes in the geometric parameters of the microfracture networks wereobserved in respect to depth, while differences between VGN and PGR were observed.This suggests that excavation does not cause detectable perturbance of the intactcrystalline rocks’ microfracture porosity, and that the disturbed layer observed bygeophysical methods may be caused be larger scale fractures not present in the relativelyintact specimens.

Based on the simulation results, the number of large microfracture clusters and maximumnumber of fractures per cluster seem to increase with increasing volumetric PGR content.The models also suggest that only a very small amount of the fractures that start fromPGR can reach the neighbouring cells (meaning: other rock types). The total number ofconnected fracture clusters if negligible in both rock types, suggesting the system is stablybelow the percolation threshold.

Varying levels of anisotropy were observed for most specimens, including PGRspecimens. This means that assumptions of isotropy or transversal isotropy are notaccurate for these specimens. From unloaded P- and S-wave velocity measurements, itwas clear that the assumption of perpendicular foliation directions, averaged from thegeological mapping of the specimens, was not entirely accurate.

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Clear dependency on loading could be observed for both velocities, an increase of 5.9 %and 4.2 % in P- and S-wave velocities, respectively, were observed when loading wasincreased from approximately 0 MPa to 20 MPa. All other elastic parameters also showeda clear increase with increasing load, except for Poisson’s ratio that is less sensitive todeformation. Distributions tend to get narrower with increasing load, suggesting that thevalues are getting closer to “real” values of the rock matrix as a bulk. This is likely dueto closing of open fractures and some of the pores. Static and dynamic properties werecompared, but showed no clear dependencies. Static values were generally lower and hada greater spread.

Comparing the crack initiation stress values to prior data from specimens saturated withnon-saline water, similar results were obtained. Crack initiation (CI) stress can beconsidered the upper limit for elasticity, and the consistency of CI results with previoustests suggests that whatever chemical corrosion the saline water may have inflicted on theintact matrix of the specimens has happened prior to sampling in situ, and thus is presentin all specimens regardless of the type of saturation water used during testing.Furthermore, this seems to imply that the decrease in ultimate strength compared to dryspecimens is mainly due to water content, and that the type of water does not seem toaffect it.

Thin sections, petrophysics and upscaled DFN modelling based on data from thin sectionsshowed similar differences between PGR and VGN, suggesting that the effect is scalable.All evidence based on these specimens from the EDZ study area seems to support thehypothesis that the disturbed rock layer on the tunnel floor, observed by other geophysicaland geological methods, may be caused by larger scale macro fractures rather than asmicro scale changes of the rock matrix properties.

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