P O S I V A O Y

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Joun i Va l l i

January 2010

Work ing Repor t 2010 -05

Investigation Ahead of the Tunnel Face byUse of a Measurement-While-Drilling System

at Olkiluoto, Finland

January 2010

Base maps: ©National Land Survey, permission 41/MML/10

Working Reports contain information on work in progress

or pending completion.

The conclusions and viewpoints presented in the report

are those of author(s) and do not necessarily

coincide with those of Posiva.

Joun i Va l l i

Pos iva Oy

Work ing Report 2010 -05

Investigation Ahead of the Tunnel Face byUse of a Measurement-While-Drilling System

at Olkiluoto, Finland

Investigation Ahead of the Tunnel Face by Use of a Measurement-While-Drilling System at Olkiluoto, Finland

ABSTRACT

This study focuses on the Measurement-While-Drilling (MWD) -system and its uses

within the scope of the ONKALO-project in Olkiluoto.

The MWD-system is based on monitoring while drilling wherein nine parameters are

logged at predetermined measurement intervals. This thesis investigated two inferred

parameters produced by a program developed by Atlas Copco AB; hardness and

fracturing. Due to the numerical nature of the data correlation to geological conditions

was attempted through the use of rock mechanical measurements. The following four

rock mechanical tests were used: the Schmidt-hammer, Uniaxial Compressive Strength,

Point Load Strength and Indirect Tensile Strength (Brazilian test).

MWD-data did not exhibit satisfactory correlations, neither with the Schmidt-hammer

test results, the uniaxial compressive strength test results nor with the point load results.

A moderate correlation was apparent with indirect tensile strength test results. Visual

observations confirmed the results: MWD can detect pegmatites from gneisses in

Olkiluoto.

The limits of the MWD-system were determined to be significant in Olkiluoto as the

bedrock of Olkiluoto is heterogeneous both in mineralogy as well as with regards to its

rock mechanical properties. The MWD-system has been proven to function under

geologically simple/homogeneous conditions where the rock properties of different

lithologies are clearly different. Drilling Rate Index tests are possibly the most reliable

method of establishing a correlation between MWD data and rock properties.

Further research should focus on the use of the MWD-system in a geologically simple

environment with a logging interval that is as frequent as possible. In addition, Drilling

Rate Index tests should be performed for each rock type and, when possible, samples

should be obtained from as close as possible to MWD-holes.

Keywords: measurement-while-drilling, drillhole, borehole, hardness, Schmidt,

uniaxial, tensile, strength.

Porauksenaikaisen mittausjärjestelmän käyttö tunnelin perästä tehtävässä kallioperän tunnustelussa, ONKALO, Olkiluoto

TIIVISTELMÄ

Tässä tutkielmassa käsitellään Measurement-While-Drilling (MWD) -järjestelmää ja

sen soveltuvuutta Olkiluodossa ONKALO-projektin puitteisiin.

MWD-järjestelmä perustuu porauksen aikaiseen seurantaan jossa tallennetaan yhdeksän

parametria määrätyin tallennusvälein. Tutkielmassa tutkittiin Atlas Copcon kehittämän

ohjelman tuottamia laskennallisia parametreja; lujuus ja rakoilu. Datan numeerisesta

luonteesta johtuen korrelointi geologisiin olosuhteisiin pyrittiin tekemään kallio-

mekaanisten mittausten avulla. Käytettyjä kalliomekaanisia tutkimusmenetelmiä oli

neljä: Schmidt-vasara, yksiaksiaalinen puristusmurtolujuus, pistekuormitus ja epäsuora

vetolujuus (brazilian test).

MWD-data ei korreloinut Schmidt-vasara -tulosten, yksiaksiaalisen puristusmurto-

lujuuden eikä pistekuormituslujuuden kanssa riittävän hyvin. Kohtalainen korrelaatio oli

havaittavaissa epäsuoran vetolujuuden kanssa. Visuaaliset havainnot vahvistivat tulok-

set: MWD kykenee Olkiluodon olosuhteissa havaitsemaan pegmaatiset alueet gneis-

seistä.

MWD-järjestelmän rajoitteet todettiin suuriksi sillä Olkiluodon kallioperä on sekä

mineralogiselta koostumukseltaan että kalliomekaanisilta ominaisuuksiltaan hetero-

geeninen. MWD-järjestelmä on osoittautunut toimivaksi geologisesti yksinkertaisissa/

homogeenisissa olosuhteissa missä eri kivilajien ominaisuudet ovat selvästi erilaisia.

Porattavuusmääritelmien tekeminen tarjoaa todennäköisesti luotettavimman keinon

tarkistaa korrelaatio MWD-järjestelmän ja kiven ominaisuuksien välillä.

Jatkotutkimuksia varten suositellaan MWD-järjestelmän käyttöä mahdollisimman

tiheällä tallennusvälillä geologisesti yksinkertaisessa ympäristössä. Lisäksi porattavuus-

määritykset pitäisi tehdä jokaiselle kivilajille ja, mikäli mahdollista, näytteet pitäisi

saada mahdollisimman läheltä toteutuneita MWD-reikiä.

Avainsanat: measurement-while-drilling, drillhole, borehole, hardness, Schmidt,

uniaxial, tensile, strength.

1

TABLE OF CONTENTS

ABSTRACT

TIIVISTELMÄ

1 INTRODUCTION ................................................................................................... 3

2 POSIVA SITE DESCRIPTION ............................................................................... 5

2.1 Posiva Oy and the ONKALO-project ............................................................... 5

2.2 Regional Geology of Olkiluoto and the surrounding area ................................ 6

2.3 Geology of the ONKALO-site .......................................................................... 7

2.4 ONKALO general mineralogy and rock properties ........................................ 12

3 MATERIAL AND METHODS................................................................................ 21

3.1 Fundamentals of bedrock drilling .................................................................. 21

3.1.1 Drilling Procedure .................................................................................. 21

3.1.2 Drilling Equipment ................................................................................. 22

3.2 Measurement-while-drilling ........................................................................... 23

3.2.1 MWD background .................................................................................. 23

3.2.2 MWD benefits and restrictions ............................................................... 24

3.2.3 MWD Parameters logged by AC Boomer E3-C30 .................................. 25

3.2.4 Atlas Copco MWD interpretation ............................................................ 26

3.3 Rock mechanical testing methods for rock strength ...................................... 26

3.3.1 Schmidt Hammer: theory and application .............................................. 26

3.3.2 Point-Load Strength: theory and application .......................................... 29

3.3.3 Indirect Brazilian Tensile Strength testing: theory and application.......... 31

3.3.4 Uniaxial Compressive Strength testing: theory and application .............. 32

3.4 Programs used data interpretation/management .......................................... 32

3.4.1 Surpac ................................................................................................... 32

3.4.2 Realworks Survey .................................................................................. 33

3.4.3 Tunnel Manager .................................................................................... 33

3.4.4 Microsoft Access/Microsoft Excel ........................................................... 34

4 RESULTS ............................................................................................................ 35

4.1 Schmidt test results ...................................................................................... 35

4.2 Pointload test results .................................................................................... 44

4.3 Indirect Brazilian Tensile Strength ................................................................ 46

4.4 Uniaxial compressive strength testing ........................................................... 47

4.5 Measurement-while-drilling ........................................................................... 49

4.6 3-D Digital Terrain Model (DTM) ................................................................... 50

5 DISCUSSION & CONCLUSIONS ........................................................................ 51

2

5.1 Schmidt Hammer conclusions ...................................................................... 51

5.2 Point-Load Test conclusions ......................................................................... 54

5.3 Indirect Brazilian Tensile Strength test conclusions ...................................... 54

5.4 Uniaxial Compressive Strength test conclusions ........................................... 55

5.5 MWD data conclusions ................................................................................. 55

REFERENCES ........................................................................................................... 59

APPENDICES............................................................................................................. 63

Appendix 1 .............................................................................................................. 63

Appendix 2 .............................................................................................................. 65

3

1 INTRODUCTION

Measurement-while-drilling (MWD) is a technique that allows for rock mass

characterisation during drilling with the aid of drill performance parameters. The

technique itself is old -Schlumberger first introduced downhole electrical logging to the

oil industry in 1911 which has since been extensively developed with the addition of

other features and in is in frequent use throughout the oil industry at present day (Segui

and Higgins 2002). Since the 1970's the technology has been extended to the mining

industry (Segui and Higgins 2002) and has now been tested as a rock mechanical

application in this study. Primarily this method is not aimed at rendering visual

geological interpretations obsolete but to increase the efficiency of underground (in this

particular case, tunnel) construction, as characterisation of rock strength ahead of

quarrying allows for modification of blasting plans as well as quarrying schedules. The

technique has been successfully used in distinguishing rock from fluid (in the oil

industry) as well as waste rock from ore (in the mining industry). All of the calculated

parameters developed from base parameters are fundamentally dependent on

penetration rate as it has been shown there are strong correlations between penetration

rate and rock strength.

Prior studies in this field have involved a number of different techniques for the

interpretation of raw drilling data; the focus of this study is on the use of an existing

method, developed by Atlas Copco. The parameter used throughout this study is

hardness which is a calculated result of an algorithm run on raw drilling data.

The objective of this study is to identify the uses of the MWD-system in the geological

circumstances that exist in Olkiluoto, Finland, and more specifically in the ONKALO

tunnel, where stress-strain situations are very different to those nearer the surface. This

encompasses correlation of MWD-hardness to measured rock strength and identification

of weaker structures within the bedrock ahead of the tunnel face as well as a possible

result of reduced blast fragmentation through optimisation of blasting.

4

5

2 POSIVA SITE DESCRIPTION

2.1 Posiva Oy and the ONKALO-project

With four nuclear power plants in operation in Finland and a fifth under construction

nuclear waste disposal requirements had to be fulfilled in accordance with the Finnish

Nuclear Energy Act which states that all nuclear waste produced in Finland must be

treated, stored and disposed of within Finnish borders and that no nuclear waste from

other countries can be imported into Finland (Posiva Oy 2008a). This responsibility lies

with the power companies operating the reactors in question and they must dispose of

their own waste accordingly as well as decommission any reactors used safely once

their usable lifespan has been exhausted.

In this particular case, Teollisuuden Voima Oyj and Fortum Power and Heat Oy

established a joint venture company, Posiva Oy, in 1995 to ensure the safe disposal of

all waste generated from their power plants. Posiva Oy is responsible for all of the

research related to the final disposal as well as design and implementation of disposal

logistics.

The final disposal site (in Olkiluoto, Finland) for spent nuclear fuel produced by TVO

Oyj and Fortum Power and Heat Oy was selected in 1999 by Posiva and therefore

proposed as the site for the repository. This proposal was then ratified in 2001 by the

Parliament and after preliminary site characterization research was completed,

construction of the underground rock characterization facility ONKALO began in 2004.

ONKALO consists of one access tunnel and three shafts of which two are ventilation

shafts and one is a personnel shaft. ONKALO will extend to the final disposal depth of -

420 m and is vital for the application for the construction license of the actual repository

as it provides a wealth of research data and will naturally function as the primary access

tunnel to the repository. The planned layout is displayed in figure 1. The current

schedule has final disposal beginning in 2020 with the repository being sealed in the

22nd

century (Posiva Oy 2008a).

6

Figure 1. ONKALO underground rock characterisation facility layout as of 2009.

Posiva's final disposal plans are based on the KBS-3 concept (originally developed by

Swedish Nuclear Fuel and Waste Management Co) which makes use of a multiple-

barrier principle. These barriers include a final disposal canister, a bentonite barrier and

the bedrock itself in order to minimise any risk to the isolation of the nuclear waste

(Posiva Oy 2008a). Development of these release barriers continues alongside research

involving several branches of science to produce 3-dimensional models such as a

geological model, a hydrogeological and hydrogeochemical model as well as a rock

mechanical model. These are then to be integrated to produce a comprehensive rock

model for the final disposal area.

2.2 Regional Geology of Olkiluoto and the surrounding area

Located in a bedrock area that covers approximately 800 million years of Precambrian

geological history the island of Olkiluoto is approximately 10 km2

in size. Protoliths of

the migmatised high-grade metamorphic mica gneisses found at Olkiluoto were mainly

supracrustal, metasedimentary and metavolcanic rocks deformed and metamorphosed

during the Palaeoproterozoic Svecofennian orogeny ca. 1900-1800 Ma. Plutonic rocks

such as trondhjemites, tonalites, granodiorites, coarse-grained granites and pegmatites

intrude the supracrustal rocks (Paulamäki 2009).

7

The supracrustal rocks of the Satakunta region are divided into two groups: a pelitic

migmatite belt situated in the southwest and a psammitic migmatite belt in the northeast

which can be correspondingly distinguished by predominant granitic/trondhjemitic to

granodioritic leucosomes (Paulamäki 2009).

According to plate tectonic reconstructions the pelitic and psammitic belts represent

parts of the early Proterozoic Southern Finland and Central Finland continental arcs

(Lahtinen et al. 2005). Deformation and metamorphism followed due to the collision of

the afore-mentioned arcs 1890-1880 Ma ago (Lahtinen et al. 2005) wherein the high T/

low P metamorphism was a result of mafic underplating which in turn led to a sharp

increase in temperature as well as recrystallisation and partial melting of the rocks of the

upper crust. Synorogenic granitoids in the region are a result of intense magmatic

activity related to mafic underplating. The Fennian orogeny ended in orogenic collapse

which was associated with regional extension and crustal thinning at 1860-1840 Ma

(Lahtinen et al. 2005). A subsequent period of crustal shortening and thickening

occurred at 1840-1800 Ma and is defined as the Svecobaltic orogeny (Lahtinen et al.

2005). This phase is characterised largely by high T metamorphism and partial melting

of the sedimentary rocks in southern Finland which produced the late-orogenic

potassium granites (S-type granites dated at 1850-1820 Ma) (Paulamäki 2009).

This was followed by extensive melting of the lower crust in an extensional tectonic

regime producing Rapakivi magma which intruded to higher crustal levels along pre-

existing shear zones and faults. The resulting rapakivi magma intrusion was of a

polyphase nature as is evidenced in the differing types of Rapakivi granite found in both

the Mesoproterozoic anorogenic Laitila rapakivi batholith (1583±3 Ma) and its satellite

massif, the Eurajoki rapakivi stock (Haapala 1977 in Paulamäki 2009). They are

classified as anorogenic granites due to not being directly related to the Svecofennian

Orogeny (Paulamäki 2009).

The subsequent phase involved fluvial deposition of the Satakunta sandstone in a deltaic

environment into a NW-SE trending graben structure, which may have begun to

develop already during the rifting period around 1650 Ma ago (Kohonen et al. 1993 in

Paulamäki 2009). Regardless, its upper parts were deposited 1400-1300 Ma and

primarily the sedimentary material stemmed from Svecofennian supracrustal and

plutonic rocks, suggesting that the rapakivi granites were not exposed at the time

(Kohonen et al. 1993 in Paulamäki 2009). The sandstone beds are tilted as well,

indicating block movements. Olivine diabase dykes and sills dated at 1270-1250 Ma in

age cut the sandstone and are believed to be, on the basis of their geochemistry, feeder

dykes to continental flood basalts. They are generally considered to represent the initial

rifting between the Baltica and Laurentia cratons (Paulamäki 2009).

2.3 Geology of the ONKALO-site

The bedrock at Olkiluoto is primarily composed of high-grade metamorphic suprcrustal

rocks, their origins having been epiclastic and pyroclastic material as indicated by their

geochemistry as well as the sporadic presence of relicts of primary bedding structures

such as graded bedding in metaturbidite sequences (Kärki and Paulamäki 2006). This

primary material was migmatised in conjunction with the development of abundant

8

leucocratic granites and later intruded by thin mafic dykes. A four-class division of the

rocks at Olkiluoto has been done on the basis of their field relations, texture and

migmatite structure:

1. migmatitic gneisses with stromatic, veined and diatexitic varieties;

2. tonalitic-granodioritic-granitic gneisses or TGG gneisses;

3. other gneisses including mica gneisses, quartz gneisses and mafic gneisses; and

4. pegmatitic granites.

Additionally some diabase dykes cross-cut the dominating lithology, which is for the

most part composed of veined gneiss (43 %) as well as diatexitic gneiss (21 %) and

pegmatite granite (20 %) (Posiva Oy 2008b). A geological map of Olkiluoto is

presented below in Figure 2.

Figure 2. Geological map of the Olkiluoto Area (Posiva Oy 2008b).

Migmatitic gneisses:

The main rock type found at Olkiluoto is migmatitic gneiss which is subdivided into

stromatic gneisses, veined gneisses and diatexitic gneisses based on migmatite

structures. These gneisses are representatives of end-members in a transition system of

gneisses and migmatites although distinctions between end-members is subtle as the

change between gneiss to migmatite is gradual. Mica-rich palaeosomes as well as

9

younger granitic leucosomes/neosomes characterize migmatitic gneisses in Olkiluoto

and on average they contain 20-40 % of leucosome. Mineralogically these gneisses are,

as their migmatitic label implies, mixed rocks so the main minerals quartz, K-feldpar

and plagioclase vary markedly. Quartz content can for example vary from 5-62 %

(Posiva Oy 2008b). Figure 3 displays veined gneiss and figure 4 diatexitic gneiss.

Figure 4. Rotated boudin of leucosome vein showing a sinistral sense-of-shear within

diatexitic gneiss (Paulamäki 2009).

Figure 3. Veined gneiss showing alternation of quartz- and feldspar-rich psammitic

bands and pelitic layers (Paulamäki 2009).

10

Tonalitic-granodioritic-granitic (TGG) gneisses:

The origin of the TGG gneisses is still undetermined although chemical compositions

indicate sedimentary origins. Modal compositions are mainly tonalitic to granodioritic

although granitic variants are also common. TGG gneisses are medium-grained, light to

medium-grey rocks that may occasionally resemble plutonic, non-foliated/weakly-

foliated rocks or coarse-grained mica gneisses. Contacts to migmatitic gneisses are

gradual with some sharp contacts typical to the intrusive nature of igneous rocks, hence

the doubt of origin. Figure 5 exhibits an example of TGG gneiss (Posiva Oy 2008b).

Figure 5. Pale, foliated TGG gneiss (Paulamäki 2009).

11

Pegmatitic granites:

Most of the pegmatitic granites found in Olkiluoto are interpreted to have formed during

the same time as the ductile deformation and migmatisation processes and as such occur

in the form of veins, vein networks and irregular masses. They are coarse-grained felsic

rocks that vary in modal composition from syenogranites to monzogranites, with quartz

content varying from 8 - 60 %. Figure 6 shows a pegmatitic granite vein cutting TGG

gneiss (Posiva Oy 2008b).

Figure 6. Pegmatitic granite veins cutting the TGG gneiss (Paulamäki 2009).

12

Diabase dykes:

Studies conducted in the past point to an age of c. 1560 Ma ago (Mesoproterozoic) for

most of the dykes that sharply transect most of the lithologies described earlier. They

strike NE-SW and dip steeply to the NW and are fine-grained although are altered to the

point where none of the original mafic minerals are visible. The largely altered

groundmass is composed of randomly oriented, albitic plagioclase laths (Posiva Oy

2008b).

Geochemistry:

A different classification has also been developed for the supracrustal rocks of Olkiluoto

as a result of their distinctly different geochemistries. There are 4 series that are

distinguished: the T, S, P series and mafic or ultramafic gneisses. Pegmatitic granites

form a separate group. The T series is characterized by mica gneisses and migmatitic

gneisses with less than 60% SiO2 and quartz gneisses with more than 75 % SiO2. The

mica gneisses are thought to originally be pelitic material (rich in clay minerals) whilst

the migmatitic gneisses to have been greywacke-type impure sandstone. The S series is

clearly from calcareous sedimentary material as it is characterized by high calcium

content. Similarly the P series is typified by high P2O5 concentrations on the order of

>0.3 % and is largely composed of TGG gneisses although the protolith is not entirely

certain: utilizing comparisons to other chemical compositions point to a mixture of

volcanic and turbiditic components (Posiva Oy 2008b).

2.4 ONKALO general mineralogy and rock properties

The Olkiluoto area is divided into several lithological units: two diatexitic gneiss units,

19 mica gneiss units, 32 units of tonalitic-granodioritic-granitic gneiss, 62 units of

pegmatitic granite and seven diabase dykes (Posiva Oy 2008b). Unfortunately this

division is not as straight-forward at the tunnel scale in Olkiluoto as the rock is very

heterogeneous and the type and degree of heterogeneity changes rapidly (Posiva Oy

2008b). Even so a concise division on a tunnel scale is as follows: metamorphic rocks

include migmatitic gneisses such as stromatic gneiss, veined gneiss, diatexitic gneiss as

well as (non-migmatitic) gneisses that in turn include mica gneiss, quartz gneiss, mafic

gneiss and TGG gneiss. Additionally igneous rocks include pegmatitic granite, K-

feldspar porphyry and diabase (Mattila 2006). With regards to tunnel observations, only

the veined gneiss, diatexitic gneiss, mica gneiss, quartz gneiss and pegmatitic granite

are commonly seen in geological tunnel mapping. Due to this a mineralogical

description for each common rock type will be presented along with a sample thin

section in order to clarify the heterogeneity of Olkiluoto rock and the difficulty of clear

rock mechanical distinctions related to MWD.

In order to describe the mineralogy of the afore-mentioned rock types it is however

necessary to use the previous whole-rock division as petrographical analyses require

further detail than merely a simple rock type distinction. Thus mineralogical

descriptions will be segregated based on the series in question whilst describing typical

rock type mineral assemblages within that particular series.

13

According to Kärki and Paulamäki (2006) the T-series includes various veined gneiss

and diatexitic gneisses although some less migmatized mica gneisses and quartz

gneisses also belong to the series. Certain TGG gneisses are also included on the basis

of similar geochemical signatures. A notable feature of the mica gneisses and

migmatites of this group is the occurrence of variably pinitized cordierite. Typical

mineral assemblages that describe T-series migmatites and mica gneisses include

quartz, plagioclase, K-feldspar, biotite, cordierite and sillimanite. White mica may be

present but only to a minor extent. Naturally more quartzitic variants contain more

quartz and feldspars and less biotite as well as usually excluding any cordierite or

sillimanite content although both of the latter minerals are common constituents of the

other members. TGG gneisses of the T-series are typically richer in K-feldspar with

cordierite being an atypical mineral. Sillimanite is never seen in T-series TGG gneisses

although sporadic garnet porphyroblasts can be encountered. Mineral composition

variation is systematic to a certain extent as it is controlled primarily by the chemical

composition and silicity of the samples. The darkest mica gneisses may have a quartz-

concentration of ca. 20 % whilst a quartz gneiss may have anywhere in the range of

50 % with plagioclase respectively increasing from 10 % to 20 %. Biotite

concentrations naturally follow a reverse trend with concentrations decreasing from ca.

40 % to below 20 % in quartz gneiss. All of the gneiss variants may contain up to 10 %

K-feldspar although concentrations may exceed 30 % in migmatite members.

As stated by Kärki and Paulamäki (2006) the S-series includes quartz gneisses, mica

gneisses, various migmatites and mafic gneisses with migmatitic rocks being rare. The

dominant types of this series often display homogeneous frequently fine-grained gneiss

layers with concretions or roundish boudins differing in composition from the host rock.

As mentioned earlier the members of this group are thought to have originated from

calcareous sedimentary materials. A dominant characteristic of the members of the S-

series is a relatively high quartz content even in the most mafic members. The average

quartz content is 46 % in the quartz and mica gneisses of the low-calcium subgroup

with 36 % in the gneisses of the high-calcium subgroup and a low 4 % in mafic

gneisses. The typical mineral assemblage of the low-calcium subgroup is quartz,

plagioclase and biotite with possible hornblende or garnet. Pyroxene may be present but

to a much lesser extent. Mafic S-type gneisses usually contain hornblende, plagioclase,

quartz, biotite and pyroxene. Mineral compositions of the S-series are similar to that of

the T-series with the members of the low-calcium subgroup resembling the most silicic

rocks of the T-series with quartz concentrations ranging from 30-60 % depending on

silicity. Biotite concentrations follow a reverse trend similar to that of the T-series.

Plagioclase concentrations vary from 20-50 % and are comparatively higher than their

equivalent variants in the T-series. K-feldspar is not present with hornblende and garnet

variably included. The high-calcium subgroup does however display marked differences

as quartz content averages ca. 30 % and can reach close to 50 % in more silicic

gneisses. Hornblende averages 10-20 % in less silicic gneisses but can decrease below

10 % in very silicic gneisses. Plagioclase content varies randomly between 20 and

50 %. Small amounts of biotite and garnet have been observed.

In line with Kärki and Paulamäki's (2006) description the P-series includes rocks such

as veined gneisses, diatexitic gneisses, TGG gneisses and mica gneisses with very small

proportions of leucosome. TGG gneisses are the largest subgroup within the series, with

less than 15 % of the samples classified as mafic gneisses. Similarly to the geochemical

14

division, the P-series rocks can be divided into three subgroups with regards to their

mineral composition. The typical mineral assemblage for P-type mafic gneisses consists

of plagioclase, hornblende, biotite, quartz with apatite and sphene. These mafic gneisses

form a subgroup with quartz ranging from 0 - 15 %, plagioclase 20 - 50 % and biotite

from ca. 2 to 30 %. In accordance with earlier systems hornblende displays a reverse

trend with increasing silicity resulting in a decrease from 50 to 10 %. A notable feature

is the presence of apatite as it is the most abundant in the most basic mafic gneiss

variants which may contain up to 6 % apatite. P-type mica gneisses and migmatites

have a strict SiO2 range, 55-62 %, with dominant minerals being plagioclase, quartz,

biotite and apatite. Average concentrations of felsic minerals, quartz and plagioclase

increase with silicity as expected with biotite naturally following an inverse trend.

Apatite concentrations ranging from 1,6 to 2,1 % are also typical of these gneisses. P-

type TGG gneisses however represent a wider subgroup as their SiO2 content ranges

from 55-70 %. Their mineral composition is not entirely different as it comprises of

plagioclase, quartz, biotite, K-feldspar and apatite. Garnet is present in the most silicic

TGG gneisses. Average quartz content ranges from 15 in basic gneisses to 35 % in

acidic gneisses. K-feldspar concentrations are low but can reach 25 % in acidic variants.

Biotite follows a reverse trend, decreasing to below 20 % at best. Plagioclase content

can vary randomly between 25 and 55 % with no effect from silicity. (Kärki and

Paulamäki 2006).

Basic metavolcanics and diabases resemble other mafic gneisses to some degree but

have been shown to be form a separate group on the basis of geochemical analysis

which will not be discussed here. Their variants can include olivine, with fairly high

apatite concentrations, along with quartz, mica, biotite with their altered variants

containing saussurite and albite.

Pegmatitic granites are typically coarse-grained and their typical mineral assemblage

involves quartz, plagioclase, K-feldpspar, muscovite and biotite. Garnet and cordierite

may also occur sporadically although mainly dependent on geochemistry. Average

quartz content varies between 10 to 40 %, plagioclase between 15 and 35 % and K-

feldspar between 25-50 %. Muscovite rarely exceeds 10 % with biotite concentrations

also remaining low (Kärki and Paulamäki 2006).

The following thin section photographs (Figures 7-16) represent typical mineralogies

for the following general rock types in the order given: veined gneiss, diatexitic gneiss,

mica gneiss, quartz gneiss and pegmatitic granite.

15

Figure 7. Veined Gneiss in plane-polarised light.

Figure 8. Veined Gneiss in cross-polarised light.

16

Figure 9. Diatexitic Gneiss in plane-polarised light.

Figure 10. Diatexitic Gneiss in cross-polarised light.

17

Figure 11. Mica Gneiss in plane-polarised light.

Figure 12. Mica Gneiss in plane-polarised light.

18

Figure 13. Quartz Gneiss in plane-polarised light.

Figure 14.Quartz Gneiss in cross-polarised light.

19

Figure 15. Pegmatitic granite in plane-polarised light.

Figure 16. Pegmatitic granite in cross-polarised light.

20

As intact rock strength has traditionally been described by its uniaxial compressive

strength (UCS) which can be obtained by a number of methods that will be covered

later, a short introduction to the normal UCS values determined for dominant rock types

in Olkiluoto is necessary.

Veined gneiss has an average UCS value of 111 MPa, mica gneiss 119 MPa, diatexitic

gneiss 105 MPa, granite/pegmatite 110 MPa and tonalite 116 MPa. It should be noted

that as Olkiluoto rocks are very heterogeneous a precise strength value range for a

specific rock type is unlikely to be accurate and therefore a geological analysis is

necessary (Posiva Oy 2008b).

21

3 MATERIAL AND METHODS

3.1 Fundamentals of bedrock drilling

Posiva Oy subcontracts SK-Kaivin as the main contractor for constructional work

related to the ONKALO project and as such the equipment described in this study is

partly owned by SK-Kaivin and partly by Posiva Oy. The drill rig manufactured by

Atlas Copco AB that generated all of the MWD-data used is however owned by Posiva.

To avoid terminology misunderstandings, drilled holes that produce a core are labelled

drillholes and holes drilled by percussive drilling that do not produce a core are labelled

boreholes. This classification standard is used in Olkiluoto and is thus necessary in this

study and will be used as such throughout.

3.1.1 Drilling Procedure

Percussive drilling is a dominant process used for fragmentation of rock and similar

materials and operates in a four-stage cycle: crushed zone formation, crack formation,

crack propagation and finally chipping (Ahtola et al. 1999).

As the drill bit begins to dent the rock surface and as stress increases with increasing

load the rock is elastically deformed, leading to the development of irregularities and

thus a zone of crushed rock. This zone is composed largely of numerous micro-cracks

and is the result of 75-80 % of the indenter's work (Ahtola et al. 1999).

Dominant crack formation follows as the process continues with placement depending

largely on the indenter shape. This stage represents a stage of restricted growth and is

therefore regarded largely as an energy barrier to full propagation (Ahtola et al. 1999).

Crack propagation occurs once the preceding energy barrier has been overcome and

develops rapidly until the tensile driving force falls below that which is necessary to

maintain crack growth (Ahtola et al. 1999).

The final stage of surface chipping occurs once sufficient load has caused the rock to

break and form large chips due to lateral cracks propagating from beneath the tip of the

indenter to the surface. Once a chip is formed the force resulting in breakage drops until

a sufficient level of force is achieved once again. The end result is a crater (Ahtola et al.

1999).

Optimal penetration of the rock is however dependant on several factors: drill bit

contact, a clean contact devoid of drill mud and optimal drill bit positioning (Telkkälä

2008). An attempted calibration of MWD-parameters to extract influence of the above-

mentioned factors as well as those imposed by the drill rig operator has however been

performed by Atlas Copco.

22

3.1.2 Drilling Equipment

The drill rig that produced all of the MWD-data used in this study is a Boomer E3-C30

which is manufactured by Atlas Copco AB. Figure 17 is a photograph of the Boomer.

Figure 17. Atlas Copco Boomer E3-C30.

The rig is a hydraulic face drilling rig equipped with three booms and a fixed boom

console as well as an advanced Rig Control System. The booms are heavy-duty

hydraulic BUT 45 booms and the E3-C30 can be equipped with COP 1838ME or COP

2238 rock drills. It's coverage area is a potential 137 m2, the rigs dimensions are as

follows: length 14-17 m, width 2,9 m, height 3,7 m and weight 39000 kg (Atlas Copco

2008). The rig is principally used in ONKALO for drilling long boreholes such as

probe-, control- or pre-grouting holes. Probe holes are 29 m long and 64 mm in

diameter, control holes are respectively 20 m and 64 mm. Pre-grouting holes are usually

26 m long and are drilled with a 64 mm diameter drill bit. Occasionally if needed blast

holes were also drilled with the Boomer, reaching a length of 5,2 m with a 54 mm

diameter. Provided a Boomer is equipped with the previously mentioned RCS and the

advanced Boom Control Function (ABC Regular or ABC Total) as well as the optional

MWD-logger it will be able to log drilling performance parameters at set intervals.

23

3.2 Measurement-while-drilling

3.2.1 MWD background

Schlumberger pioneered downhole electrical logging in 1911 in the oil industry in order

to reduce the "blindness" of drilling operations and increase the amount of knowledge

available in oil field geology and related structural characteristics. Following the initial

onset of logging-while-drilling a rapid development took place resulting in improved

logging systems and increased probing capabilities (Segui and Higgins 2002). It has

proven to be an invaluable tool for the oil industry in order to improve formation

evaluation and reservoir navigation. Ever since the 1970’s the technology for logging

while drilling has been extended to mining operations, although mostly in open pit

bench drilling/downhole drilling. The first recorders used were pen strip recorders

which had been adapted to mining and generated long reports of the results of drilling

but as interpretation of the results required hours of work the benefit was minimal

(Segui and Higgins 2002). Additionally the system was prone to frequent failures. With

the easy availability of computers and software the scenario changed dramatically,

resulting in fast data retrieval and analysis for both geologists to correlate inferred

lithologies to existing mine models as well engineers, who received feedback on the

timing and performance of each drill rig, the condition of the drill rig components and

the location and downhole details of each blast hole. Therefore rock mass variations

could be taken into account in blast design provided the geology was not complex or

very heterogeneous. Originally the system was based on a single parameter (commonly

penetration rate) but later evolved into a multi-parameter system which needs to be

evaluated as a whole as it reflects drill settings (influenced by the drill operator) as well

as rock properties (Segui and Higgins 2002). This in turn led to the development of

algorithms which filter through the parameter sets (Smith 2002). An important factor

that was also to be discovered was that any MWD-system is site-specific, as it does not

have a definitive recognition for a given rock type in any situation (Segui and Higgins

2002). Data resolution can also vary as the data retrieval interval in borehole length can

be set to anywhere between 50 cm and 3 mm (3 mm was achieved in laboratory

conditions at the Cooperative Research Center of Mining and Technology) (Smith

2002). It can also be set to a time interval although this was mostly only the case in

older systems. Certain MWD-systems are able to produce a set of normalized numbers

related to the specific energy (SE) from the default setup. Specific energy is defined as

the work done per unit volume excavated i.e. the amount of energy necessary to

excavate a given volume of rock should depend entirely on the properties of the rock

(Segui and Higgins 2002). SE was favored by some as opposed to interpreting

direct/calculated parameters such as penetration rate as they could be affected by

pulldown pressure or fractures and voids. This limitation however only applies to

downhole drilling and therefore penetration rate interpretations are considered

applicable in tunnel excavation. Additionally SE is not as free of influence as suggested:

there is evidence that drill settings and the condition of the drill and bit affect it to the

degree that if used for exact rock identification based on precise rock strength results

may be very inaccurate (Beattie 2009). Earlier attempts at MWD use in blast design or

excavation in general involved determining blastability, drillability or communition

indices.

24

A number of previous locations where MWD has been tested include sites such as the

open pit copper mine of Aitik in Sweden (Mozaffari 2007), the OSCAR area in the

Kiirunavaara iron mine in Sweden (Schunnesson 1998), the Glödberget tunneling site in

Sweden (Schunnesson 1998), the Zinkgruvan mine in Sweden (Schunnesson 1998), an

iron mine in Newman, western Australia (Segui and Higgins 2002), the Ernest Henry

Mine in Queensland, Australia (Smith 2002), limestone quarries in the Montreal area in

Quebec, Canada (Mutftuocluc et al. 2007), an open-pit taconite mine in the Mesabi Iron

Range in northern Minnesota, USA (Beattie 2009) and a limestone quarry in Parainen,

Finland (Kukkonen 2005).

3.2.2 MWD benefits and restrictions

Theoretically MWD has always been regarded as having more benefits than restrictions

as it has in some cases proven to be a cost-effective method that conserves time spent in

alternative investigation methods (Vynne 1997 in Smith 2002). These include

geophysical sensing methods, geological logging related to borehole results as well as

laboratory testing.

Kukkonen (2005) stated that his results demonstrated that MWD could generate a

relatively reliable geomechanical model displaying fracture zones although geological

modelling with regard to lithologies was found to be inaccurate. His primary source of

data was a limestone quarry in Parainen, Finland. He also determined that MWD was

lacking when attempting to evaluate rock strength or hardness since discriminating

between granite and limestone was difficult. This was a fundamental issue with MWD

given laboratory tests were able to distinguish a marked distinction between the two

rock types in rock strength.

Mozaffari (2007) concluded that using MWD data in conjunction with image analysis

should provide a more uniform fragmentation in open-pit mining by adapting individual

hole charges and blast design accordingly. Thus he found MWD to be descriptive of the

mechanical properties of the rock mass to be drilled, with penetration rate being the

most accurate representation of bench strength.

Telkkälä (2008) found that using statistical methods in analysing a large amount of

MWD data was able to distinguish separate rock types from each other. She did

however state that only the hardest of rock types was clearly visible in the data, other

rock types blended together preventing accurate identification.

A similar study by Beattie (2009) conducted in Canada using backpropagation artificial

neural networks which were trained and tested for her data set, revealed that neural

networks by themselves are not capable of effective rock type classification from MWD

in that particular geological environment. She also stressed that a classification scale

that applies to a hard-rock environment will not apply to a soft-rock environment.

Segui and Higgins (2002) concluded that as human error is removed from the

classification process and automated logging systems develop, MWD can provide an

accurate description of the rock mass that is drilled into. They also pointed out cost

savings and improved blast performance related to automated drill monitoring in real-

time although were careful to mention the inherent requirements for site-specific

25

calibration. MWD systems are not absolute rock recognition systems and must be

calibrated in situ and interpreted correctly to be of use in rock recognition.

Smith (2002) cited Vynne (1997) as determining a payback period for drill monitors to

be less than twelve months with rates of return in excess of 30 %. Total yearly savings

calculated by Vynne (1997) came to an excess of A$500 000/ ca. 305000 € with added

benefits in significantly reduced geological and geophysical investigation as well as

optimal drilling. Less manpower was also needed for log interpretation and data

handling once the system was implemented and calibrated fully. However it was

pointed out that total elimination of other conventional research techniques is not

warranted and that system functionality is site-dependent.

Schunnesson (1998) has focused on MWD extensively and has worked on excluding

influences from the operator and drill control system from MWD data in order to

establish correlations with particular rock type features. He stated that is necessary to

calibrate the data on-site by using a calibration data set i.e. core and cutting analyses,

geophysical logs, borehole TV or other visual observations. He suggested that once

reliable calibration data was obtained then use of statistical methods such as neural

networks, pattern recognition or multivariate analysis could be made. Schunnesson

(1998) found that lithological boundaries could be revealed from the data once

normalised and calibrated correctly as well as even deducing fracture locations from

MWD data. His specific data involved working on data from an iron mine where he was

able to clearly distinguish ore boundaries from waste rock. Schunnesson (1998) has also

pointed out a potential for RQD (Rock Quality Designation) classification ahead of the

tunnel face from MWD data related to sites with clear, systematic variation in RQD

although it could not be fully demonstrated as a working interpretation at Glödberget,

Sweden due to limited variation in RQD conditions.

Taking into regard the facts mentioned above, MWD systems have a potential to

improve site operations regardless of the site type although its full potential can be

negated by a complex, geologically heterogeneous site. To remove external influences

in addition to the site-specific calibration requirement, normalization of the MWD data

such as drill operator adjustments must also be done. Therefore with both key

restrictions taken into account an existing research data set must be available before the

systems benefits can be utilised.

3.2.3 MWD Parameters logged by AC Boomer E3-C30

The Boomer rig at Olkiluoto was set to log MWD data every 10 cm of depth per

borehole drilled and produced logs containing the following nine parameters:

Hole Depth (mm)

Percussive Pressure (bar)

Feed Pressure (bar)

Dampener Pressure (bar)

Rotation Pressure (bar)

26

Rotation Speed (rpm)

Water Pressure (bar)

Water Flow (l/min)

Penetration Rate (dm/min)

3.2.4 Atlas Copco MWD interpretation

Atlas Copco provides rigs with measurement-while-drilling options available as well an

accompanying program: Tunnel Manager. Tunnel Manager (dependant on the license)

is able to provide drill plan design functionality as well as hardness and fracture

interpretation. Both of the additional two parameters are, like penetration rate,

calculated parameters but can be regarded as inferred. This is due to the fact that they

are calculated by algorithms that filter the data accordingly. Although the terms

hardness and fracturing are used they are a numerical set of data derived from measured

parameters and should be interpreted site-specifically.

3.3 Rock mechanical testing methods for rock strength

Testing on rocks has developed alongside rock mechanical advances such that logging

does not only involve lithology, colour, mineralogy etc but also involves its mechanical

properties such as its strength and permeability etc. Tests can however be considered

fundamental or index tests where fundamental tests are usually slower to perform and

more expensive but determine an intrinsic property of the rock that is of a

conventionally accepted standard, or index tests which are quick to perform and

relatively inexpensive but provide index values which require scaling to fundamental

values (Hudson and Harrison 1997). Two of the most commonly applied index tests

include the Schmidt rebound hammer test and the Point Load test which can be

converted to Uniaxial Compressive Strength values via correction curves. Thus an

exemplary fundamental test is the Uniaxial Compressive Strength test which defines a

widely accepted rock strength value in MPa (Hudson and Harrison 1997). Of the tests

mentioned only UCS tests were not performed although previous results from Olkiluoto

were available, therefore a more detailed description of the UCS test will also be

described later on.

3.3.1 Schmidt Hammer: theory and application

Originally developed in the 1940s as an index apparatus for non-destructive testing of

concrete hardness in situ to determine hardening rates, the Schmidt hammer has seen

wide use in rock mechanics ever since the early 1960s (Aydin and Basu 2005). Primary

uses have involved estimating the uniaxial compressive strength and Young's modulus

of rock materials, determination of rock weathering, assessing joint separation and

discontinuities, estimation of underground large-scale in situ strength, mine roof

control, rock abrasivity, rock rippability and rock mass excavatability classification,

27

abrasion resistance of rock aggregates, penetration rate prediction of drilling machines

and prediction of roadheader and tunnel boring machine performance (Buyuksagis and

Goktan 2006). Popular use has partly been a result of its portable, simple and affordable

attributes as well as its simple operational principle and the theory involved in

estimating UCS. The ISRM (International Society for Rock Mechanics) has issued

standard methods for Schmidt hammer testing as early as 1978 (Aydin 2008). Rock

characterization as well as the methods for testing have been researched and revised as

recently as 2008, with ongoing improvements in more accurate rock characterization

taking place at present (Aydin 2008). Earlier publications have primarily focused on

improving data gathering procedures as well correlations for different lithologies. A

relatively recent study focused on the differences between hammer types, since different

types exist of which two are the most commonly used for rock property determinations:

the L-type and the N-type hammer (Buyuksagis and Goktan 2006).

The operational principle of the Schmidt hammer is very simple: the SH (essentially a

spring-loaded piston) is pressed orthogonally against a surface after which the piston is

automatically released onto the plunger (Aydin 2008). A part of the impact energy of

the piston is spent largely in absorption (work done in plastic deformation of rock

material under the plunger tip) as well as transformation (into heat and sound). The

residual energy is a measure of the impact penetration resistance or hardness of the

surface which enabled the piston rebound (Aydin 2008). Therefore it follows that the

harder the surface, the shorter the penetration time or depth resulting in a greater

rebound. The hammer then displays the distance traveled by the piston expressed as a

percentage value of the initial extension of the key-spring which is called the rebound

value (R) (Aydin 2008). This is considered to be an index of surface hardness. The

hammer axis must remain as perpendicular as possible in relation to the surface to be

measured. Rebound values are however influenced by gravitational forces such that

impact direction must be recorded and consequently normalized with reference to the

horizontal direction (Aydin and Basu 2005). Both ISRM and the American Society for

Testing and Materials (ASTM) recommend correction of rebound values using

correction curves supplied by manufacturers, although these curves have recently been

defined more specifically in order to cope with the difference in results caused by

different hammers as well as earlier correction curves being based solely on concrete

testing (Basu ja Aydin 2004). It must also be noted that when the in situ block size is

large, the R value reveals rock mass properties but when the rock is fragmented the R

value is only a measure of the rock mass quality (Hudson and Harrison 1997). Another

important factor in testing is the rock surface as the very nature of the test is naturally

dependent on surface geometry. Irregularities, a deteriorated surface or even single large

grains will cause a significant reduction in R as impact energy is expended in crushing

(Aydin and Basu 2005). Therefore ideally surfaces should always be sanded down and

20 measurements should be taken from single impacts, separated by no less than a

plunger diameter (Aydin 2008). Other requirements are an accurate representation of the

rock mass under study, i.e. the specimen should be intact, petrographically uniform and

typical to the rock mass domain (Aydin 2008). As mentioned earlier two types of

hammer exist with regards to rock mechanics and as a result a recent study revealed

marked differences in hammer results (Buyuksagis and Goktan 2006). While the

procedure for Schmidt testing has indeed been standardized by the ISRM and ASTM

the type of hammer to be used has not been clearly defined. The ISRM suggests use of

28

the L-hammer with the ASTM omitting preference. The study conducted by Buyuksagis

and Goktan (2006) revealed that the N-type hammer (used in Olkiluoto) outperforms

the L-type hammer and thus appears to be a more effective tool in strength estimation

for rocks in the UCS range of 20-290 MPa. They also pointed out that procedures based

on continuous impacts at a point in comparison to impacts at a single point are more

reliable and generate more accurate estimation of UCS. It should however be noted that

sedimentary and volcanic rocks were not tested in their study and that more recent

studies mentioned later recommend the use of single impact point measurements (Aydin

2008).

The results in this particular study were obtained by use of an N-type hammer (see

Figure 18) with the following measurement procedure: 10 impacts were recorded from a

measurement surface that had been sanded, with a plunger diameter separating each

impact point. All of the values collected were corrected by correction curves supplied

with the hammer so as to exclude gravitational effects. Out of the 10 measured, the five

lowest values were then discarded and the rest averaged. This resulted in a final

Schmidt index value for a selected measurement surface.

Figure 18. The Schmidt Hammer (N-type).

29

A thorough study done by Aydin and Basu (2005) involved a critical review of all the

issues related to Schmidt hammer rock characterization as well as improvements in

measurement accuracy. They used a total of 40 granitic (specifically monzogranite) core

specimens of various degrees of weathering obtained from Hong Kong. They also used

both dominant hammer types and established strong correlations between hammer

results as well as demonstrating effective use of both types. Rebound values correlated

very well both with UCS and Young’s modulus (E), although the N hammer performed

better. They also explored the possibility of using the Schmidt hammer as a tool for

prediction of weathering grade and found that multiple impacts at a single point proved

best in this particular case. If plunger tip diameter as well as impact energy could also

be increased predictions would be more accurate. In general conclusions were very

positive and demonstrated the cost-effective benefits of Schmidt hammer use in rock

characterization (Aydin and Basu 2005).

A review of the ISRM Suggested Method for determination of the Schmidt hammer

rebound hardness by Aydin (2008) clarified the procedure involved in Schmidt testing

and the correlation of Schmidt index values to UCS. Averaging single impact readings

was determined to be the best method as the repeated (continuous) impact method alters

the original microstructure of the surface resulting in the loss of invaluable information.

Correlations between UCS or E vs. R were found to be the most informative if done

using the mean rebound value from an entire set of measurements. According to Aydin

(2008), correlations should ideally be established for a given rock type whose response

falls within a single response domain. Heterogeneity as well as the scale of the test must

be taken into consideration, however, as variability between single rock types can be

considerable. Therefore a large number of measurements should be conducted to define

precise ranges for given rock types (Aydin 2008).

3.3.2 Point-Load Strength: theory and application

As one of the first index tests developed for testing rock strength quickly and efficiently

yet with a simple principle, the Point Load strength test has an extensive history behind

it. Although the ISRM Commision on Testing Methods published this test as one of the

first tests in 1972, even earlier research had been conducted by Protodyakanov (1960) in

(Broch ja Franklin 1972) in Russia and by Hobbs (1963) in (Broch ja Franklin 1972) in

England. It had however only been described as a rock strength index test by Franklin et

al. (1972) in (Broch ja Franklin 1972) and Broch ja Franklin (1972). Primarily intended

as an index test for rock strength classification it has however been successfully used to

predict uniaxial tensile and compressive strength (Chau and Wong 1996). This is due to

a correlation between Point Load test results and Uniaxial Compressive strength results.

Due to its nature as an index test, the test itself is inexpensive and the apparatus is light

and portable which led to the increase in popularity for rock strength testing (Chau and

Wong 1996). Therefore the ISRM has set Suggested Methods for Point Load testing in

accordance with the other tests mentioned in this study. The test measures the Point

Load Strength Index (IS(50)) which on its own may be a better indicator of rock strength

as the ISRM states that correlations are often approximate (ISRM, Suggested Method

for Determining Point Load Strength 1985). This is due to UCS usually being 20-25

times Point Load Strength although in some rock types with strong anisotropy the factor

can vary between 15 and 50 (Chau and Wong 1996). The Point Load test can also

30

accommodate various sample geometries as the test can be conducted axially or

diametrically on rock cores or in the case of the irregular lump test variant, on samples

varying in size and geometry. The irregular lump test is however considered to be far

more inaccurate then tests conducted on cores and as indicated above, the measured

Index should always be corrected by extrapolation to match a 50 mm diameter core

(Chau and Wong 1996). This diameter was chosen as it is the most suitable midway

value for the range of cores available at present.

The test method used involves loading of rock specimens that are either core, cut blocks

or irregular lumps through spherically truncated, conical platens until failure occurs

(ISRM, Suggested Method for Determining Point Load Strength 1985). Little or no

specimen preparation is usually required although the ISRM suggests use of core

samples with a diameter of 50 mm that have saw-cut faces (ISRM, Suggested Method

for Determining Point Load Strength 1985). Additionally, axial or diametric loading can

be used provided cores are available. There are however notable requirements for

specimens concerning the core test variants. The axial test should make use of

specimens with a length to diameter ratio of 0.3-1.0 whereas specimens with a length to

diameter ratio of greater than 1.0 are suitable for the diametric variant. The testing

machine consists of a loading system (with regards to the portable version typically

involves a frame, pump, ram and platens), a system for measuring the load P required to

break the specimen and a system required for measuring the distance D between the two

platen contact points. (ISRM, Suggested Method for Determining Point Load Strength

1985) Both axial and diametric tests were conducted on 51 mm diameter core

specimens in the data used in this study which satisfies the suggested 50 mm diameter

criteria. A larger set of data related to a study done in 2005 by Pohjanperä et al.

involved use of the same testing machine, the Bemek Rocktester.

Although the most commonly accepted formula relating the Point Load strength index

and the Uniaxial Compressive strength was founded by (Broch ja Franklin 1972) and

later confirmed by Bieniawski (1974) and Brook (1980) in (Chau and Wong 1996),

there are objections to the indiscriminate use of the formula. Pells (1975) in (Chau and

Wong 1996) stated that a conversion factor of 24 can potentially lead to a 20 % error in

the prediction of UCS for certain types of rock such as sandstone, dolerite and norite.

Read et al. (1980) in (Chau and Wong 1996) found that the conversion factor for

sedimentary rocks and basalts in the Melbourne area is 20 and 16, respectively. The

original formula is as stated below:

C0 = κIS(50) ≈ 24IS(50) ,

where C0 is the UCS and IS(50) is the point load index (Chau and Wong 1996). The index

to strength conversion factor is κ. With respect to results obtained from Olkiluoto, the

conversion factor used was determined empirically to be 20 (Pohjanperä, Wanne and

Johansson 2005). The same factor was used throughout for all rock types. It should be

noted that as anisotropy plays such a notable part in results, strength testing procedures

should always involve noting the direction of weakness planes such foliation. The tests

done in Olkiluoto that were used in this study were partly conducted in the direction of

any potential weakness planes, although a large proportion of results ignored weakness

plane orientation. Failure planes should also be noted, as they are important as

premature failures will give anomalous values (Chau and Wong 1996). When premature

31

failures occur the specimen breaks into several pieces and at a comparatively early stage

in loading, leading to a highly anomalous value which should be ignored.

3.3.3 Indirect Brazilian Tensile Strength testing: theory and application

The indirect Brazilian Tensile strength test is intended to measure the uniaxial tensile

strength of prepared rock specimens indirectly (ISRM 1978). It is based primarily on

the assumption that those rock materials used are linearly elastic and homogeneous and

that most rocks in biaxial stress fields fail in tension at their uniaxial tensile strength.

This occurs when one principal stress is tensile and the other finite principal stress is

compressive with a magnitude not exceeding three times that of the tensile principal

stress. Tensile strength is calculated according to the equation below:

σt = (2 / π) P/Dt

where P is the load on the disc at failure, D is the disc diameter and t is the disc

thickness (Hudson and Harrison 1997). According to the ISRM Suggested Method for

the Brazilian indirect tensile strength test, the testing apparatus should consist of two

steel loading jaws which should be designed so as to contact a disc-shaped rock sample

at diametrically-opposed surfaces over an arc of contact of approximately 10° at failure.

The radius of the jaws should be 1.5 times the specimen radius. A suitable machine for

recording and measuring compressive loads should also be attached and calibrated

(ISRM 1978).

The apparatus used in this particular study was a Bemek Rocktester, provided by

Suomen Malmi Oy. The BRT is portable and is much more affordable due to its cost

being a fraction of the cost of a conventional testing machine. Its total weight is

approximately 35 kg. It consists of small 65 kN loading frame with attachments for

different tests, a hand operated hydraulic pump, an electronic balancing unit and a

programmable microprocessor. The microprocessor can store and print data from a

transducer and two strain gauges as well as performing arithmetic operations on the said

data (Kanduth and Milne 1986).

A second set of data was also used, which made use of a MTS 815 Rock Mechanics

Testing System, a computer-controlled servo-hydraulic compression machine. The

essential elements are a load-cell, extensometers for strain measurements, a load frame,

a hydraulic power supply, a test controller and a PC micro-computer (Hakala et al.

2005). It should be noted that the Rock Mechanics Testing System made use of an

alternate testing procedure, where the load was applied through two 3,5 mm wide flat

steel jaws (Hakala 2005). Sample dimensions in this study were 31mm in width with a

diameter of 50 mm. As per the requirements set about by the ISRM both faces were

even as the specimens were collected from existing cores with a diamond saw. Details

such as lithology, orientation of potential weakness planes, sample source, diameter and

height, degree of saturation, mode of failure etc were recorded as per ISRM Suggested

Methods. Where earlier tests conducted by the Rock Mechanics Testing System

computed the tensile strength directly, the Rocktester used provided both the analog

pressure reading at failure as well as the computed indirect tensile strength. During

32

testing any anisotropy in samples was taken directly into consideration by orientating

potential weakness planes (e.g. foliation in VGN) along the load axis.

3.3.4 Uniaxial Compressive Strength testing: theory and application

Perhaps the oldest and simplest mechanical rock test, the uniaxial compressive strength

test is still widely used for determination of unconfined compressive strength as well as

Young's modulus. It is also a fundamental test in comparison to all of the afore-

mentioned index tests (Hudson and Harrison 1997). The simplest version of the test

involves the compression of a cylindrical core of rock between two parallel metal

platens (Jaeger et al. 2007). The load is applied using hydraulic fluid pressure with the

aim of inducing a state of uniaxial stress in the specimen. Typical test requirements

involve a height to diameter ratio of 2,5-3.0 with a preferred diameter of approximately

54 mm as well as a continuous, constant loading rate such that failure occurs within 5-

10 mins of loading or alternatively a stress rate of 0.5-1.0 MPa/s (ISRM 1985). The

uniaxial compressive strength can then be calculated from the recorded maximum load

at failure divided by the original cross-sectional area. A spherical seat for the platen at

the upper end of the specimen is necessary to bring about even stress distribution. It

should be noted that even with a spherical seating the actual state of stress in the rock

core is not homogeneous as frictional forces acting along the interface between the core

and the platens constrains lateral expansion which should occur in a true uniaxial stress

state (Jaeger 2007). Nevertheless the results obtained are a fundamental measure of the

strength of the rock specimen analyzed.

The uniaxial compression tests used in this study were conducted in an earlier study of

the strength and strain anisotropy of mica gneiss in Olkiluoto but as the results were

from Olkiluoto lithologies pertinent to this study they were deemed essential for rock

strength evaluation in relation to MWD parameters. Specimens with diameters of 42

and 52 mm were used and axial strain was also measured using extensometers. Results

were then later scaled to the equivalent of 62 mm diameter results as the majority of

earlier results were from 62 mm diameter samples. The same apparatus, the MTS 815

Rock Mechanics Testing System, was used to conduct the tests so the tests were

performed under radial strain rate control, corresponding to an elastic axial loading rate

of 0.75 MPa/s (Hakala et al. 2005).

3.4 Programs used data interpretation/management

3.4.1 Surpac

Surpac is a comprehensive software package for mining and geological exploration

industries and is used primarily to visualise, plot, model, design and report surface and

underground mining/construction data. Originally Surpac was designed for use in

mining for geologists to be able to determine the physical characteristics of deposits

with limited information. To accomplish this Surpac takes advantage of 3-D graphics,

geostatistics and an integrated modelling environment (Gemcom Software Products -

Surpac 2009). Data is acquired from databases that are in Posiva's case managed in

33

Microsoft Access. Surpac's uses have widened to encompass geotechnical, rock

mechanical/engineering uses; Posiva Oy has used Surpac primarily for visualisation and

digitisation wherein geological mapping done in the tunnel on paper is transferred into a

digital format. 3-D modelling is also done for deformation zones and lithologies as well

as visualisation of drillholes and their related data. Tools included in Surpac for

example are 3-D wireframing, block modelling, and variogram modelling. Detailing the

individual functions and properties of Surpac and their very extensive nature is outside

the scope of this study; Surpac is however incredibly versatile and was a vital tool in

this study as all of the data gathered is displayed within Surpac. Versions used include

5.2 E and 6.0.2.

3.4.2 Realworks Survey

Realworks Survey is a program designed to import data from Spatial Imaging Sensors

in order to turn them into 3-D visuals. 3-D scanners operate on the principal of point

registration fixed to a coordinate, thus with a preset point density the scanner registers a

scanned area as points in 3-D space that are fixed to real-world coordinates. As a result

a 3-D model is produced. Therefore Realworks Survey enables users to register,

visualize, explore and manipulate scanned data as point clouds (Trimble - Realworks

2009). Posiva Oy uses scanned point cloud data to verify planned tunnel dimensions

after quarrying. Officials also use the data to monitor that project regulations are

adhered to. Digitisation of geological mapping is also done using Realworks Survey,

although scanned point clouds are significantly reduced in density for ease of use during

digitisation. This study used a reduced point cloud to create wire-profiles of a tunnel

section in order to create a Digital Terrain Model (DTM) of the tunnel onto which

photographs of the tunnel were fixed upon. This took advantage of the positional data

incorporated into scanned point clouds and measured points to correctly fix the

photographs to their respective positions. Realworks was also used in visualizing drilled

boreholes in relation to completed tunnel dimensions.

3.4.3 Tunnel Manager

Tunnel Manager is a program developed by Atlas Copco Rock Drills AB for use with

Boomer ABC Regular and Total drills. Primarily it is a software package designed for

planning, administration and analysis of tunnelling projects. There are three versions

available, although the features in each version are solely dependent on the USB license

key in use. Tunnel Manager MWD version 1.5 was used for analysis during this study

as it is the only version able to generate calculated hardness values based on the existing

MWD-parameters. Tunnel Manager has several functions: tunnel project administration,

tunnel line design, laser design, fixpoint definitions, tunnel contour design, drill plan

design, drill plan rule definitions, drill log reporting, MWD log handling, MWD

analysis, profile log evaluation and drill rig or tunnel site production reports. During this

study TM was mainly used for importing and viewing all data from the drill rig in use.

Although a scale can be modified to visualise changes in e.g. hardness within TM it was

deemed better that all data was exported into Surpac so as to correlate individual

borehole data to proven changes in lithology or bedrock structure. TM is based on a

Microsoft SQL Server Desktop Engine database, MSDE5, in which all data is kept in a

34

defined structure. The topmost level of the structure is the Site, followed by the Tunnel

below which nodes for all logs, drill plans, reports, laser lines and contours can be

found. Naturally MWD-logs and their data were of interest in this study. Hardness

values generated in TM MWD were exported into numerical values within an excel file

from where hardness data was extracted for use in Surpac. TM can also display charts

with depth against a specified parameter, e.g. hardness. TM MWD is also able to

display MWD slicing and MWD mapping where slicing displays 2-D section slices and

mapping a 2-D view from above of the tunnel in question. These functions are

principally meant for interpreting the rock mass and identifying changes in the values of

the parameter under analysis. When viewing an MWD log within TM it is identified by

the chainage where the logs originate from and when opened displays all of the

boreholes imported into TM (holes included were chosen specifically for this study as

only those holes that are parallel to the tunnel profile are of comparable use). MWD

parameters can then be chosen for display in 3-D or chart view, of which hardness and

fracturing are only available in TM MWD (i.e. using a MWD-license key).

3.4.4 Microsoft Access/Microsoft Excel

Microsoft Access is necessary to create databases that Surpac uses to display borehole

data. Once certain table structures are created and then mapped by Surpac, all of the

data contained in an Access database can be displayed in Surpac.

Excel was necessary in order to migrate data from Tunnel Manager to Surpac. The

process involved importing all MWD logs into Tunnel Manager, after which they were

exported into an Excel file that also contained the additional hardness and fracturing

parameters mentioned earlier. Once in Excel format, an Excel file composed of macros

for data exporting (created by Ismo Lallo, Rollcon Oy) was used to combine matching

borehole MWD data with its respective coordinates and export all of the data into the

afore-mentioned database.

35

4 RESULTS

4.1 Schmidt test results

Schmidt testing was done on varying lithologies in different locations at Olkiluoto.

Earlier tests done by Matti Hakala in February 2008 were done on the wall in the

personnel shaft and in the exhaust air shaft at depth levels 187-172 and 90-171

respectively. Both shafts were measured with two parallel lines 30 cm apart in each

shaft with measurement intervals of 1m. Later tests done by Ville Backman in July 2008

were conducted in laboratory conditions on pre-grouting hole samples obtained from the

personnel shaft (ONK-PP90) and the exhaust air shaft (ONK-PP68). Testing locations

can be seen in Figure 19.

Figure 19. Schmidt test locations in Olkiluoto: red marker - exhaust air shaft, blue

marker - personnel shaft. The research tunnel is displayed in dark red (Posiva Oy

2008b).

36

Both cores were approximately 85 m in length, tests were done on preselected areas of

each core. Further tests done in this study in 2009 have been performed in the

ONKALO research tunnel on the tunnel walls, with tunnel ceiling data collected during

the last stage. Before Schmidt testing in the tunnel, all rock surfaces were sanded down

to a smooth surface with a Hilti Angle Grinder equipped with an abrasive blade

primarily meant for concrete grinding. After grinding the surfaces were washed in order

to better distinguish lithology. The first set of data was collected between chainages

3260 and 3312, with a measurement interval of appr. 1 m. Each measurement surface

was made large enough to allow 12 impacts in the immediate vicinity of each other,

with each individual impact separated by a plunger diameter. Lithology, structural

elements such as foliation and its orientation/type/intensity were recorded and its rock

mechanical foliation (RMF) (Milnes et al. 2006) denoted. Other remarks included

whether or not the values were acceptable, as occasionally rock surfaces sounded

hollow on impact, leading to far lower values than normal. This was due to unavoidable

occasional small sections of tunnel wall that were loose and disconnected from the

bedrock. For values to be valid, measurements had to come from bedrock that was

structurally sound. With regards to the first set of data, both the left and right wall were

chosen for measurements resulting essentially in two parallel measurement lines

running down the tunnel as seen in Figures 20 and 21. This chainage interval was

chosen due to two deciding factors: MWD-data collection with the AC Boomer began at

chainage 3263 and a notable brittle fault zone, OL-BFZ080 (Posiva Oy 2008b),

intersects the tunnel approximately between chainages 3290-3320. For further

distinction of the brittle fault zone from Schmidt data, two short measurement lines

perpendicular to the structure with 30 cm measurement spacings were performed on

each wall. The first pair of perpendicular lines was conducted at the first intercept of the

brittle fault zone, chainage 3290, seen in Figures 22 and 23. The second set took place

at chainage 3300, figures 24 and 25. Due to the heterogeneity of Olkiluoto bedrock,

Schmidt results were deemed necessary to be collected from the immediate vicinity of

the collected MWD data i.e. as close as possible to a drilled borehole. In order to

accomplish this, 10 boreholes were drilled at chainage 3565 that were visible on the

tunnel wall after excavation. Of these 10, two holes were usable, as pressures in the

other 8 were significantly lower in comparison to all existing data due to differences in

operator drilling procedures. Two measurement lines were then conducted after sanding

on the two valid holes, with a measurement interval of 10 cm (due to the MWD logging

interval of 10 cm). Results were insufficient as most of the wall was loose rock which

had an adverse effect on measurements. Whilst awaiting a full blast hole sequence, a

contact between mica gneiss and pegmatitic granite on the right tunnel wall at chainage

3580-3585 was chosen in order to obtain more values for pegmatite as well as to

attempt to resolve a clearly defined contact zone from numerical Schmidt hammer data.

Nine measurement surfaces were sanded to smooth surfaces, 4 around the 3580

chainage marker in a square pattern with another 4 around the 3585 marker. As the

contact occurred at 3585, four of the measurement surfaces displayed pure pegmatite

and the transition to mica gneiss. Therefore a ninth measurement surface was selected

after the transition so as to obtain values for "pure" mica gneiss. Upon reaching

chainage 3620 blasting for the EDZ research niche could commence, whereupon

perimeter blast holes were drilled using the Boomer. This meant that of 39 perimeter

boreholes (excluding bottom holes) 32 were visible on the tunnel surface after

excavation, allowing for several measurement lines to be performed extremely close to

37

where MWD data had been sourced. 4 holes with 3 on the left wall and one on the right

were chosen as loose rock and accessibility limited the selection available. Boreholes on

the right wall were also affected by abnormal pressures leading to data which could not

be compared to earlier data, thus only one hole on the right wall was chosen to be

acceptable. Measurements were conducted at 10 cm intervals along each borehole,

providing a significant amount of results. Additionally a mobile personnel lift was used

to reach 2 boreholes on the ceiling. These were chosen as the left-hand hole penetrated a

significant pegmatite lens whereas the right-hand hole was another representative of

"normal" Olkiluoto gneiss. Naturally before measurements took place all of the surfaces

were sanded and washed, including those on the ceiling. All of the EDZ hole results are

available in figures 26-31. Numerical data is available upon request from Posiva Oy.

Regarding data interpretation, all of the collected data has had the following procedures

performed on it: the five highest measurements for each measurement surface were

recorded and averaged, leaving a single representative rebound value for each

measurement surface. Additionally corrections for the direction of the hammer piston

with relation to gravity were performed.

Figure 20. Schmidt Profile line along the left tunnel wall between chainages 3260-

3312. OL-BFZ080 is distinguished as a severe drop in rebound. A loose measurement

surface renders an abnormally low value.

38

Figure 21. Schmidt Profile line along the right tunnel wall between chainages 3260-

3308. OL-BFZ080 is distinguished as a severe drop in rebound.

Figure 22. Schmidt profile line performed perpendicular to OL-BFZ080 on the left

tunnel wall at chainage 3290. OL-BFZ080 is distinguished as a severe drop in rebound.

39

Figure 23. Schmidt profile line performed perpendicular to OL-BFZ080 on the right

tunnel wall at chainage 3290. OL-BFZ080 is distinguished as a severe drop in rebound.

A loose measurement surface renders an abnormally low value.

Figure 24. Schmidt profile line performed perpendicular to OL-BFZ080 on the left

tunnel wall at chainage 3300. OL-BFZ080 is distinguished as a severe drop in rebound.

40

Figure 25. Schmidt profile line performed perpendicular to OL-BFZ080 on the right

tunnel wall at chainage 3300. OL-BFZ080 is distinguished as a severe drop in rebound.

Figure 26. Schmidt profile line performed along MWD borehole number 2 on the left

wall of the EDZ research niche. Several loose measurement surfaces are excluded and

are not available in the graph. Abnormally low values from intact surfaces are

attributed to large mica grains influencing rebound.

41

Figure 27. Schmidt profile line performed along MWD borehole number 3 on the left

wall of the EDZ research niche.

Figure 28. Schmidt profile line performed along MWD borehole number 4 on the left

wall of the EDZ research niche. Higher values are due to quartz-rich areas.

42

Figure 29. Schmidt profile line performed along MWD borehole number 5 on the right

wall of the EDZ research niche. A large part of the measurement surfaces available

were disconnected and thus those measurements were excluded.

Figure 30. Schmidt profile line performed along MWD borehole number 5 on the left

side of the ceiling of the EDZ research niche. Partially loose rock caused low values

outlined in red.

43

Figure 31. Schmidt profile line performed along MWD borehole number 5 on the right

side of the ceiling of the EDZ research niche. Quartz-rich measurements are indicated

as higher values.

44

4.2 Pointload test results

A total of 1385 results were used in the study of which 1022 tests were performed in

2005 (Pohjanperä et al. 2005). These 1022 tests were done on samples taken from 29

deep investigation holes from the Olkiluoto area (Pohjanperä, Wanne and Johansson

2005). Samples from drillholes OL-KR1-KR28 and OL-PH1 were collected from the

locations depicted below in Figure 32.

Figure 32. Core collection locations. (Pohjanperä et al. 2005).

The remainder of the results were collected from additional work completed on behalf

of Matti Hakala by Ville Backman on the earlier-mentioned pre-grout drillhole core

samples obtained from the personnel shaft (ONK-PP90) and the exhaust air shaft

(ONK-PP68). Results were classified according to rock type as in the previous study but

as the earlier results had incorrect rock type interpretations newer corrected rock type

labels were used. Due to the quantity of the data and its numerical nature it can be

requested from Posiva Oy, although essential graphs are available in figures 33 and 34.

Point load results from before 2008 are higher due to different sampling localities

further indicating the heterogeneity of Olkiluoto rock.

45

Figure 33. Average Point-Load strength (converted into UCS with a conversion factor

of 20) for data prior to 2008.

Figure 34. Average Point-Load strength (converted into UCS with a conversion factor

of 20) for data obtained in 2008.

46

4.3 Indirect Brazilian Tensile Strength

Indirect Brazilian Tensile Strength tests have been conducted ever since 1996 in

Olkiluoto, with the first series of results from 1996 provided by Matti Hakala after

which more were done in 2002. Erik Johansson completed 10 more tests in 2004.

Further tests on behalf of Matti Hakala performed by Ville Backman were done in 2008

on the earlier mentioned pre-grout drillhole cores ONK-PP68 and ONK-PP90 although

with a different apparatus, the Bemek Rocktester. Earlier results were obtained using the

MTS 815 Rock Mechanics Testing System for increased accuracy and reliability. For

additional research material 159 tests were conducted on two investigation hole

samples obtained from the Excavation Damage Zone research niche using the Bemek

Rocktester. It should be noted sampling depths varied. As the results included

lithological divisions a graph for lithology-specific tensile strengths can be seen in

figure 35. The lowest values were obtained for the pegmatitic granite.

Figure 35. Average Tensile Strength for all available data including data obtained

prior to 2009 (a total of 373 results).

47

4.4 Uniaxial compressive strength testing

Uniaxial compressive strength tests have originally been done for Olkiluoto rocks in

1992 following which tests have been done in 1994, 1995, 1996, 2002 and 2004. Tests

have been conducted on the following cores:

OL-KR01 1001m

OL-KR02 1051m

OL-KR03 502m

OL-KR04 902m

OL-KR05 559m

OL-KR10 614m

OL-KR12 795m

OL-KR14 514m

OL-KR24 551m

Sampling depths vary and detailed information is available from Posiva Oy. Earlier tests

up to 1996 as well as tests performed in 2004 were obtained by Erik Johansson. Tests

done in 1996 and 2002 were performed by Matti Hakala at the Laboratory of Rock

Engineering at Helsinki University of Technology in conjunction with the afore-

mentioned Indirect Brazilian Tensile strength tests conducted there. Results were

gathered using loading rates between 0.5 and 1 MPa/s so as to conform to ISRM

suggested rates. A graph of the lithology-specific uniaxial compressive strength for all

of the data available from Olkiluoto is seen in figure 36. Numerical data is available

from Posiva Oy on request. Due to the nature of the test and its costs, further tests have

not been conducted, although the data gathered so far is acceptably representative of the

UCS of most major rock types found in Olkiluoto and deemed thus sufficient for the

purposes of this study.

48

Figure 36. Average Uniaxial Compressive Strength for all available data obtained from

Olkiluoto rocks.

49

4.5 Measurement-while-drilling

Measurement-while-drilling data consists of two parts: the positional log or otherwise

called the round log and the MWD data log. Since the beginning of the use of the

Boomer's MWD capabilities data has been collected from chainage 3263 and currently

extends to chainage 3813. All of the data has been merged so as to display borehole data

at its correct respective real-world coordinates in Surpac. This involved matching up a

total of 582 MWD logs and 51 round logs in Microsoft Excel using macros developed

by Ismo Lallo. Prior to this Tunnel Manager MWD was used to create Excel exports

which included all of the "hardness" data in numerical format originally created by the

Tunnel Manager program. A total of 34 Tunnel Manager reports were created. All of the

data is available in a Microsoft Access database and can be viewed in Surpac. A color

scale has been created to visualize changes in lithology. Color scaling has been

established by visual correlation of clear lithology changes and is a rough segregation of

numerical data varying on a scale of -2 to 12. Further segregation produced unreliability

so a division into two lithological groups was taken to be most accurate. Gneisses are

represented by turquoise blue and pegmatitic granites by red in figures 37-1 and 37-2.

The full MWD log generated in excel format by Tunnel Manager for the EDZ research

niche containing all MWD parameters as well as calculated parameters is available on

request from Posiva Oy.

Figure 37-1. Blast boreholes for the EDZ research niche, including two long probe

boreholes. Where available, MWD hardness is seen as red for low and turquoise for

normal. White sections are areas where MWD data is unavailable.

50

Figure 37-2. Pre-grouting holes from chainages 3288-3307. Where available, MWD

hardness is seen as red for low, turquoise for normal. White sections are areas where

MWD data is unavailable. OL-BFZ080 is displayed in blue.

4.6 3-D Digital Terrain Model (DTM)

Brittle Deformation Zone OL-BFZ080 was also surveyed during this study and was

then overlapped with MWD data to attempt to confirm structural distinctions from

MWD data. This involved surveying a total of 93 points in order to encompass the full

breadth of the zone. The model was then created using all of the points and then

extrapolated outwards in order to penetrate the tunnel profile fully.

51

5 DISCUSSION & CONCLUSIONS

5.1 Schmidt Hammer conclusions

Schmidt results display a systematic scale on which Olkiluoto rock types vary although

due to the heterogeneity of Olkiluoto rocks, distinction of specific rock types such as

quartz gneisses as opposed to pegmatites is questionable. This is not only due to

heterogeneity in mineralogy but also due to the similarity in strength in certain rocks.

Pegmatites are in general found to be the hardest rock type although also brittle yet the

Schmidt hammer will give very similar rebound readings for quartz gneisses. The

Schmidt hammer is also sensitive to local variations so much so that diatexitic gneiss

may give significantly different readings from different locations. Additionally,

although careful selection of measurement surfaces was done throughout the study,

certain surfaces may have in fact been slightly disconnected/loose and therefore

readings would not be entirely reliable. Fully reliable selection is near-impossible as

detection of discontinuities behind measurement surfaces within bedrock is challenging

and would require use of geophysical sensing methods. The Schmidt hammer does

however exhibit sensitivity for discontinuities when discontinuity planes are parallel to

the direction of the plunger and in close proximity. Larger fault gouge areas are

obviously clearly apparent from Schmidt data as rebound will then be minimal. In

general two major observations can be made: pegmatites and quartz gneisses are usually

above 68 in rebound readings whereas the other gneisses are usually below 68. If

rebound values drop below 50, structural weaknesses are in close proximity of the

measurement surface. As a reliable method for predicting UCS of Olkiluoto rocks the

Schmidt hammer is found to be questionable and not nearly as accurate as proper

laboratory testing. Unlike results produced by Aydin and Basu (2005) which were for

granitic samples wherein an exponential correlation was established for Schmidt vs

UCS, results from Olkiluoto cannot be correlated similarly. Difficulties in correlation

arise from local heterogeneity which essentially leads to Schmidt and UCS values that

vary on a narrow strength range for all gneisses as well as pegmatite. A correlation chart

for UCS and Schmidt for pre-grout core ONK-PP68 is available in figure 38, as it was

the only available set of data with UCS and Schmidt data obtained from matching

locations.

52

Figure 38. UCS vs. Schmidt data obtained from pre-grout drillhole core ONK-PP68 for

varying rock types (Veined gneiss=green triangle marker, Diatexitic gneiss=blue

diamond marker and Pegmatitic granite=red sphere marker).

As for correlation to MWD data and distinction of structural discontinuities such as

fractures, the Schmidt hammer is unfeasible. Finally, a reverse correlation could be

established between MWD hardness and Schmidt rebound: when Schmidt rebound was

high enough to indicate pegmatite MWD hardness was found to be low. This is however

dependent on lithological interpretation as quartz gneiss caused high rebound values but

was found to be high in MWD hardness values. It should be noted that this correlation is

rough and is based on experiences during measurements, which cannot be therefore

fully verified from collected data. Therefore ultimately the Schmidt hammer cannot be

used for MWD correlation which is mostly a result of the limited variation in major

rock types as well as the narrow strength range mentioned earlier. Further evidence for

this can be found in figures 39 and 40.

53

Figure 39. Schmidt Rebound vs MWD Hardness obtained from Hole 3 from the left wall

in the EDZ Research niche. Mainly veined gneiss.

Figure 40. Schmidt Rebound vs MWD Hardness obtained from Hole 10 from the ceiling

of the EDZ Research niche. Mainly pegmatite.

54

5.2 Point-Load Test conclusions

Point-Load tests are an accepted way of deducing the UCS of rock types as a cheap

method and therefore the amount of results was much higher than any other method in

this study (although also due to an earlier study mentioned in section 4.2). Results

however also pointed to the same conclusion as that reached by Schmidt hammer tests:

Olkiluoto lithologies do not vary much when gauged by rock strength. Point load results

varied on a scale between 108 and 143 MPa when scaled with a conversion factor of 20

to UCS. Although variation is larger than that of Schmidt hammer data it is still far too

restricted to segregate each gneiss definitively due to heterogeneity within specific rock

types. As tunnel lithologies are mostly gneisses and pegmatite, point load is therefore

found to be unsatisfactory for correlation to MWD. A significant additional factor that

causes significant issues when attempting to distinguish different lithologies from

MWD data is a very similar point load result for pegmatite when compared to gneisses

as seen in figures 33 and 34. Ultimately correlation to MWD data is therefore found to

be unachievable without localized testing i.e. from a close proximity to MWD

boreholes. Even then point load tests would need to be conducted on samples that

mirror the logging interval of MWD data so as to allow for definitive correlation

without graphical interpretation using positional data, as was done for part of the

collected Schmidt data.

5.3 Indirect Brazilian Tensile Strength test conclusions

Indirect Brazilian Tensile strength tests indicated a very similar trend with one sole

exception: every rock type apart from pegmatite showed a tensile strength of

approximately 12-14 MPa whereas pegmatite exhibited a constantly lower value of

below 10 MPa. Even as results were taken from very near MWD boreholes the minimal

distinction of rock types from each other (with internal variability even within a given

rock type) led to the conclusion that only pegmatite has the potential to differ

significantly enough for it to be distinguishable from MWD data. This was apparent

from graphical observations as MWD data was found to interpret areas from where pure

pegmatite had been extracted as soft areas, due to the brittle nature of pegmatite. As for

correlation of Brazilian data to MWD data numerically, this was found to be negligible

and would require samples sourced from the same precise locations as MWD boreholes.

It must be noted that even then, correlation would be doubtful due to the restricted

variability mentioned earlier with the exception of pegmatite. The reader may note that

tonalitic-granodioritic-granitic gneiss appears to differ in tensile strength sufficiently

enough to appear harder and would therefore be expected to be distinguishable from

MWD data. This is however not the case as TGG values are based on a much smaller

result batch and further tests would need to be done to validate consistently higher

tensile strength. TGG has also been found to be very rare in ONKALO as it has only

recently been found again in the EDZ research niche and is expected to be rare later on

during excavation. Regardless of its lesser significance, TGG was found to be more

resilient during testing primarily due to a fine-grained, evenly distributed quartz matrix.

Due to this resilience distinguishing TGG from MWD data is to be expected as a much

harder rock but as of yet cannot be done with the quantity of data available.

55

5.4 Uniaxial Compressive Strength test conclusions

Uniaxial compressive strength tests revealed a very similar trend in Olkiluoto rocks as

to those seen before, although pegmatite was not found to be any different than gneisses

as in the previous test. Figure 36 in section 4.4 shows a strength range of 100-120 MPa

for all of the lithologies in question which in turn causes correlation to MWD data to be

impossible directly. UCS does therefore not have a direct influence on MWD hardness

or on MWD results. This in contrast to what Kahraman et al. (2003) deduced in their

study on dominant rock properties affecting the penetration rate of percussive drills.

They concluded that the higher the UCS the lower the penetration rate which is not the

case in Olkiluoto. Further UCS tests may be performed on samples collected from

present tunnel depth but will most probably result in values highly similar to those seen

earlier.

Overall results from this range of tests highlights the difficulty in establishing

lithological classifications based solely on rock strength properties in Olkiluoto.

Therefore it must be noted that to attempt to establish a valid classification 6 months of

data collection is insufficient; instead it would take years to accomplish as data sets

need to significantly larger for such heterogeneous rock.

5.5 MWD data conclusions

MWD data amounts to a very large portion of the data available in this study but as

mentioned before, correlation to rock-mechanical data must be done on data from

precisely similar locations. This meant that interpretation of MWD hardness data

obtained from injection boreholes, probe boreholes and control boreholes could only be

made use of after a correlation had been established from blast boreholes which were

not only visibly open to lithological interpretation but also to accurate localized

sampling. The most essential part of this study was a blast hole sequence drilled by the

AC Boomer for the EDZ research niche, which allowed for visual interpretation of

MWD hardness data sources. Most importantly MWD hardness data was found to

reliably separate gneisses from pegmatite as a large pegmatite area was apparent in the

ceiling area of the blast hole sequence. After modifying the scale for displaying MWD

data, the method was found to confuse OL-BFZ080 with pegmatite. It is however

distinguishable as BFZ related fault gouge/breccia as results were fragmented and not

purely of a soft area. This was due to alternating areas of solid rock and severely

fractured rock. Due to restrictions imposed on this study by ONKALO excavation

management and the nature of the site, research could not be conducted in sufficient

quantities to allow for a larger quantity of data which could be accurately correlated.

Restrictions involved the aversion of the use of the AC Boomer for blast hole drilling by

excavation management, as well as the restricted location of excavation as boreholes

could not be freely chosen as strict limitations related to the size and layout of the

research tunnel ONKALO are imposed by repository plans and long-term safety

requirements. Therefore blast holes could not be drilled often enough by the AC

Boomer to produce sufficient quantities of MWD hardness data that could then be

visually sourced on excavated tunnel walls.

56

This was a fundamental issue in this study due to the heterogeneity mentioned earlier, as

lithologically Olkiluoto rocks vary macroscopically even on a scale of 20-30 cm.

Sampling for correlation to MWD hardness data therefore needs to be performed as

close as possible to the origin of MWD data and should ideally "mirror" a borehole log

in its entirety. In reality this involves sampling every 10 cm (the current logging interval

of the Boomer) for every blast hole that is as fully visible as possible after excavation

on the tunnel wall. MWD data is therefore found to be very sensitive and is highly site-

specific; the data cannot be interpreted similarly for a separate site. Furthermore, MWD

hardness as calculated by Atlas Copco's Tunnel Manager is found not to be hardness as

such but more a value of rock resistance to repeated impact stress combined with drill

cut fragmentation. This meant that a drilling rate index (DRI) would be best for MWD

correlation and should in the future be viewed as the primary means of lithological

interpretation from MWD data. DRI values are established for different rock types by

two tests: the Swedish Stamp Test and the Sievers Miniature Drill test. The Swedish

Stamp test involves measurement of the percentage of undersized material (rock) that

passes through a 11.2 mm mesh after 20 impacts on a sample placed in a mortar using a

14 kg weight. It produces the intact rock specimen brittleness value S20. The Sievers

miniature drill test involves measurement of the hole depth in a rock sample after 200

revolutions in 1/10 mm. It in turn generates the Sievers miniature drill-test value SJ.

Normally a mean value of 4-8 test holes is used. Once the two are combined the DRI

index value for a particular rock type can be established. This would then naturally only

distinguish lithologies from MWD data, but provided UCS tests were also done in

conjunction with DRI tests on samples from their respective proximities, MWD could

possibly be calibrated to work as a UCS evaluation method. Therefore future studies

could focus more on indirect calibration of MWD data through correlation of DRI to

other rock mechanical data such as UCS before attempting to evaluate the use of MWD

data on its own. It is also vital that all of the data in such a study would be collected

site-specifically as DRI values are also susceptible to heterogeneity such as that seen in

Olkiluoto lithologies. Further errors that could contribute to a failure to correlate MWD

data to lithologies or rock-mechanical properties are interpretation of MWD data

without detailed inspection of each MWD parameter independently. As penetration rate

and Atlas Copco's hardness are both reliant on pressures involved in drilling, they are

both severely affected by abnormal pressures. It is hence vital that MWD data gathered

is comparable with regards to pressures as otherwise data interpretations will fail to be

accurate.

To sum up, MWD as a method was found to be challenging to use successfully in

Olkiluoto geological conditions but was able to detect pegmatite from gneisses. As

mentioned earlier, the system did however confuse pegmatite with brittle fracture zones

such as OL-BFZ080 but the issue was resolved to a satisfactory degree. Fracturing

when fault gouge or brecciation was not present was excluded as unreliable after the 3-

D model and digitised fractures did not correlate with fracturing produced by Tunnel

Manager MWD. Further attempts at using MWD regardless of site should consider the

suggestions above before research and should keep in mind that MWD is purely a

function of drillability and thus requires studies on drillability for site-specific

geologies. Quite possibly MWD applications in hard rock conditions could be found to

be successful in areas where geological contrasts are clear and rock types are largely

homogeneous. Situations such as these may arise in underground or open-pit mining.

57

Additional detail from MWD could possibly identify ore zones satisfactorily ahead-of-

the-face as well as lead to significant savings in excavation related to civil engineering

projects due to more accurate blasting as individual hole charging and blasting could be

adapted during excavation. A further benefit in civil engineering projects would be a

reduced zone of damage around the excavated tunnel as blasting would not be

unnecessarily intense where smaller charges would be adequate. This was however

found not to be the case in Olkiluoto as current blasting materials and methods could

not be altered easily so as to account for ahead-of-the-face detected pegmatite. As

systems develop and reliability is increased MWD may well prove to be a valued

method in the mining/engineering industry but as of yet is found to require significant

effort for it to function as it is meant to.

58

59

REFERENCES

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Atlas Copco Ltd. 2008: Boomer E3-C30 Manual. 199 p.

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60

Jaeger, J, Cook, N., and Zimmerman, R. 2007: Fundamentals of Rock Mechanics. 4th

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affecting the penetration rate of percussive drills. International Journal of Rock

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Mechanics, 5.

Kohonen, J., and Rämö O.T. 2005: Sedimentary rocks, diabases and late cratonic

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- geological setting of the Olkiluoto investigation site. Posiva Oy. Olkiluoto. 49 p.

Pohjanperä, P., Wanne, T. and Johansson, E. Point Load Test Results from Olkiluoto

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Posiva Oy. 2008b: Posiva Site Report. Posiva Oy. Olkiluoto. 647 p.

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61

Smith, B. 2002: Improvements in Blast Fragmentation Using Measurement While

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2009).

62

63

APPENDICES

Appendix 1: A sample MWD log generated from blast hole 24 of the EDZ research niche

Table 1. A sample MWD log exported from Tunnel Manager into Microsoft Excel. The

log was generated for blast hole 24 in the EDZ research niche.

Depth

(cm)

Pen.

Rate

Perc.

Pr.

Feed

Pr.

Damp.

Pr.

Rot.

Speed

Rot.

Pr.

Water

Fl.

Water

Pr.

Hard. Fract. Time

(s)

10,1 0 129,1 20,5 48,7 215,1 40,9 32,1 19,3 - - 0

20,4 101,1 125,8 38 60,7 218,3 44,3 30,9 18,9 - - 6

30,8 189,1 161,9 61 68,4 203,2 54,8 0 19,3 - - 9

41 220,6 166,5 59,7 70,6 208,6 53,5 30,1 19,3 3,917 1,255 12

51,9 233,2 170,4 58,5 68,4 212,9 51,9 0 19,3 4,273 0,175 15

62,1 241,8 169,1 58,5 66,3 218,3 54 30,4 19,3 3,355 -0,091 17

72,3 218,9 172,1 58,9 66,3 215,1 53,5 30,1 19,8 3,755 -0,033 20

83 229 171,2 58,9 67,2 211,8 50,6 30,1 19,3 4,101 0,256 23

94,1 239,4 172,5 58,5 67,2 215,1 49,3 30,1 19,3 3,607 0,507 26

104,5 223,7 170 60,6 66,7 203,2 54,4 0 19,8 2,58 0,244 28

115,2 194,4 168,7 60,2 65,4 215,1 53,5 0 19,8 3,304 0,39 32

126,2 216,4 171,6 60,2 67,2 211,8 51,9 0 19,8 3,402 -0,334 35

137,1 215,9 167,8 60,2 68 207,5 51,9 30,1 19,8 3,303 -0,333 38

148 214,1 169,1 59,7 66,3 209,7 50,2 30,4 19,3 3,166 -0,729 41

158,7 209,89 168,7 60,2 66,3 211,8 52,3 30,6 19,8 2,944 -0,711 44

169,1 206 171,6 60,6 67,2 216,1 51,9 30,4 19,8 3,153 -0,693 47

179,8 209,89 169,5 59,7 66,3 214 51,4 0 19,8 3,246 -0,809 50

190,6 214,4 172,1 58,9 64,6 211,8 52,3 0 19,8 3,315 -0,922 53

201,6 215,1 170,8 60,6 68 214 50,6 31,1 20,2 3,451 -0,321 56

211,7 218,7 170,8 59,3 67,6 210,7 50,2 30,2 19,8 4,615 0,538 59

222,3 249,1 169,1 58,9 65,9 210,7 49,3 31,2 20,2 3,284 0,586 61

233,1 213,2 170 61 65 212,9 54,4 30,2 19,8 3,444 0,519 65

243,2 218,1 170,8 60,2 65,4 211,8 49,7 30,2 19,8 3,146 0,275 67

253,9 209,8 170,8 61 64,6 211,8 53,5 30,9 19,8 3,138 -0,281 70

264,5 208,79 170 61 66,3 210,7 52,3 30,6 19,3 3,316 -0,239 73

275,3 212,5 168,7 60,6 65 211,8 54,8 32,1 19,3 2,374 -0,031 76

285,6 187,6 170 61,4 65,9 215,1 54 31,8 20,2 3,355 0,067 80

296,5 215,3 171,2 59,7 64,6 209,7 51,9 31,8 19,8 3,217 -0,005 83

307,2 211,7 171,6 60,6 65,9 209,7 50,2 31,6 19,8 3,173 0,068 86

317,8 207 167,4 61 65,9 205,3 52,7 0 13,6 2,743 0,09 89

64

Table 2. MWD log generated for blast hole 24 of the EDZ research niche (cont.).

328,6 197,8 170,8 60,6 67,6 210,7 48,1 32,1 20,2 3,38 0,058 92

339,5 212,7 167,8 60,2 65 211,8 54,4 31,2 19,3 3,194 0,054 95

350 208,5 169,1 61 67,2 211,8 51 30,2 19,3 3,512 0,594 98

360,1 217,1 169,1 59,7 66,7 206,4 50,6 31,4 19,8 5,225 0,514 101

371,4 265,89 171,6 54,6 59 211,8 48,5 31,4 19,3 6,507 2,246 104

381,5 300,89 171,6 54,6 60,3 215,1 42,2 31,1 19,3 4,69 2,632 106

392,2 251,7 172,5 61 67,2 215,1 54,8 30,1 19,3 4,233 2,057 108

403,2 236,29 169,1 58,5 64,2 209,7 45,5 30,6 19,8 5,526 2,095 111

413,6 273,6 171,6 57,2 64,2 210,7 45,1 31,1 20,2 5,41 1,792 113

423,9 268,6 169,5 57,2 62,9 211,8 45,1 31,4 19,8 3,495 1,394 116

434,7 214,3 167,4 59,7 66,3 209,7 50,2 30,7 19,3 - - 119

445,3 207 170,4 61 67,2 208,6 57,3 0 19,3 - - 122

451,2 199,5 171,2 60,6 66,3 202,1 54,4 30,4 19,8 - - 123

65

Appendix 2: Sample test data obtained from drillhole OL-KR10 including test results for a variety of tests such as Uniaxial Compression Strength, Indirect Brazilian Strength or Direct Tension

Table 3. Sample test data obtained from drillhole OL-KR10, page 1.

Borehole Specimen Rock Test Elastic parameter

name depth Type Type diameter Control Loading rate Date E n

( m ) ( mm ) ( MPa / s ) ( d.m.y ) ( GPa ) ( )

KR10 394,13 MGN Direct Tension AE 62 Actuator Displacement0,75 25.9.1996 36,7 0,05

KR10 394,29 MGN Uniaxial AE 62 Radial Strain Rate0,75 12.9.1996 57,0 0,28

KR10 395,57 DGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 395,61 VGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 395,64 VGN Triaxial Sc=1 62 Radial Strain Rate0,75 14.5.1996 52,0 0,26

KR10 395,80 VGN Triaxial Sc=5 62 Radial Strain Rate0,75 15.5.1996 55,0 0,25

KR10 397,16 VGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 397,20 VGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 397,23 MGN Triaxial Sc=0.5 62 Radial Strain Rate0,75 13.5.1996 69,9 0,19

KR10 397,39 VGN Triaxial Sc=3 62 Radial Strain Rate0,75 15.5.1996 66,7 0,20

KR10 397,55 VGN Triaxial Sc=15 62 Radial Strain Rate0,75 23.5.1996 67,8 0,22

KR10 397,88 MGN Direct Tension AE 62 Actuator Displacement0,75 25.9.1996 49,2 0,07

KR10 398,16 VGN Direct Tension AE 62 Actuator Displacement0,75 25.9.1996 35,0 0,06

KR10 398,77 MGN Triaxial Sc=0.5 62 Radial Strain Rate0,75 22.5.1996 72,0 0,19

KR10 398,93 VGN Triaxial Sc=3 62 Radial Strain Rate0,75 27.5.1996 72,2 0,17

KR10 399,09 VGN Triaxial Sc=15 62 Radial Strain Rate0,75 20.6.1996 70,8 0,19

KR10 399,26 VGN Uniaxial Damage Controlled62 Radial Strain Rate0,75 7.6.1996 75,2 0,33

KR10 399,75 MGN Uniaxial 62 Radial Strain Rate0,75 15.4.1996 68,1 0,23

KR10 400,01 VGN (MGN)Uniaxial 62 Radial Strain Rate0,75 22.4.1996 63,8 0,30

KR10 400,17 VGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 23.10.1996 68,1 0,21

KR10 400,55 MGN Uniaxial Damage Controlled62 Radial Strain Rate0,75 10.6.1996 68,0 0,26

KR10 401,68 MGN Triaxial Sc=0.5 62 Radial Strain Rate0,75 29.5.1996 76,5 0,19

KR10 401,84 MGN Triaxial Sc=3 62 Radial Strain Rate0,75 24.6.1996 75,0 0,16

KR10 402,01 MGN Uniaxial AE 62 Radial Strain Rate0,0075 8.10.1996 73,3 0,30

KR10 402,17 MGN Uniaxial AE 62 Radial Strain Rate0,0075 11.10.1996 66,7 0,23

KR10 402,51 VGN Uniaxial AE 62 Radial Strain Rate0,0075 27.9.1996 66,4 0,27

KR10 404,75 MGN Uniaxial 62 Radial Strain Rate0,75 22.4.1996 62,2 0,27

KR10 404,91 MGN Uniaxial 62 Radial Strain Rate0,0075 22.4.1996 58,9 0,27

KR10 406,00 MGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 48,1 0,04

KR10 406,36 VGN Uniaxial 62 Radial Strain Rate0,75 23.4.1996 68,8 0,25

KR10 413,29 PGR Uniaxial 62 Radial Strain Rate0,75 23.4.1996 64,9 0,26

KR10 418,38 PGR Indirect Brazil 62 Actuator Displacement0,2 14.8.1996

66

Table 4. Sample test data obtained from drillhole OL-KR10, page 2.

Conf. pres. Critical stress states Density T. strength Source Leuk% in sample Description Angle between Angle between

s3 sCI sCD sP r sT rupture vs. sample rupture vs. foliation

( MPa ) ( MPa ) ( MPa ) ( MPa )(kg/m3) (Mpa)

2765 -5,7 2 35 80

0 47 102 119 2699 2 15 Murros seuraa kiilteen suuntausta40 0

2571 -11,0 2 70 Epävarma kivilajimääritys

2773 -13,3 2 20 Epävarma kivilajimääritys

1 42 67 77 2732 35 Murtunut leukosomi raitojen kontakteja pitkin45 20

5 44 95 95 2742 30 Poimutunut leukosomi30

2718 -6,4 2 15 Epävarma kivilajimääritys

2722 -4,5 2 20 Epävarma kivilajimääritys

0,5 49 92 95 2709 30 Murtumat leukosomissa ja sen kontakteissa. Rikkoutunut täysin.40-50 0

3 46 85 94 2713 45 Leuk. sikkuralla, murtuma leikkaa leuk.50

15 84 148 160 2783 35 Leuk. sikkuralla, murtuma keskimärin raitojen suunnassa35 0

2740 -6,9 2 15 Kaksi murtumaa näytteen molemmissa päissä. Toinen leuk pahkun kohdalla ja toinen kiven suuntatuneisuuden mukainen (MGN)80 ja 45 45 ja 0

2694 -6,1 2 30 Leuk ja kiven suuntaus ohjaa murtumista80

0,5 54 99 112 2731 <5 Murtuma seuraa kiven kiilteiden suuntausta.45 0

3 49 114 126 2742 15 Paleosomi (MGN) suuntautunut, joka ohjaa murtumaa30 0

15 102 148 166 2781 15 Pilkkoutunut leuk raita ja kiven heikko suuntaus ohjaa murtumaa20 10

0 66 106 119 2738 2 15

0 51 85 123 2685 2 <5 Murros vain näytteen laidoissa simpukkamaisina murroksina0

0 50 85 105 2720 2 10 Kivi pääosin kuin MGN, bud leuk raita ohjannut murtumista.45 0

3 60 122 130 2736 30 20

0 59 113 144 2742 2 5

0,5 69 154 168 2730 Kiven suuntaus ei näyttäisi ohjaavan murtumista, murros näytteen laidassa.0-10

3 75 143 169 2726 0 Heikko suuntautuneisuus, joka ei ohjaa murtumaa0-20 90

0 59 86 133 2686 2 <5 Hieno-keskirak. leuk<5%0

0 59 107 117 2693 2 <5 Hieno-keskirak. leuk<5%. Murros pääosin pirkin näytettä, muutama haara kiven suuntautuneisiiden kukaan0 (45) (0)

0 64 112 137 2672 2 35 Keskirak. leuk.35%, murtunut paikoin leuk raitoja pitikin - päärako näytteen suuntainen.0 0-45

0 50 97 116 2715 2 Haamumainen foliaatio ohjannut murtumista.10 0-15

0 40 77 97 2714 2 Hienorakeinen, ei leukosomia. Otettu VGN:stä leukosomittomasta kohdasta.45

2762 -8,1 2 5 Katkennut leuk raidan kontaktia pitkin80 0

0 61 134 162 2699 2 25 Leukosomi ei ohjaa murtumista0-10 30-40

0 49 93 122 2644 2 100

2594 -5,5 2 100 Näyteessä reilusti piniittiä tai illiittiä

67

Table 5. Sample test data obtained from drillhole OL-KR10, page 3.

Borehole Specimen Rock Test Elastic parameter

name depth Type Type diameter Control Loading rate Date E n

( m ) ( mm ) ( MPa / s ) ( d.m.y ) ( GPa ) ( )

KR10 418,42 PGR Uniaxial Damage Controlled62 Radial Strain Rate0,75 13.1.1900 64,6 0,37

KR10 418,59 PGR Indirect Brazil 62 Actuator Displacement0,2 14.8.1996

KR10 418,63 PGR Indirect Brazil 62 Actuator Displacement0,2 14.8.1996

KR10 419,29 MGN Uniaxial Dry AE 62 Radial Strain Rate0,75 26.9.1996 71,9 0,26

KR10 420,40 MGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 420,45 MGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 24.10.1996 73,2 0,18

KR10 421,18 MGN Triaxial Sc=0.5 62 Radial Strain Rate0,75 26.6.1996 68,1 0,17

KR10 422,61 MGN Triaxial Sc=15 62 Radial Strain Rate0,75 2.9.1996 75,6 0,17

KR10 422,77 MGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 71,1 0,09

KR10 422,94 MGN Uniaxial AE 62 Radial Strain Rate0,75 18.9.1996 77,3 0,23

KR10 423,34 PGR Triaxial Sc=3 Damage Controlled62 Radial Strain Rate0,75 6.11.1996 68,7 0,23

KR10 428,78 VGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 428,81 VGN Triaxial Sc=1 62 Radial Strain Rate0,75 22.5.1996 65,4 0,35

KR10 428,98 VGN Triaxial Sc=5 62 Radial Strain Rate0,75 28.5.1996 44,0 0,19

KR10 429,14 VGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 59,8 0,11

KR10 432,49 MGN Triaxial Sc=1 62 Radial Strain Rate0,75 17.6.1996 50,3 0,24

KR10 432,65 VGN Triaxial Sc=5 62 Radial Strain Rate0,75 25.6.1996 54,9 0,21

KR10 432,81 VGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 44,6 0,03

KR10 433,35 VGN Triaxial Sc=15 62 Radial Strain Rate0,75 3.9.1996 60,7 0,19

KR10 433,64 VGN Uniaxial AE 62 Radial Strain Rate0,75 13.9.1996

KR10 434,58 VGN Triaxial Sc=3 62 Radial Strain Rate0,75 3.9.1996 52,8 0,20

434,86 VGN Uniaxial AE 62 Radial Strain Rate0,0075 26.9.1996 38,7 0,34

KR10 435,24 VGN Uniaxial Dry AE 62 Radial Strain Rate0,75 26.9.1996 57,1 0,28

KR10 435,85 DGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 435,89 MGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 45,3 0,05

KR10 436,53 DGN Uniaxial 62 Radial Strain Rate0,75 23.4.1996 45,3 0,32

KR10 436,69 MGN Uniaxial 62 Radial Strain Rate0,0075 24.4.1996 44,7 0,29

KR10 437,03 DGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 437,07 DGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 437,10 VGN Triaxial Sc=1 62 Radial Strain Rate0,75 26.6.1996 62,8 0,16

KR10 437,26 MGN Triaxial Sc=5 62 Radial Strain Rate0,75 4.9.1996 54,6 0,21

KR10 437,42 VGN/MGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 46,3 0,08

68

Table 6. Sample test data obtained from drillhole OL-KR10, page 4.

Conf. pres. Critical stress states Density T. strength Source Leuk% in sample Description Angle between Angle between

s3 sCI sCD sP r sT rupture vs. sample rupture vs. foliation

( MPa ) ( MPa ) ( MPa ) ( MPa )(kg/m3) (Mpa)

0 59 90 114 2595 2 100

2564 -4,4 2 100 Näyteessä rakeiden laidoilla piniittiä tai illiittiä

2578 -5,3 2 100 Näyteessä reilusti piniittiä tai illiittiä

0 86 156 184 2708 2 Heikko haamumainen raidoitus, joka ei näyttäisi ohjaavan murtumista0-10

2696 -12,6 2 0 Epävarma kivilajimääritys

3 58 121 129 2741 0 20

0,5 61 88 88 2773 10 Muutama heikkä leuk. raita, jotka ei ohjaa murtumista0-10 30

15 98 223 238 2762 0 Homogeeninen näyte20

2739 -9,8 2 <5 80

0 94 158 176 2720 2 Kivi murskautunut toisesta päästään.80

3 68 147 179 2620 100 Hieman piniittiä tai jotakin töhkää

2716 -7,3 2 25 Epävarma kivilajimääritys

1 31 31 39 2750 25 Murtuma leuk. kontaktia pitkin30 0-10

5 42 72 72 2698 35 Poimutunut ja budinoitunut leukosomi40 0

2748 -10,7 2 30 Murtunut osittain leuk raitaa pitkin. Runsaasti kiillettä.50

1 36 61 65 2753 5 Suuntautunut MGN40 0

5 48 67 70 2732 10 Suuntautunut tasomaisesti, keskirakeinen45 0

2707 -8,2 2 35 Murros leukosomissa75 0

15 58 111 112 2703 10 Tasomainen suuntaus40 0

0 36 2785 2 20 Kiven suuntaus ohjaa murtumista45 0

3 38 69 71 2761 <15 Runsaasti paleosomia sis. näyte, jossa melko voimakas suuntautuneisuus25 10

0 34 40 45 2769 2 40 Keskirakeinen, murrosleikkaa paikoin foliaation.0 0-30

0 51 94 116 2791 2 15 Kiven suutautuneisuus ohjaa murtumista35 0-15

2772 -13,4 2 25 Epävarma kivilajimääritys

2733 -6,2 2 <5

0 35 69 88 2794 2 Muuttunut kivi jolle on vaikea määrittää Posivalaista nimeä (432.2-439.3).50

0 36 64 78 2780 2 Hienorakeinen, yksi leuk raita näytteen laidassa -ei vaikuta murtumiseen.50

2764 -12,3 2 40 Epävarma kivilajimääritys

2774 -10,5 2 40 Epävarma kivilajimääritys

1 63 82 83 2745 10 Hyvin vähän leuk.45 15

5 64 87 96 2706 <5 Epämääräinen homogeenisen näköinen kivi45

2683 -7,4 2 15 Omituinen kivi90

69

Table 7. Sample test data obtained from drillhole OL-KR10, page 5.

Borehole Specimen Rock Test Elastic parameter

name depth Type Type diameter Control Loading rate Date E n

( m ) ( mm ) ( MPa / s ) ( d.m.y ) ( GPa ) ( )

KR10 438,78 VGN Uniaxial AE 62 Radial Strain Rate0,75 24.9.1996 50,4 0,33

KR10 438,94 VGN Uniaxial Dry AE 62 Radial Strain Rate0,75 24.9.1996 55,1 0,25

KR10 439,10 VGN Uniaxial AE 62 Radial Strain Rate0,0075 24.9.1996 48,1 0,30

KR10 441,28 VGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 28.10.1996 52,9 0,20

KR10 441,58 VGN Uniaxial Damage Controlled62 Radial Strain Rate0,75 1996 54,9 0,28

KR10 441,80 VGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 441,85 VGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 29.10.1996 49,7 0,21

KR10 442,66 VGN Uniaxial 62 Radial Strain Rate0,0075 25.4.1996 55,6 0,28

KR10 442,92 MGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 48,2 0,06

KR10 550,89 VGN/DGN Triaxial Sc=1 62 Radial Strain Rate0,75 15.5.1996 49,8 0,22

KR10 551,05 MGN Triaxial Sc=5 62 Radial Strain Rate0,75 16.5.1996 57,6 0,25

KR10 552,18 VGN Triaxial Sc=1 62 Radial Strain Rate0,75 23.5.1996 71,3 0,20

KR10 552,34 VGN Triaxial Sc=5 62 Radial Strain Rate0,75 28.5.1996 60,1 0,28

KR10 553,01 MGN Triaxial Sc=0.5 62 Radial Strain Rate0,75 14.5.1996 71,3 0,22

KR10 553,17 VGN Triaxial Sc=3 62 Radial Strain Rate0,75 15.5.1996 60,7 0,23

KR10 553,33 MGN Triaxial Sc=15 62 Radial Strain Rate0,75 23.5.1996 - -

KR10 555,80 VGN Triaxial Sc=0.5 62 Radial Strain Rate0,75 22.5.1996 65,7 0,20

KR10 555,96 VGN Triaxial Sc=3 62 Radial Strain Rate0,75 28.5.1996 61,6 0,19

KR10 556,13 MGN Triaxial Sc=15 62 Radial Strain Rate0,75 24.6.1996 55,1 0,20

KR10 557,46 MGN Triaxial Sc=0.5 62 Radial Strain Rate0,75 17.6.1996 58,0 0,19

KR10 557,62 MGN (VGN)Triaxial Sc=3 62 Radial Strain Rate0,75 25.6.1996 70,7 0,17

KR10 557,78 MGN Triaxial Sc=15 62 Radial Strain Rate0,75 3.9.1996 69,6 0,16

KR10 558,70 MGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 30.10.1996 66,4 0,21

KR10 558,97 MGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 31.10.1996 62,3 0,17

KR10 559,13 VGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 559,17 VGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 559,53 MGN Direct Tension AE 62 Actuator Displacement0,75 25.9.1996 17,7 -0,01

KR10 559,70 MGN Uniaxial Damage Controlled62 Radial Strain Rate0,75 1996 52,2 0,30

KR10 562,43 VGN Uniaxial 62 Radial Strain Rate0,75 22.4.1996 61,7 0,28

KR10 562,66 VGN Uniaxial 62 Radial Strain Rate0,0075 24.4.1996 57,1 0,27

KR10 563,28 MGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 44,3 0,05

KR10 563,44 MGN Uniaxial 62 Radial Strain Rate0,0075 15.4.1996 58,1 0,29

70

Table 8. Sample test data obtained from drillhole OL-KR10, page 6.

Conf. pres. Critical stress states Density T. strength Source Leuk% in sample Description Angle between Angle between

s3 sCI sCD sP r sT rupture vs. sample rupture vs. foliation

( MPa ) ( MPa ) ( MPa ) ( MPa )(kg/m3) (Mpa)

0 40 63 76 2809 2 20 Murros seuraa kiilteen/fol/leuk. suuntausta10 0-10

0 44 86 91 2782 2 30 Murros seuraa leuk raidoitusta10-45 0-20

0 33 43 48 2768 2 15 Keskirakeinen, murtunut pääasiallisesti kiilteiden mukaan.0-30 0(-60)

3 31 76 80 2745 25 20

0 56 75 76 2696 2 15 Kiven suuntaus ohjaa murtumista45 0

2810 -9,5 2 15 Epävarma kivilajimääritys

3 49 67 68 2737 15 Suuntautuneisuus ohjaa murtumaa25 20

0 38 65 78 2747 2 30 Keskirakeinen, leuk 30 %, budiineina50 30

2707 -7,6 2 <5 Kohtuudella suuntautunut45 0

1 48 75 105 2705 10 Hieno-keskirak. homogeenisen näköinen kivi jossa vähän leukosomia20

5 48 111 137 2730 10 Raitainen ja tasomaissesti suuntautunut, ei ohjaa murtumaa45 50

1 55 75 91 2781 15 Tasomainen raidoitus40 0

5 38 58 95 2720 10 Tasomainen raidoitus, joka ohjaa murtumaa45 0

0,5 46 60 79 2756 <3 Heikosti raitainen, jossa raidat ohjaavat murtumista45 0

3 42 81 93 2760 <10 Runsaasti paleosomia sis. näyte, jossa melko voimakas suuntautuneisuus10 0

- - - - 2717 <5 Suuntautunut näyte, voi olla osa VGNää0-45 0

0,5 44 81 97 2820 30 Hienorakeinen neosomi40 0

3 62 102 104 2801 15 Runsaasti paleosomia sis. näyte, jossa melko voimakas suuntautuneisuus. 10 0

15 57 119 127 2721 10 Suuntautunut MGN, jossa suuntaus ei ohjaa murtumaa40 30

0,5 42 77 84 2714 Hienorakeinen, raitainen, heterogeeninen tekstuuri45 30

3 58 116 121 2724 <10 Pääosin paleosomia (MGN), joka heikosti suuntautunut45 0

15 88 150 156 2731 10 Suuntautunut MGN, jossa suuntaus ei ohjaa murtumaa50

3 46 108 115 2709 5 20 30

3 53 102 108 2769 5 40 0

2751 -9,1 2 10 Epävarma kivilajimääritys, Pääosin MGN

2722 -7,6 2 85 Epävarma kivilajimääritys, Pääosin leukosomia

2763 -6,1 2 10 Leuk ja kiven suuntaus ohjaa murtumista55 0

0 40 75 89 2720 2 0 Kiven suuntautuneisuus ei ohjaa 0 80

0 43 87 94 2742 2 50 Pääosin leukosomia, joka osin ohjaa murtumista45 0-15

0 44 69 91 2713 2 25 Keskirakeinen, leuk 25%, murtuma fol suuntainen70 0

2767 -6,8 2 <5 Massiivinen, heikko suuntaus40 0

0 40 66 82 2739 2 5 Keskirakienen, leuk 5%. Murtunut ureasta kohdasta.

71

Table 9. Sample test data obtained from drillhole OL-KR10, page 7.

Borehole Specimen Rock Test Elastic parameter

name depth Type Type diameter Control Loading rate Date E n

( m ) ( mm ) ( MPa / s ) ( d.m.y ) ( GPa ) ( )

KR10 564,96 MGN Uniaxial AE 62 Radial Strain Rate0,75 25.9.1996 60,9 0,29

KR10 565,12 MGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 565,16 MGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 565,20 MGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 565,37 VGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 44,6 0,03

KR10 566,15 VGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 50,8 0,06

KR10 566,31 VGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 31.10.1996 68,2 0,19

KR10 566,47 VGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 1.11.1996 66,7 0,24

KR10 567,91 MGN/DGN Triaxial Sc=1 62 Radial Strain Rate0,75 20.6.1996 66,1 0,17

KR10 568,07 VGN Triaxial Sc=5 62 Radial Strain Rate0,75 25.6.1996 67,1 0,19

KR10 569,79 VGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 30,9

KR10 569,95 VGN Uniaxial Damage Controlled62 Radial Strain Rate0,75 1996 72,4 0,28

KR10 570,83 VGN Triaxial Sc=0.5 62 Radial Strain Rate0,75 26.6.1996 66,8 0,17

KR10 570,99 VGN Triaxial Sc=3 62 Radial Strain Rate0,75 3.9.1996 55,2 0,23

KR10 571,15 VGN Triaxial Sc=15 62 Radial Strain Rate0,75 4.9.1996 58,1 0,20

KR10 571,39 MGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 571,43 MGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 571,46 DGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 33,4 0,03

KR10 571,62 DGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 19,9 0,02

KR10 571,78 VGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 574,15 VGN Uniaxial 62 Radial Strain Rate0,75 24.4.1996 56,6 0,31

KR10 574,31 VGN Uniaxial 62 Radial Strain Rate0,0075 26.4.1996 60,7 0,28

KR10 574,47 MGN Uniaxial Damage Controlled62 Radial Strain Rate0,75 1996 61,8 0,27

KR10 574,63 MGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 4.11.1996 62,3 0,21

KR10 574,79 VGN Indirect Brazil - PaS 62 Actuator Displacement0,2 14.8.1996

KR10 574,82 VGN Indirect Brazil - PeS 62 Actuator Displacement0,2 14.8.1996

KR10 575,15 MGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 47,9 0,05

KR10 575,31 MGN Triaxial Sc=3.0 Damage Controlled62 Radial Strain Rate0,75 5.11.1996 70,7 0,18

KR10 575,47 VGN Uniaxial AE 62 Radial Strain Rate0,75 24.9.1996 61,8 0,29

KR10 576,39 VGN Direct Tension 62 Actuator Displacement0,75 15.7.1996 51,6 0,08

KR10 576,55 VGN Triaxial Sc=15 62 Radial Strain Rate0,75 20.6.1996 73,5 0,15

KR10 581,43 VGN Uniaxial 62 Radial Strain Rate0,75 23.4.1996 55,5 0,32

72

Table 10. Sample test data obtained from drillhole OL-KR10, page 8.

Conf. pres. Critical stress states Density T. strength Source Leuk% in sample Description Angle between Angle between

s3 sCI sCD sP r sT rupture vs. sample rupture vs. foliation

( MPa ) ( MPa ) ( MPa ) ( MPa )(kg/m3) (Mpa)

0 54 105 129 2700 2 5 Murtumat näytteen keskellä jossa 1 leuk raita0-10

2751 -10,4 2 15 Epävarma kivilajimääritys

2747 -12,9 2 10 Epävarma kivilajimääritys

2745 -10,9 2 0 Epävarma kivilajimääritys

2753 -6,3 2 15 Vähän leuk, voimakas suuntaus45 0

2735 -9,6 2 15 Vähän leuk, voimakas suuntaus45 0

3 58 122 128 2750 30 20 20

3 69 109 117 2728 30 20

1 51 93 105 2730 0 KEskirakeinen, liian tumma kivi...40

5 59 103 106 2730 <10 Liian tumma näyte, jossa heikko raidoitus - ohjaa murtumaa30 0

2711 -2,7 2 40 Leuk väliin jäävä kiilleosuus ohjaa murtuimista45 0

0 62 98 111 2746 2 15 Leukosomi osin illiittiytynyt0 70

0,5 44 76 86 2732 40 Murtunut leuk raitoja pitkin - kontakteissa0-20

3 47 61 63 2725 50 Murtuma seuraa kiven kiilteiden suuntausta, joka leikkaa (5 astetta) leuk raitoja 45 5

15 58 115 118 2690 20 Epämääräinen leukosomi50

2725 -6,9 2 5 Epävarma kivilajimääritys

2713 -7,5 2 5 Epävarma kivilajimääritys

2732 -6,3 2 70 Sekainen näyte85

2782 -3,3 2 70 Sekainen näyte70

2740 -6,4 2 50 Epävarma kivilajimääritys

0 43 87 95 2746 2 15 Liuskeisuus ohjaa murtumista30 0

0 42 72 90 2737 2 10 Murtunut leuk raitaa pitkin0 0

0 45 85 90 2761 2 0 Homogeeninen näyte jossa hyvin heikko suuntaus20 0

3 61 109 116 2762 <10 Hieno/keskirakeinen näyte

2726 -11,1 2 30 Epävarma kivilajimääritys

2713 -13,5 2 25 Epävarma kivilajimääritys

2760 -8,2 2 <10 Hienorakeinen ja suuntautunut60 0

3 75 117 117 2717 <10 Hieno/keskirakeinen näyte20 10

0 44 98 116 2718 2 25 Murtuma näytteen alalaidassa, jossa murrosta ohjaa kiilteet.45 0-10

2738 -9,3 2 15 Vähän leuk, voimakas suuntaus70

15 75 140 145 2699 10 Näytteessä 1 leuk raita, joka ohjaa murtumaa20 0

0 41 83 105 2735 2 25 Fol ei ohjaa murtumista25 90