TERO Borehole Logging Device and Test ... - Posiva · TERO Borehole Logging Device and Test...

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P h o n e ( 0 2 ) 8 3 7 2 3 1 ( n a t . ) , ( + 3 5 8 - 2 - ) 8 3 7 2 3 1 ( i n t . )

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TERO Borehole Logging Deviceand Test Measurements of RockThermal Properties in Olkiluoto

November 2005

POSIVA 2005 -09

I lmo KukkonenI l kka Suppa la

Arto Korp i sa loTeemu Kosk inen

POSIVA 2005-09

November 2005

POSIVA OY

F I - 27160 OLK I LUOTO, F INLAND

Phone (02 ) 8372 31 (na t . ) , ( +358 -2 - ) 8372 31 ( i n t . )

Fax (02 ) 8372 3709 (na t . ) , ( +358 -2 - ) 8372 3709 ( i n t . )

I lmo Kukkonen , I l kka Suppa la , Ar to Korp i sa lo

Geo log i ca l Su rvey o f F i n l and

Teemu Kosk inen

St ips Oy

TERO Borehole Logging Deviceand Test Measurements of RockThermal Properties in Olkiluoto

ISBN 951-652 -141 -XISSN 1239-3096

The conc lus ions and v i ewpo in ts p resen ted i n the r epo r t a r e

those o f au tho r ( s ) and do no t necessa r i l y co inc ide

wi th those o f Pos i va .

Tekijä(t) – Author(s)

Ilmo Kukkonen, Ilkka Suppala, Arto Korpisalo Geological Survey of Finland Teemu Koskinen Stips Oy

Toimeksiantaja(t) – Commissioned by Posiva Oy

Nimeke – Title

TERO BOREHOLE LOGGING DEVICE AND TEST MEASUREMENTS OF ROCK THERMAL PROPERTIES IN OLKILUOTO Tiivistelmä – Abstract A new thermal drill hole device for determining thermal properties of rocks in situ in slim boreholes, and results of measurements in the OL-KR2 drill hole in Olkiluoto are presented in this report. The constructed device is based on conduction of heat from a cylindrical heat source placed in a borehole with 56 mm diameter. The down-hole probe is built of a hollow aluminium cylinder, which is 1630 mm long and whose outer diameter is 50 mm. A foil-like heating resistor is placed on the inner surface of the hollow cylinder. The length of the heating section of the cylinder is 1.5 m. The applied maximum heating power is 22 W. Heating power is monitored during measurements. 28 NTC thermistors are placed in four lines along the inner surface of the aluminium cylinder. In addition four thermistors are placed on the circuit boards for monitoring the background heating produced by the electronics. Convection in the hole is prevented with soft silicon rubber packers. The instrument is pressure-proof up to 700 m in water-filled holes. The measurements in Olkiluoto borehole OL-KR2 were carried out in May-June 2004 and September, 2004. Twenty-two 1.5 m long sections were selected for the measurements. The main rock type in the borehole is migmatitic mica gneiss together with grey granite and pegmatitic gneiss. The in situ measurements comprise of a 6-hour heating period followed by a 12-hour cooling period. The measurements were interpreted with numerical models of conductive heat transfer from a cylindrical source. To compare the in situ measurements with drill core data, laboratory measurements were made as well. Three pieces of core were chosen corresponding to each in situ measurement section. Thermal conductivity was measured in two ways, first along the core axis, which in most cases is perpendicular to schistosity, and second, along schistosity from a sample drilled off from the cores. The results indicate a distinct anisotropy of thermal conductivity, with an average anisotropy factor of 1.25 ± 0.25. Because the in situ thermal conductivity estimates correspond to conductivity in the radial direction from the borehole, this must be taken into account in the comparison between laboratory and borehole measurements. Therefore thermal conductivity determined in situ was higher than the values measured from cores along core axis, but in a reasonable agreement with values determined perpendicular to borehole axis. The drill hole values of conductivity are about 0.3 – 0.5 W m-1 K-1 higher than laboratory data. Diffusivity estimates are 0.1 – 0.2·10-6 m2 s-1 lower. The estimation of diffusivity (and specific heat capacity) is more problematic due to the strong correlation of the contact resistance effects and diffusivity, as well as the poorly known hole calliper. Further development is needed for improved parameter estimation.

Avainsanat - Keywords

thermal conductivity, specific heat capacity, thermal diffusivity, nuclear waste disposal, Olkiluoto, mica gneiss, in situ measurement ISBN ISBN 951-652-141-X

ISSN ISSN 1239-3096

Sivumäärä – Number of pages 96

Kieli – Language English

Posiva-raportti – Posiva Report Posiva Oy FI-27160 OLKILUOTO, FINLAND Puh. 02-8372 (31) – Int. Tel. +358 2 8372 (31)

Raportin tunnus – Report code

POSIVA 2005-09 Julkaisuaika – Date

November 2005

Tekijä(t) – Author(s)

Ilmo Kukkonen, Ilkka Suppala, Arto Korpisalo Geologian tutkimuskeskus Teemu Koskinen Stips Oy

Toimeksiantaja(t) – Commissioned by Posiva Oy

Nimeke – Title

TERO-REIKÄLUOTAUSLAITTEISTO JA TESTIMITTAUKSET KALLION TERMISISTÄ OMINAISUUKSISTA OLKILUODOSSA Tiivistelmä – Abstract

Tässä työssä esitellään uusi kallion termisten ominaisuuksien in situ mittaamiseen tarkoitettu laitteisto ”TERO” sekä Olkiluodon kairareiässä OL-KR2 tehtyjen mittausten tulokset. Laite perustuu sylinterimäi-sen lämpölähteen lämpötilavasteeseen, joka riippuu lämmitystehon lisäksi ympäröivän väliaineen läm-mönjohtavuudesta ja diffusiviteetistä. Reikäanturi on suunniteltu halkaisijaltaan 56 mm kairanreikiin. Anturi on rakennettu alumiiniputkesta, jonka pituus on 1630 mm ja ulkohalkaisija 50 mm. Kalvomainen lämmitin, jonka pituus on 1500 mm, on asennettu putken sisäpinnalle. Lämmitysteho on maksimissaan 22 W. 28 NTC-pintaliitostermistoria on asennettu neljään jonoon pitkin alumiiniputken sisäpintaa. Neljä elektroniikkakorteille sijoitettua termistoria monitoroi elektroniikan tuottamaa taustalämpöä. Lämmitys-tehoa mitataan jatkuvasti. Veden pystysuuntainen virtaus reiässä estetään pehmeiden silikonikumipakke-reiden avulla. Laite on paineenkestävä 700 m syvyyteen saakka. Olkiluodon kairareikä OL-KR2:n mittaukset tehtiin touko-kesäkuussa ja syyskuussa 2004. Kaksikym-mentä kaksi 1.5 m pitkää mittausväliä valittiin mittauksiin. Kairareiän pääkivilaji on kiillegneissi, minkä lisäksi reikä lävistää harmaita graniitteja ja pegmatiittia. Kairareikämittaukset koostuivat n. 6 tunnin pi-tuisesta lämmitysjaksosta ja 12 tuntia kestäneestä jäähtymisjaksosta. Mittausten tulkinta tapahtui numee-risten lämmönsiirtomallien avulla. Jotta reikämittauksia voidaan verrata aikaisempiin termisten ominai-suuksien tutkimuksiin Olkiluodon kairasydännäytteistä, tehtiin myös laboratoriomittauksia. Kultakin mittausväliltä valittiin systemaattisesti kolme näytettä. Lämmönjohtavuus mitattiin sekä reiän suunnassa että sitä vastaan kohtisuorassa suunnassa pitkin liuskeisuutta. Kohtisuoraan suuntaan tehtyjä mittauksia varten alkuperäisistä halkaisijaltaan 42 mm:n kairasydämistä kairattiin 24 mm:n näytteet. Mittaustulokset osoittavat, että kiillegneissi on termisesti anisotrooppinen ja että anisotropiakerroin on keskimäärin 1.25 ± 0.25.

Koska kairareikämittauksessa tulee esiin ennen kaikkea reikää vastaan kohtisuorassa suunnassa oleva lämmönjohtavuus, on se otettava huomioon verrattaessa laboratorio- ja reikämittauksia. Reikämittauksista määritetty lämmönjohtavuus on korkeampi kuin kairareikää pitkin laboratorionäytteillä mitattu johtavuus. Reikätuloksista määritetyt johtavuusarvot olivat noin 0.3 – 0.5 W m-1 K-1 korkeampia kuin laboratorio-mittaukset liuskeisuuden suunnassa. Vastaavasti diffusiviteettitulokset ovat noin 0.1 – 0.2·10-6 m2 s -1 al-haisempia kuin laboratoriotulokset. Termisen diffusiviteetin (ja lämpökapasiteetin) arvioiminen reikä-mittauksesta on ongelmallisempaa johtuen kontaktiresistanssi-ilmiöiden ja diffusiviteetin voimakkaasta korrelaatiosta lämmönsiirtoyhtälöissä, mutta myös huonosti tunnetun reikähalkaisijan vuoksi. Mittaustu-losten analyysimenetelmien kehitystyötä on syytä jatkaa kallion termisten ominaisuuksien mahdollisim-man tarkaksi määrittämiseksi.

Avainsanat - Keywords lämmönjohtavuus, ominaislämpökapasiteetti, terminen diffusiviteetti, ydinjätteiden loppusijoitus, kiillegneissi, Olkiluoto ISBN ISBN 951-652-141-X

ISSN ISSN 1239-3096

Sivumäärä – Number of pages 96

Kieli – Language Englanti

Posiva-raportti – Posiva Report Posiva Oy FI-27160 OLKILUOTO, FINLAND Puh. 02-8372 (31) – Int. Tel. +358 2 8372 (31)

Raportin tunnus – Report code

POSIVA 2005-09 Julkaisuaika – Date

Marraskuu 2005

1

TABLE OF CONTENTS

Abstract

Tiivistelmä

Preface ............................................................................................................ 3

1 INTRODUCTION ................................................................................................. 5

2 BOREHOLE LOGGING DEVICE “TERO” ............................................................ 7

3 MEASUREMENT PRINCIPLE AND MECHANICAL CONSTRUCTION OF THE “TERO” IN SITU LOGGING DEVICE.................................................................... 9

3.1 Principle of measuring rock thermal properties in a borehole ....................... 9

3.2 Procedure of borehole measurements........................................................ 10

3.3 The mechanical construction of the probe .................................................. 10

3.4 Heating foils and thermistors ...................................................................... 12

3.5 Electronics .................................................................................................. 13

3.6 Thermistor calibration ................................................................................. 13

3.7 TERO Graphical interface........................................................................... 15

4 TEST MEASUREMENTS WITH THE “TERO” IN SITU LOGGING DEVICE...... 19

4.1 Measurements in Olkiluoto, borehole OL-KR2............................................ 19

4.2 Laboratory measurements of drill cores...................................................... 22

4.3 Interpretation of logging results and comparison with laboratory measurements ............................................................................................ 24

4.4 Factors influencing thermal parameter estimation ...................................... 48

5 DISCUSSION AND CONCLUSIONS.................................................................. 61

REFERENCES .......................................................................................................... 65

APPENDIX A: Photographs of laboratory samples from borehole OL-KR2 ................. 67

APPENDIX B: Borehole video images of measurement sections in borehole OL-KR2... .......................................................................................................... 75

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Preface The study has been carried out at the Geological Survey of Finland (GTK) on contract for Posiva Oy. On behalf of the orderer, the supervising of the work was done by Heikki Hinkkanen, Aimo Hautojärvi, Maiju Paunonen (Posiva Oy) and Erik Johansson (Saanio & Riekkola Oy). The geophysical design, equipment, construction, measurements, software development, interpretation and reporting were done by Ilmo Kukkonen, Ilkka Suppala, Arto Korpisalo (GTK) and Teemu Koskinen (Stips Oy).

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1 INTRODUCTION Thermal parameters of rocks are necessary data in planning final repository for spent nuclear fuel in deep bedrock. The thermal properties of rocks can be determined with laboratory measurements of core samples, theoretical calculations from mineral composition and data on properties of the constituent minerals, and with in situ measurements in boreholes. Laboratory measurements and theoretical calculations on thermal properties of rocks at Olkiluoto and other disposal candidate sites in Finland have been presented previously by Kjørholt (1992) Kukkonen and Lindberg (1995, 1998) and Kukkonen (2000). A comparison between different laboratory measurements applied in site studies in Finland and Sweden has been given by Sundberg et al. (2003). In situ measurements have been under development in Posiva since 1999. Kukkonen and Suppala (1999) summarized the literature data on various in situ techniques and carried out theoretical simulations of in situ measurements. A prototype and test version of an in situ borehole tool, based on temperature response of a heated cylindrical probe, was built and applied in 1999 by Kukkonen et al. (2000 and 2001). Thermal modelling was elaborated from simple 1-dimensional infinite line source and cylindrical sources into a 2-dimensional finite element models including the internal structure of the probe, and the contact resistance layer between the in situ probe and borehole wall as well as finitely long cylinder models (3D models). Based on this experience, the first version of production logging tool was designed and constructed by Suppala et al. (2004). The logging device is called with the acronym “TERO”. The present work reports the basic properties of the TERO device and measurements carried out in a deep borehole (OL-KR2) in Olkiluoto. Further, we compare the in situ results with laboratory measurements of the corresponding drill core samples, and discuss further development of the thermal parameter estimation.

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2 BOREHOLE LOGGING DEVICE “TERO” The TERO logging device comprises the borehole tool, logging cable and winch together with the computer and current source located at the surface. The winch and steel armoured cable were purchased from the German company LogIn GmbH, and they represent standard geophysical logging instrumentation of the day. The cable is 700 m long steel armoured 4-conductor logging cable. The motorized winch is controlled from a separate control panel. In principle the winch system has an option of automated operation, but it is not included at the present TERO device. A computer collects the depth data from the winch and with the aid of the control unit measured resistances from temperature sensors, heating current and voltage as well as the resistances of the single point resistance sensor. The main components of the TERO device are shown in Figure 1. The system at the GTK test hole in Espoo is depicted in Fig. 2. Figure 1. The components of the TERO logging device.

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Figure 2. TERO logging device at the GTK test hole, Espoo. The basic properties of the TERO device are as follows:

- Determination of thermal conductivity and diffusivity in situ in slim 56 mm boreholes

- The measurement principle: Thermal response of a heated cylinder - Length of cable is 700 m, motorized winch - The outer diameter of probe is 50 mm - Length of the heated part of the probe is 1.5 m - Heating power is 22 W at maximum - Monitoring of heating power is carried out - Heating takes place with heating foils installed at the inner surface of the probe

tube - Flow of water along the measurement section is prevented with soft packers

made of silicon rubber - Number of temperature sensors is 28, located around the probe along four axial

lines - A galvanic single point sensor is included in the tool for precise determination of

logging depth with the aid of fracture anomalies - Determination of tool orientation is done with the magnetic field (3-component

flux gate sensors) and inclination (acceleration) sensors.

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3 MEASUREMENT PRINCIPLE AND MECHANICAL CONSTRUCTION OF THE “TERO” IN SITU LOGGING DEVICE 3.1 Principle of measuring rock thermal properties in a borehole Let us assume a situation where the in situ probe is initially in a borehole under thermal stationary conditions. When the probe is heated, its temperature as a function of time depends on the applied heating power, heat losses into rock, and the thickness of the water layer between the probe and borehole wall. Temperatures are also dependent on the internal structure of the probe and its material properties. In the present study the probe properties are taken into account as solid parameters of the time-dependent heat conduction model. The remaining parameters which need to be estimated are thermal properties of the surrounding medium. In a borehole, the probe is (mostly) immersed in water, and the thickness of the water layer varies with varying hole calliper. In the interpretation the water layer, acting as a heat capacitor and resistance, must be taken into account. The linear equation of conduction of heat giving the temperature T (r,t), dependent on the location r (location vector in a Cartesian coordinate system) and on time t, is (Carslaw and Jaeger 1959, Jarny et al., 1991):

),()),()((),()( tgtTt

tTcρ rrrλrr +∇⋅∇=∂

∂

(3.1)

In equation 3.1 λ is thermal conductivity, which is a tensor variable (W m-1 K-1), ρc is heat capacity (density · specific heat capacity) (J m-3 K-1) and g is heating power (W m-3). Thermal diffusivity is the ratio of thermal conductivity and heat capacity s = λ/ ρc. Dividing both sides of equation 3.1 by ρc shows that the problem can be also described in terms of thermal diffusivity and heat capacity. Temperature increase of an infinitely long cylinder having an infinite conductivity and situated in a homogeneous space can be presented with an analytical solution (Carslaw and Jaeger, 1959). Such a solution was used in the first examinations of the applications of the method as well as in the interpretations (Kukkonen and Suppala 1999; Kukkonen et al., 2000). The analytical solution, however, is not sufficient for describing the modelling of a cylinder with finite length and conductivity, nor is it able to handle the contact resistance effects between the probe and borehole wall. Therefore we have applied here numerical finite element techniques with the MATLAB® software. Our models are a (i) cylindrically symmetric, infinitely long probe (1-D element mesh), (ii) infinitely long probe (2-D element mesh) and (iii) a finite cylindrically symmetric probe (2-D element mesh). The forward modelling of the problem is carried out with the numerical solution of equation 3.1. The unknown variables, thermal conductivity or diffusivity of rock and the thermal contact resistance or alternatively, the thickness of the water layer, are estimated with the method of least squares using the measured and calculated probe

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temperatures. In previous reports (Kukkonen and Suppala, 1999; Kukkonen et al., 2000, 2001) we have discussed the interpretation of the measurements in more detail. According to sensitivity analysis of the theoretical simulations we can estimate that thermal conductivity can be determined at best with an error smaller than 2 % and diffusivity with less than 5 % error, given that the error in temperature measurements is smaller than ±0.03 K (Kukkonen et al., 2001). Determination of diffusivity depends strongly on contact resistance, i.e. the water layer thickness. In other words, this requires that the water layer thickness should be known for reliable determination of diffusivity. 3.2 Procedure of borehole measurements In practice the measurement is done as follows. First, the probe is lowered to the measurement depth in the borehole. The probe is allowed to equilibrate to the temperature for about an hour, when it has reached a steady-state condition. The heating time can be chosen freely, and longer heating times involve larger volumes into the measurement around the borehole. Theoretical considerations indicated (Kukkonen and Suppala, 1999) that, for instance, a 12-hour heating period influences the temperatures, that can be measured in practice, only to a distance of about 0.5 m from the borehole wall. Therefore, the heating time can be pre-set to as long as 6-16 hours. Temperatures are registered during lowering of probe in the hole, thermal equilibration, as well as heating and cooling of the probe. Thus, the measurement of one depth station in the borehole takes 12-24 hours. Only temperature changes during the heating and cooling periods are taken into account in the interpretation of the data. The most important temperature readings are obtained from the centre of the probe, where the approximation of an infinitely long cylinder is valid for longest times. 3.3 The mechanical construction of the probe The in situ probe is shown in Fig. 3. Thermistors and heating resistors are placed on the inner surface of the aluminium pipe. The white plastic parts have been made of “Ertacetal” polyacetate (POM). The electronic circuit boards are located in the plastic parts of the upper end of the probe. The probe is pressure resistant up to 700 m of water depth. Flow along the borehole is prevented with one-two pairs of soft packers made of silicon rubber, and produced by PRG-Tec Oy (Fig. 4).

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Figure 3. Parts and dimensions of the in situ probe (above), the constructed probe with the 1.5 m long heating and thermistor foil. The cable end is connected at the left end, and the additional weights are hanged at the right end of the probe.

Figure 4. Soft silicon rubber packers at the lower end of the probe. The screws made of bronze are used for centralizing the probe in the borehole. The outer and inner diameters of the probe are 50 and 40 mm, respectively. Cross section of the probe is given in Fig. 5. The thermal conductivity of the aluminium alloy is 201 W m-1 K-1 and heat capacity 24302700 (J m-3 K-1) according to the producer of the pipe. Respectively, producer information provides a value of 0.31 W m-1 K-1 for the thermal conductivity of the POM plastic.

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Figure 5. Cross section of the probe showing the aluminium tube, heating foil and thermistor foil. In order to enhance positioning of the probe in depth, a galvanic single-point sensor is included in the probe. The current electrode of the measurement is the steel stipule used for hanging the extra weights applied at the lower end of the probe. The weight is indispensable because the friction of the packers makes the lowering of the probe in the hole very slow. The extra weight has a mass of 20 kg. 3.4 Heating foils and thermistors The probe is heated with two heating foils (resistors) on the inner surface of the aluminium pipe. The foils provide homogeneous warming of the pipe. Thermistors and foils have been installed together. One of the heating foils with the thermistors is shown in Fig. 6. The foils were purchased from Calesco Foils Ab. The foil producer was also responsible for installing the thermistors on the foils. We applied NTC thermistors by BCcomponents (type no. 232261513103). Their nominal resistance is 10000 Ω at 25 ºC. The total number of thermistors installed along the probe on four axial lines is 28. In addition, the electronics is monitored with four thermistors installed on the circuit boards. The heating and thermistor foils are compressed against the inner surface of the aluminium pipe with a pressurized rubber inner tube of a bicycle. The length of the rubber tube is about equal to the length of the heating foils. The foils are not glued on the aluminium tube. Therefore, the heating foils and thermistors can be easily replaced and maintained.

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Figure 6. A close-up of the upper end of the heating foil and the thermistor foils (on top). The upper end of the foil is shown in Fig. 6. The heating foil is actually a thin electric resistor made of brass and glued between resistive foils made of polyester plastic (thickness 0.075 mm). The temperature sensor foil has a set of copper conductors joining the thermistors with the circuit boards. The resistance of the heating foils is about 100 Ω. When heated, the resistance increases slightly (0.145 Ω/K). The effect of temperature dependence is smaller than 1 % when the foil temperature increases less than 7 K. The effect on interpreted results is small and it has been ignored in the modellings. 3.5 Electronics Electronics responsible for measuring the temperatures and the single-point resistance has been installed within the probe. In addition, the 2-component acceleration sensor and 3-component flux gate magnetometer have been installed inside the probe. The recording and controlling PC and the control unit are at the surface containing the modem, voltage source for measurements as well as the current source for heating. The heating power is continuously monitored at the upper end of the cable. The resistance of the cable is 57 Ω. 3.6 Thermistor calibration The thermistors were calibrated after installation on the heating foils. The both foils were placed inside a cylinder made of plastic and aluminium. The cylinder was then placed into a hole drilled in a salt cube (18 cm x 18 cm x 15 cm) made of compressed NaCl (mass of about 10 kg). Reference temperatures were taken with a Hg thermometer

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placed on the cylinder axis. The Hg container of the thermometer was inside a copper tube and the surrounding empty medium was filled with salt. The salt cube was insulated with styrox and placed in a test box, the temperatures of which were kept stable with a thermostate. The temperature readings were followed with a small video camera imaging the scale of the Hg thermometer (Fig. 7). Figure 7. Arrangement of the temperature calibration of thermistor foils inside a piece of compressed NaCl. The foils are placed at the center of the salt cube inside an aluminium pipe. At the center of the aluminium pipe there is a Hg thermometer which is read with the video camera. The empty space was filled with salt and the cube was insulated with styrox. The complete installation was then placed in a thermally controlled box. The inside temperature of the test box could be observed in temperature readings taken outside the salt cube as periodic temperature variations, but no variation was observed inside the salt cube. The resolution of the Hg thermometer scale is 0.1 ºC. Temperatures were estimated to a reading accuracy of 0.02 ºC from the video image. The corresponding thermistors resistances were calculated as averages of 30 readings taken at 10 second intervals. Six to eight reference temperatures between 2-26 ºC were applied. The calibration curves of foils are shown in Fig. 8. Thermistor temperatures are calculated from a polynomial of third degree dependent on the natural logarithms of the measured voltage differences: Ti(U) = ai + bi ln(U) + ci ln(U)2 + di ln (U)3. Generally, the fitted calibration curves were equally satisfactory. The median of the absolute fitting error was about 0.01 K in calibration of both foils.

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Figure 8. Calibration curves of the thermistors and the applied calibration temperatures. For instance, the median values of deviations of all thermistor results recorded in 20 sequential readings taken at 10 s intervals is ± 0.001 K, whereas the maximum deviations are smaller than ± 0.003 K. The error in fitting of the polynomial function is mainly due to reading errors of the Hg-thermometer (a conservative estimate ± 0.01 K. Nevertheless, the calibration satisfies requirements of measuring temperatures with an maximum error smaller than 0.03 K yielding conductivity and diffusivity estimates better than ± 2 % and ± 5 %, respectively. 3.7 TERO Graphical interface As a part of the system development a graphical interface system has been designed and constructed for the TERO device (Fig. 9). TERO Graphical Interface (TGI) is a versatile analysis and interpretation toolbox, which is developed in MatLab environment. The development work is done under Windows XP Operating System but XP is not a strict demand. To use the whole power of the TGI there should be at least 1 GB ROM memory (2 GB is recommended). In the case that MatLab 6.5.1 or earlier versions are used, Java exception errors will be generated by the system. The system works best in the latest version of MatLab (currently 7.0.1). In addition, the TGI system requires the latest version of FemLab (3.1).

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Figure 9. View of the TERO graphical interface.

Data analysis of temperature readings of the 28 thermistors in the probe is carried out according to the four vertical sensor sequences. Each sequence comprises seven thermistors, and in the TGI software there is a window where all the calculations can be done for each sequence. Controlling of which registrations are plotted is easily done depending on the functioning of thermistors in the sequence. By one pushbutton it is possible to remove the average temperature level and the initial time period from the registrations to produce simulation-ready data files. There are three possible simulation levels in the TGI system. The 1D cylinder symmetric simulation is the quickest and most simple level, whereas 2D simulation is little bit heavier to use. Nevertheless, the results are output in a reasonable time. The third simulation level is a 2D cylinder symmetric configuration, which is the most demanding in terms of model details and computing time. It is possible to decide freely which thermistor results are plotted and applied in modelling. A printing utility makes it possible to print all the results using the MatLab print commands. Zooming into finer details is possible with respect to each axis separately. Data interpretations can be run when the analysis part of software is active because all parts of the software are planned to operate independently. Thus, after having started with the “Data analysis” it is possible to freely continue to the interpretation and

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modelling section of TGI. Interpretations can be done in three levels, 1D cylinder symmetric, 2D and 2D cylinder symmetric case. In the interpretation level all the functions in any open window can be controlled. 1D level is the quickest, however, also the most inaccurate means of modelling due to infinite length of model cylinder. It is often useful to start with the 1D simulations and then continue to 2D models. Further, it is important to keep the number of parameters to be optimised as small as possible in the first calculations and add parameters one by one in the next interpretations. 2D and 2Dc (cylinder case) are more time consuming methods but also more representative of the real probe response. Anisotropy can be included in the models in both 2D models. This, however, is restricted to cylindrical anisotropy where the main components of anisotropy is oriented in the axial and radial directions from the probe. Further details and instructions of the TGI system can be found in the User's guide (Korpisalo, 2005).

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4 TEST MEASUREMENTS WITH THE “TERO” IN SITU LOGGING DEVICE 4.1 Measurements in Olkiluoto, borehole OL-KR2 Borehole OL-KR2 was selected as a target for full scale test measurements with the TERO device. The hole has a nominal diameter of 56 mm, and it intersects mostly migmatitic mica gneiss, grey granite and granitic pegmatite. The main focus of the in situ measurements was to determine the properties of mica gneiss, which is the prevailing rock type at Olkiluoto. A measurement program was designed with an aim to locate both homogeneous and less homogeneous sections from the mica gneiss, and also of the granitoid rocks. The same sections were later sampled for laboratory measurements for comparison (chapter 4.2). Generally we aimed at gaining experience of the method, instruments and interpretation under real conditions. Twenty two 1.5 m long sections of the borehole were chosen for measurements. In this, we used the video images of the borehole as well as previous single point logs. For repeatability reasons we wanted to have good control of the logging depths and hydraulically conductive and active fractures possibly disturbing the thermally conductive regime. In addition, the hydraulic transmissivities were also checked for high hydraulic conductivities. Most of the sections have very low transmissivities. The loggings were carried out in two stages, between May 19 and June 5, 2004 and between September 17 and 28, 2004. Excluding a problem with pressure sealing of the cable head, and replacement of certain electronic circuit boards, the TERO device was observed to be functional and it worked well. The logging program is summarized in Table 1. Table 1. Measurements of thermal properties in the borehole OL-KR2 with the TERO in situ logging device. Depth (m) and rock type

Date of measurement (dd-mm-yy)1

Galvanic single-point anomalies

Previous lab measurements and notes on rock properties

Transimissivity log (T,m2/s)

Notes2

326.0 – 327.5 MGN

29.5.04

No anomaly

Even-grained

tight

Close to fracture at 325.9 m, log T=-9.5 Section 325.9 – 329.8 m: no fractures

335.0 – 336.5 MGN

28.5.04 No anomaly Even-grained, some felsic dyke material

-10.3 4 fractures with fillings

340.0 – 341.5 27.5.04 Even-grained Tight 2 tight fractures

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Table 1. (cont.) Measurements of thermal properties in the borehole OL-KR2 with the TERO in situ logging device. Depth (m) and rock type





Notes2

345.5 – 347.0 MGN

26.5.04

No anomaly

Even-grained + veins

-9.7

350.0 – 351.5 MGN

25.5.04 22.5.04

No anomaly Even-grained + veins

tight No fractures 344 – 353.7 m

354.5 – 356.0 MGN -“-

30.9.04 21.5.04

No anomaly Even-grained -10.1 Close to a fracture with filling at 354.4 m, 354.4 - 361.9 m: no fractures

365.0 – 366.5 MGN

20.5.04

No anomaly Even-grained, some veins

tight 3 fractures with fillings

377.0 – 378.5 MGN 379.9 – 381.4

29.9.04 19.5.04

No anomaly Even-grained tight No fractures

386.0 - 387.5 MGN 388.9 –390.4

28.9.04 19.5.04

No anomaly Even-grained tight No fractures

446.5 - 448.0 GRA 446.5 - 448.0 GRA

27.9.04 30.5.04

No anomaly tight 5 fractures with fillings

- Previous lab sample 456.56, in MGN layer

467.0 - 468.5 MGN

27.9.04 (26.9.04)

No anomaly tight 3 fractures with fillings

470.85 - 472.35

24.9.04 Anomaly Open fracture

tight Ri III

476.0 – 477.5 MGN

25.9.04 No anomaly tight 3 fractures with fillings, section 472.8 – 476.9 m: no fractures

481.0 - 482.5 GRA

23.9.04

No anomaly

tight

4 fractures with fillings

- - Previous lab sample 485.64 m MGN

21

Table 1. (cont.) Measurements of thermal properties in the borehole OL-KR2 with the TERO in situ logging device. Depth (m) and rock type





Notes2

486.0 - 487.5 MGN -“-

22.9.04 21.9.04

No anomaly Previous lab sample 486.41 m MGN included in section

tight 3 fractures with fillings, section 486.8 – 489.9 m: no fractures

491.0 – 492.5 MGN

20.9.04 No anomaly -9.6 8 fractures with fillings

- - Previous lab sample 496.58 m MGN, contact only 0.6 m downwards

502.0-503.5 MGN

19.9.04 No anomaly Previous lab sample 502.81 m included in section

tight No fractures, section 493-503.6 m: no fractures, beneath a fracture zone

506.25 –507.75 MGN

18.9.04 Anomaly Open fracture -8.2 .. -7.2

Ri III, hydraulically conductive fracture

510.65 – 512.35 m -“-

17.9.04 5.6.04

MGN/PEG contact

tight 3 fractures with filling

526.0 – 527.5 MGN

16.9.04 4.6.04

No anomaly MGN layer exactly at the measurement section. Previous lab sample 526.82 m MGN

-10.0 1 fracture with filling

- - Previous lab sample 550.88 MGN

1 Procedure of measurements: The probe was descended to measurement depth and left for thermal equilibration for 1 hour. Heating time was 6 h followed by a cooling time of 12 hours. Heating time of 8 hours was applied on 25.9. and 1 h in the second measurement of 30.5. Short heating pulses (3-5 min duration) for detection of strong water flow were applied before the main heating period in measurements 19.5. – 29.5.04. 2 Data on fractures and transmissivities according to H. Ahokas (JP-Fintact Oy)

22

4.2 Laboratory measurements of drill cores Because all previous determinations of thermal properties at the Olkiluoto investigation site have been based on laboratory measurements of drill core samples (Kukkonen and Lindberg 1995, 1998; Kukkonen 2000), it was necessary to measure the drill cores corresponding to the in situ measurement intervals of the OL-KR2 borehole. Three pieces of drill core samples were selected systematically from the in situ measurement sections in such a way that the central sample was taken from the centre of the in situ interval and two others were taken at 50 cm above and beneath the central point of the 1.5 m long interval (Fig. 10). The sampling however, was locally complicated by the lack of core at the investigated sections because core had been already consumed for other purposes. Disks of 7 mm thickness were prepared from the 42 mm diameter core samples (Fig. 10). These samples represent the thermal conductivity in the direction of the borehole axis. However, the mica gneiss has a distinctly anisotropic texture, and previous laboratory measurements have suggested that thermal conductivity is anisotropic with an anisotropy factor of about 1.3 (Kukkonen and Lindberg 1995). The direction of maximum conductivity agrees with the plane of schistocity and gneissic foliation. To be able to estimate the effect of anisotropy a 25 mm diameter core was drilled in laboratory from the centre samples of the in situ measurement intervals. These cores were oriented in the direction of the schistocity to represent the (presumably) maximum conductivity. Three 7 mm thick disks were cut from these 25 mm diameter cores. Thermal conductivity was measured with the steady-state divided bar method using an apparatus built at the GTK. The method has been described in Kukkonen and Lindberg (1995). Thermal diffusivity was estimated from the samples using measured conductivity, density and previous average value of specific heat capacity of the Olkiluoto mica gneiss (Kukkonen, 2000). Bulk density of the samples was determined by the Archimedean principle by weighing them in water and air.

Figure 10. Scheme of sampling the OL-KR2 drill core for laboratory measurements of thermal conductivity. Three samples (diameter 42 mm) representing the conductivity in direction of core axis (A) were cut off from the core, whereas samples (diameter 25 mm) representing conductivity in direction perpendicular to the core axis (P) were drilled off the original core. From each of these, 1-3 disks were further cut off.

23

Table 2. Summary of in situ and laboratory measurements of thermal conductivity and diffusivity estimates made in borehole OL-KR2. Depth Rock λH sH λa λp sa sp F λ type _________________________________________________________________________________________ 326.0-327.5 MGN 3.31 1.48 2.72 ± 0.06 (3) 3.28 ± 0.24(3) 1.37 ± 0.04 (3) 1.65 ± 0.12 (3) 1.20 335.0-336.5 MGN 3.61 1.62 2.75 ± 0.11(4) 3.25 ± 0.05 (3) 1.63 ± 0.03 1.39 ± 0.05 (4) 1.24 340.0 – 341.5 MGN 3.29 1.48 2.97 ± 0.50(3) 3.04 ± 0.07 (3) 1.51 ± 0.27 (3) 1.53 ± 0.05 (3) 1.24 345.5 – 347.0 MGN 3.22 1.45 2.45 ± 0.27 (3) 2.98 ± 0.34 (3) 1.24 ± 0.16 (3) 1.52 ± 0.18 (3) 1.31 350.0 – 351.5 MGN 3.25 1.49 2.62± 0.15 (3) 2.99 ± 0.11 (3) 1.32 ± 0.08 (3) 1.49 ± 0.05 (3) 1.07 354.5 – 356.0 MGN 3.34 1.35 2.70 ± 0.13 (3) 3.24 ± 0.02 (3) 1.36 ± 0.07 (3) 1.64 ± 0.01 (3) 1.24 365.0 – 366.5 MGN 3.41 1.53 3.12 ± 0.01 (2) 3.40 ± 0.08 (6) 1.60 ± 0.02 (2) 1.72 ± 0.04 (6) 1.09 377.0 – 378.5 MGN 3.27 1.45 2.49 ± 0.15 (3) 2.77 ± 0.04 (3) 1.25 ± 0.08 (3) 1.39 ± 0.02 (3) 1.17 379.9 – 381.4 MGN 3.32 1.51 2.80 ± 0.15 (3) 3.13 ± 0.03 (3) 1.41 ± 0.08 (3) 1.57 ± 0.01 (3) 1.08 386.0 – 387.5 MGN 3.31 1.47 2.57 ± 0.12 (3) 3.25 ± 0.01 (3) 1.33 ± 0.04 (2) 1.64 ± 0.005 (3) 1.21 388.9 – 390.4 MGN - - 2.51 ± 0.07 (3) 3.10 ± 0.01 (2) 1.26 (1) 1.58 ± 0.01 (2) 1.20 446.5 – 448.0 GRA 4.04 1.76 3.42 ± 0.22 (3) 4.33 ± 0.15 (3) 1.77 ± 0.11(3) 2.22 ± 0.07 (3) 1.20 467.0 – 468.5 MGN 4.35 1.88 3.08 ± 0.56 (3) 4.29 ± 0.10 (3) 1.39 ± 0.23 (2) 2.08 ± 0.07 (3) 1.39 470.85 – 472.35 MGN 4.22 2.04 3.00 ± 0.05 (4) - 1.49 ± 0.02 (3) - - 476.0 – 477.5 MGN 4.23 1.87 3.43 ± 0.35 (4) 4.18 ± 0.04 (3) 1.70 ± 0.19 (4) 2.09 ± 0.02 (3) 1.17 481.0 – 482.5 GRA 3.87 1.72 3.34 ± 0.21 (3) 2.77 (1) 1.72 ± 0.10 (3) 1.46 (1) 0.83 486.0 – 487.5 MGN 4.29 2.00 2.62 ± 0.52 (5) 3.50 ± 0.08 (2) 1.30 ± 0.28 ( 5) 1.76 ± 0.07 (2) 2.00 491.0 – 492.5 MGN 3.94 1.75 2.99 (2) - 1.49 ± 0.19 (3) - - 502.0 – 503.5 MGN 3.88 1.71 2.62 (1) 3.47 ± 0.21 (3) 1.28 ± 0.24 (2) 1.62 (1) 1.52 506.25 – 507.75 MGN 4.54 2.02 3.22 ± 0.04 (3) 3.49 ± 0.71 (2) 1.59 ± 0.04 (3) 1.58 (1) 1.06 510.65 – 512.35 MGE 3.97 1.77 3.25 ± 0.28 (4) 4.19 ± 0.18 (2) 1.65 ± 0.19 (4) 2.17 (1) 1.36 + PEG 526.0 – 527.5 MGN 4.02 1.72 2.44 ± 0.41 (5) 3.52 ± 0.13 (3) 1.21 ± 0.25 (59 1.76 ± 0.06 (3) 1.72 _________________________________________________________________________________________

24

Table 2 (cont.) Summary of in situ and laboratory measurements of thermal conductivity and diffusivity estimates made in borehole OL-KR2 _________________________________________________________________________________________ MGN = mica gneiss, GRA = granite, PEG = pegmatite λH = thermal conductivity determined in borehole (W m-1 K-1) sH = thermal diffusivity determined in borehole (10-6 m2 s-1) λa = thermal conductivity and its standard deviation along the borehole axis determined in laboratory from core samples (W m-1 K-1), number of samples in brackets λp = thermal conductivity perpendicular to borehole axis determined in laboratory from core samples (cont.) sa = thermal diffusivity and its standard deviation along the borehole axis determined in laboratory from core samples (10-6 m2 s-1); number of samples in brackets sp = thermal diffusivity and its standard deviation perpendicular to borehole axis determined in laboratory from core samples (10-6 m2 s-1); number of samples in brackets F λ = anisotropy factor of thermal conductivity (= sp / sa) _________________________________________________________________________________________ Results of laboratory measurements have been compiled into Table 2. Thermal conductivities of the mica gneiss samples are in a good agreement with the previous results. In the sampled sections the mica gneiss foliation is in most cases approximately perpendicular to the drill core axis. Therefore, the standard samples represent mostly the minimum conductivity with a mean of 2.85 ± 0.23 W m-1 K-1. The samples drilled in the direction perpendicular to the core axis, which in mica gneiss samples corresponds to the plane of schistocity, yielded systematically higher conductivities, with a mean of 3.42 ± 0.16 W m-1 K-1. The average factor of anisotropy is 1.25 ± 0.25 but individual values range from 0.83 to 2.00. The existence of a systematic anisotropy, particularly in the mica gneiss, is an important factor influencing the interpretation of the in situ measurements, which are particularly sensitive for the thermal conductivity in the radial direction from the borehole axis. 4.3 Interpretation of logging results and comparison with laboratory

measurements In the following we present results of thermal property determinations from the in situ measurements in the Olkiluoto OL-KR2 borehole. Parameter estimation is based on fitting measured temperature data with the forward modelling of heat transfer from a heated cylinder with finite length and conductivity (2-dimensional cylinder symmetric finite element model). We have applied here temperatures in the centre of the probe measured during both heating and cooling periods. The parameter estimation problem is solved as a nonlinear least squares problem. To minimize the misfit between the time dependent measured and modelled temperatures the Levenberg-Marquardt method is applied (e.g. Dennis and Schnabel, 1996; Madsen et al., 2004). This damped Gauss-Newton method locally approximates the given nonlinear problem with linear least squares problem. The optimisation process iteratively adjusts the unknown model parameters.

25

In every iteration the first partial derivatives of modelled temperatures with respect to each estimated model parameter were computed. This sensitivity matrix was computed using finite differences. Thus, in every iteration the forward problem was solved N+1 times, where N is the number of unknown parameters. In the present optimisation technique, the strategy is to update the damping (Marquardt) parameter following the method of Nielsen (1999), otherwise it is based on the algorithm presented by Dennis and Schnabel (1996). The results are presented in Figs 11-52. In addition to the temperature-time curves for central temperature sensors in each measurement we also present temperature “maps”, i.e., temperature distributions of the 28 sensors at experiment time c. 21 000 s (100 s before end of heating) and c. 22 000 s (500 s after beginning of cooling). These maps reveal temperature differences, which may be attributed to axial loss of heat, anisotropy, and water flow effects but probably also to technical problems in certain thermistors. At two depth sections there were open fractures, which are known from previous investigations to be hydraulically conductive and active (470.85 – 472.35 and 506.25 – 507.75 m) (Figs 34 and 46). The modelled conductivities and diffusivities do not differ essentially from other measured sections having no hydraulically active fractures, but the maps of temperature differences show a “hot spot”, which coincide with the fractures. We interpret this effect as due to heat stored in the fracture and surrounding medium by the flowing water. The effect is stronger in the interval of 470.85 – 472.35 m. This gives a hint that the TERO measurements could provide indirect information of active hydraulic systems. Comparison between laboratory measurements and in situ measurements are given in Figures 53-55. In the comparison we apply the lab conductivities measured in the direction of the schistocity. The thermal conductivities estimated from in situ measurements agree relatively well with laboratory data, although the in situ data seem to be about 0.3 – 0.5 W m-1 K-1 higher (Fig. 53). Comparison between diffusivities estimated from borehole measurements and laboratory data indicates that in situ data are about 0.1 –0.2 10-6 m2 s-1 lower. Here too, there is scatter around the 1:1 relationship (Fig. 54). The range of variation is smaller for in situ estimates than for laboratory estimates of diffusivity. However, the comparison between heat capacities estimated from borehole data and laboratory data indicate much higher values from borehole than lab data.

26

Figure 11. Measured and modelled temperature response of the central thermistor (no. 4 of line 3) during in situ measurement at depth of 326.0 – 327.5 m in OL-KR2 borehole. Estimated values of conductivity (λ), diffusivity (s), hole radius (hrad) and the mean squared error of fit (misfit) are indicated.

Figure 12. Maps of temperature changes measured in the probe 100 s before end of heating period (left), and 500 s after beginning of cooling (right). The horizontal numbering (1…4) identifies the vertical lines of thermistors, and vertical numbers, the axial positions, respectively. Depth of measurement 326.0 – 327.5 m.

27



28



29



30



31



32



33



34



35



36



37



38



39



40



41

Figure 41. Measured and modelled temperature response of the central thermistor (no. 4 of line 3) during in situ measurement at depth of 486.0 – 487.5 m in OL-KR2 borehole. Estimated values of conductivity (λ), diffusivity (s) , hole radius (hrad) and the mean squared error of fit (misfit) are indicated.


42



43



44



45



46



47

Figure 53. Comparison between thermal conductivities determined in situ and measured in laboratory. The red line indicates 1:1 relationship. Figure 54. Comparison between thermal diffusivity estimated from in situ measurements and determined with laboratory measurement of drill cores. The red line indicates 1:1 relationship.

48

Figure 55. Comparison between heat capacities estimated from in situ measurements and determined from laboratory measurements of drill cores. The red line indicates 1:1 relationship. 4.4 Factors influencing thermal parameter estimation The present estimation of thermal properties from the TERO measurements is based on a least squares optimization method, which utilizes complete numerical models of conductive heat transfer from the hollow cylinder to the surrounding medium. The estimation problem is challenging because the wanted parameters, i.e., conductivity, heat capacity (or diffusivity) and the contact layer thickness, are mutually coupled. Particularly heat capacity and diffusivity estimation are sensitive for contact layer thickness variations. Conductivity is also affected by the contact layer but less than heat capacity. Conductivity is the most stable parameter in the present optimization process, and the numerical values apparently do not seem to be modified by even considerable changes in the contact layer thickness, whereas heat capacity (or diffusity) is modified significantly. Previous theoretical modelling and sensitivity analysis of parameter estimations (Kukkonen et al., 2001) showed that conductivity has a well-defined narrow minimum of the best estimate value, whereas heat capacity and contact layer thickness have much softer and wider minima. In Figures 11-52 the basic results of parameter optimization were shown for each measured section. In these results the complete heating-cooling cycle was used in

49

parameter optimization. The parameters contribute differently in different phases of the experiment, and it could perhaps be possible to use different parts of the heating-cooling cycle for more independent estimation of rock properties. A well-known result based on analytical solutions of heat transfer in 1-dimensional cylindrical geometry is given by Carslaw and Jaeger (1959, p. 345). In such a problem, conductivity can be estimated from the slope of temperature increase (in a T - ln t plot) at long heating times, and diffusivity from the intercept of the long-time T-ln t asymptote with the time axis, respectively. In the presence of a contact layer the time-temperature curve is shifted to higher temperatures, which distorts the diffusivity estimation. However, conductivity is still reliably determined because the shift does not affect the long-time slope. When heating is turned on in the TERO device, the temperature response at very short heating times (smaller than 100 s) is controlled by the properties of the probe and the contact layer. At intermediate heating times (up to about 1,000 – 10,000 s), the probe temperature is controlled by rock diffusivity and contact layer properties. At long heating times (greater than about 10,000 –100,000 s), when a pseudo-stationary state is slowly approached, the slope of temperature response is increasingly influenced by rock conductivity. In the present TERO measurements the applied heating times were considerably shorter than required to reach the pseudo-stationary conditions. Further, the finite length of the TERO probe creates axial conduction of heat along the borehole, and the slope of the temperature curve does not become linear even at very long heating times. Thus, modelling of the TERO measurements requires a complete numerical solution of heat transfer between the probe and rock. In the following, we present theoretical modelling results for different thermal parameter values. The applied heating power of the probe is 22.3 W, which is equal to the value used in OL-KR2 measurements. To provide a comprehensive view of various factors involved in the problem, we present the modelled temperature responses scaled according to the maximum temperature (i.e, as relative temperatures). In addition we present the temperatures and relative temperatures as inverse values (Figures 56-58). Such a technique allows observing the mutual dependencies between different thermal parameters. In the cooling phase, when there is no heat source, temperatures are controlled by the diffusivity of rock, together with the heat capacities of the probe, rock and the contact layer. If there is no contact layer the slope of inverse relative temperatures during cooling is constant with constant diffusivity regardless of conductivity (Fig. 59). The small difference between inverse relative temperature curves can be attributed to effects of the probe itself, i.e., differences in heat capacity and conductivity between the probe and rock.

50

Figure 56. Theoretical modelling results of TERO response with constant diffusivity of rock. Conductivity was pre-set and heat capacity was calculated from conductivity and diffusivity. The results are given as absolute temperature values (above left), relative temperatures scaled according to the maximum temperature (below left), inverse temperatures (above right) and inverse relative temperatures (below right). Hole diameter is 56 mm.

51

Figure 57. Theoretical modelling results of TERO response with constant heat capacity of rock. Conductivity was pre-set and diffusivity was calculated from conductivity and heat capacity. The results are given as absolute temperature values (above left), relative temperatures scaled according to the maximum temperature (below left), inverse temperatures (above right) and inverse relative temperatures (below right). Hole diameter is 56 mm.

52

Figure 58. Theoretical modelling results of TERO response with constant conductivity of rock. Diffusivity was pre-set and heat capacity was calculated from conductivity and diffusivity. The results are given as absolute temperature values (above left), relative temperatures scaled according to the maximum temperature (below left), inverse temperatures (above right) and inverse relative temperatures (below right). Hole diameter is 56 mm.

53

Figure 59. Theoretical modelling results of TERO response with no contact layer in the model (hole diameter is 50 mm and equal to probe diameter). Diffusivity is constant, conductivity is pre-set and heat capacity is calculated. When the contact layer is included in the model, the responses differ distinctly (Fig. 56). The inverse temperature curves are heavily dependent on the maximum temperatures at the end of the heating period, and these are affected by the contact layer. In the case that the contact layer effect could be removed from the temperature response, we could directly estimate diffusivity with a set of standard inverse relative temperature curves. It is not possible to remove the effect in a general case, but reasonable approximations may be reached in standard boreholes in good condition and hole diameters close to the nominal value. A comparison of inverse relative temperatures calculated with or without the contact layer is shown in Fig. 60.

54

Figure 60. Comparison of theoretical modelling of TERO measurements with constant conductivity (3 W m-1 K-1) and diffusivity (1.32·10-6 s2 m-1) but the hole radius varying from 28 to 29 mm (diameter varying from 56 to 58 mm, respectively). The result with no contact layer (“no hole”), which would correspond to a hole diameter of 50 mm, is included for comparison. As the estimated parameters influence the TERO response differently in different times, the sensitivity of the least squares optimization was also tested with theoretical modelling. Starting from a forward model calculated with pre-set rock properties and a contact layer thickness of 3 mm (56 mm hole diameter), the temperature response was re-interpreted by fixing the hole diameter at a value of 57 mm. Therefore, we can compare the original, ‘correct’ rock parameters (3.5 W m-1 K-1, 1.552 · 10-6 s2 m-1) with results inverted using an incorrect hole diameter value (57 mm). Inverse results for the complete measurement time up to 60,000 s are within about 0.8 % of the correct conductivity, but differ by almost 20 % for the diffusivity (Fig. 61). Using only the heating period in inversion gives a conductivity estimate within about 2.5 % of the correct value, whereas diffusivity deviates by as much as about 26 % of the correct value (Fig. 62). However, using only the cooling period gives a conductivity estimate of about 0.4 % too large, and diffusivity deviates only about 3 % of the correct value (Fig. 63). This result encourages applying particularly the cooling part of the response for diffusivity estimation. However, it should be taken into account that these theoretical results include no noise, nor is there any trend in the temperature data. In practice, noise is not usually a very critical issue, but a trend may easily disturb the response and bias the result.

55

Figure 61. Inversion results of a theoretical temperature response assuming the hole diameter to be 57 mm instead of the correct value 56 mm. In parameter optimization, the complete heating-cooling cycle of 60,000 s was used.

Figure 62. Inversion results of a theoretical temperature response assuming the hole diameter to be 57 mm instead of the correct value 56 mm. In parameter optimization, only the heating period up to 21,500 s was used.

56

Figure 63. Inversion results of a theoretical temperature response assuming the hole diameter to be 57 mm instead of the correct value 56 mm. In parameter optimization, only the cooling period of 22,020 – 60,000 s was used. The effect of the length of time used in parameter estimation was investigated with a set of measured data from borehole OL-KR2. The results have been compiled into Figs. 64 – 67. For instance, conductivity estimates differ less than 3 % when the time used in inversion varies from 8,000 to 60,000 s. Diffusivity on the other hand, shows in most cases a decreasing trend with values varying by up to 25 % in well-behaving cases. In some cases the diffusivity values are much less stable which can probably be attributed to trends or other disturbances in the data. The results show that the conductivity estimation is relatively stable (overall variation less than about 2 %). During heating, the interpreted conductivity values increase with increasing time to about 15,000 – 20,000 s (Fig. 64). Diffusivity, on the other hand, varies only by about 10 % at best, but shows also much larger variations. One measurement, at 486.0 – 487.5 m, is particularly problematic. However, there seems to be a trend in the temperature data at this depth interval, and the result is not representative of conditions assumed in the model. Generally, the results based only on cooling times are in line with other results and do not essentially differ from the others. It should be noted, however, that the estimated diffusivity values show much less variation when estimated from the cooling phase. Comparison of results obtained with different values of the misfit criterion are shown in Table 3. In the standard simulations (Figures 11-52) the misfit criterion was 1.25·10–5 K2 (corresponding to a noise level of 3.25 mK). In the parameter estimations in Figures 64-67 the criterion was set at 2.0·10 –6 K2 (corresponding to a temperature noise level of

57

1.4 mK). It can be seen in Table 3 that the misfit criterion influences strongly the diffusivity estimates but only very little the conductivity and hole radius estimates. Figure 64. Optimized values of thermal conductivity as a function of measurement time applied in the inversion using the data measured in OL-KR2 borehole. Each point indicates the end time of the period used in the inversion. Broken lines indicate results based on cooling times only. Length of heating was 21,600 s. Misfit criterion was 2.0 ·10 –6 K2 . Figure 65. Optimized values of heat capacity as a function of measurement time applied in the inversion using the data measured in OL-KR2 borehole. Each point indicates the end time of the period used in the inversion. Broken lines indicate results based on cooling times only. Length of heating was 21,600 s. Misfit criterion was 2.0 ·10 –6 K2.

58

Figure 66. Optimized values of diffusivity as a function of measurement time applied in the inversion using the data measured in OL-KR2 borehole. Each point indicates the end time of the period indicated in the inversion. Broken lines indicate results based on cooling times only. Length of heating was 21,600 s. Misfit criterion was 2.0 ·10 –6 K2 . Figure 67. Optimized values of borehole radius as a function of measurement time applied in the inversion using the data measured in OL-KR2 borehole. Each point indicates the end time of the period used in the inversion. Broken lines indicate results based on cooling times only. Length of heating was 21,600 s. Misfit criterion was 2.0 ·10 –6 K2 .

59

Table 3. Effect of misfit criterion on the estimated parameter values from OL-KR2 data. The complete heating-cooling cycle was used in inversions. Depth Conductivity Diffusivity Hole rad. Conductivity Diffusivity Hole rad. (m) (W m-1 K-1) (10-6 s2 m-1) (mm) (W m-1 K-1) (10-6 s2 m-1) (mm) Misfit criterion 1.25·10 –5 K2 Misfit criterion 2.0·10 –6 K2 _______________________________________________________________________ 365.0-365.5 3.41 1.53 28.7 3.43 2.00 28.1 467.0-468.5 4.35 1.88 28.8 4.34 1.69 29.0 481.0-482.5 3.87 1.72 29.0 3.88 2.00 28.4 486.0-487.5 4.29 2.00 29.1 4.28 3.24 28.0 502.0-503.5 3.88 1.71 28.6 3.86 1.53 28.8 526.0-527.5 4.02 1.72 28.6 4.00 1.50 28.8 _______________________________________________________________________ Effect of rock anisotropy on the TERO measurements is shown in Figure 68. The forward response of the probe was calculated with finite cylinder model, and the factor of conductivity anisotropy (calculated as λp / λa, where λp is conductivity in the direction perpendicular to borehole axis and λa is conductivity in the direction along the borehole) was varied from 0.8 to 1.6. Conductivity in direction perpendicular to borehole (λp) was kept at a constant value of 3.31 W m-1 K-1, whereas conductivity in direction of borehole (λa) was varied from 2.07 to 4.14 W m-1 K -1. The results indicate that the anisotropy effects are quite small. The differences in probe temperatures between isotropic and anisotropic cases are mostly smaller than 0.01 – 0.02 K. This applies to temperatures at both the centre and end of the probe. In these simulations, the length of experiment, heating power and probe properties corresponded to those of applied in the present TERO measurements. Such small temperature differences between isotropic and anisotropic cases would be very difficult to recognize and interpret reliably. The effect can be expected to slightly increase with increasing heating time, but even with a heating time of 100,000 s it was less than 0.02 K in previous simulations (Kukkonen et al., 2001).

60

Figure 68. Effect of rock anisotropy on the TERO measurements. Simulated probe temperatures are shown for both isotropic and anisotropic rock conductivity. The curves indicate temperatures at distances of 0, 48 and 72 cm from the probe centre. Conductivity perpendicular to the borehole axis (λp) was kept constant (3.31 W m-1 K-1), whereas conductivity along the borehole axis (λa) was varied from 2.07 to 4.14 W – 1K -1

and corresponding anisotropy factors are 0.8 – 1.6.

0 1 2 3 4 5 6

x 104

0

0.5

1

1.5

2

2.5

Time, s

Temperatute change, K

48 cm

72 cm

0. cmλp= 3.31, λa= 4.1375

λp= 3.31, λa= 3.31

λp= 3.31, λa= 2.0688

61

5 DISCUSSION AND CONCLUSIONS The present results provide the first experience obtained with the TERO logging device for determining rock thermal properties in situ in boreholes. The system was found to be technically functional. However, certain technical problems were encountered. Heat generation from currents in the electronics at the upper end of the probe responsible of the temperature measurements, was noted to bias the temperature readings of the uppermost thermistors. The effect could be reduced by keeping the electronics off during the thermal stabilization prior to heating. Pressure sealing of the cable head was found to be leaky after the first measurements, and was repaired with a new type of epoxy. The comparison of results obtained in borehole OL-KR2 and the corresponding drill core samples measured in laboratory, indicate that the in situ estimates are about 0.3 – 0.5 W m-1 K-1 higher than laboratory measurements (Fig. 53). Diffusivity estimates are 0.1 – 0.2·10-6 m2 s-1 lower than the laboratory data (Fig. 54). The differences are not completely systematic, which is probably due to heterogeneity of the rock. For diffusivity, the difference could be attributed to estimation problems, but for conductivity the deviation may indicate other factors. For instance, a trend in the data could result in a systematic deviation in conductivity estimates. The optimization of thermal parameters was done with the Levenberg-Marquardt least-squares method. Due to the physical characteristics of the problem, the thermal parameters, particularly diffusivity and contact layer thickness, are strongly coupled which makes it difficult to estimate them with high accuracy. As shown previously (e.g. Kukkonen et al., 2001), estimation of conductivity with an accuracy better than of 2 % and diffusivity better than 5 % requires the temperature readings to be correct within 0.03 K. To achieve such results in parameter estimation, the contact layer thickness should be known accurately. Generally, this is not possible, and the contact layer thickness is one of the estimated parameters in the present optimization technique. The present theoretical modellings suggest, that the diffusivity estimation is most accurately done by using the cooling phase of the experiment in inversion as was shown using the noise-free theoretical data (Figures 61-63). This applies even to cases where the applied contact layer thickness was fixed and incorrect by 0.5 mm. It would seem that also conductivity could be reliably estimated from the cooling phase. The sensitivity analyses should be continued using theoretical data from forward models but noise effects included. The effect of measurement time on the parameter estimation was studied using also the real data from the OL-KR2 borehole (Figures 64-67). The results indicate that conductivity estimates are relatively stable and do not depend strongly on the length of measurement time or which phase of the experiment is used. On the other hand, diffusivity estimates often show a trend of slightly decreasing values with increasing experiment time, while the contact layer thickness (hole radius) shows an increasing trend. It can be attributed to the strong coupling between contact layer thickness and diffusivity.

62

The diffusivity and contact layer thickness estimates are also sensitive for the misfit criterion applied in optimization (Table 3). The misfit criterion is a measure of the squared differences between measured data and optimized model. Again, conductivity is the least affected parameter. These conclusions can be made by comparing the basic inversion results (Figures 11-52) with the results calculated by varying the measurement time in inversion (Figures 64-67). When the criterion was changed from 1.25 · 10-5 K2 to 2.0·10 –6 K2 (corresponding to noise levels of 3.5 and 1.4 mK, respectively), the diffusivity estimates changed by about 10-60 % and the hole radius estimates by 0.7 – 3.7 %. Conductivity changed only by 0.2–0.7 %. This effect is attributed to the strong coupling of parameters, which complicates their independent estimations, particularly diffusivity. The applied misfit criteria are in agreement with typical good quality borehole data, which often shows variations of about 2 mK around the average. The sensitivity of diffusivity estimation for the misfit criteria indicates that within the uncertainties of the data, there may still be considerable variations in the best estimates. As shown in earlier studies of systematic mapping of estimation accuracy (Kukkonen et al., 2001), there is usually a well-defined minimum of conductivity, but diffusivity and contact layer thickness have minima, which are much smoother and extended. As a result the estimation accuracy is not ideal for diffusivity, and it may not be possible with the present procedure to improve it without accurate and independent data on borehole calliper. One way to reduce the estimation problems may be found by developing nomogram-type methods for the parameter estimation. Such an approach is possible as was seen from the results and discussion based on theoretical models in section 4.4 (Figures 56-60). Using high-quality borehole calliper data the contact layer effects could be controlled to allow a more reliable and stable diffusivity estimation. Even then, the basic problems of the physical situation and coupling of parameters cannot be avoided. Nomogram techniques remain to be developed as an option in further studies of the TERO measurements. The measurements may be strongly disturbed by flow in the borehole, although the present data were mostly not influenced by flow effects. To completely prevent any flow in hole, the packers should be more efficient. It would require inflatable packers, which would make the device more complicated and heavier. With experience in several other holes it could be possible to decide how effective the present silicon rubber packers are in general, and whether they need to be replaced by inflatable packers. Flow systems in the hole disturb the assumed steady-state initial conditions in the borehole walls and create transient thermal conditions when the section is closed and flow is prevented. This may be one factor resulting in temperature trends in measured data, such as those observed in some of the measurement sections of the OL-KR2 hole. A trend in the data is particularly harmful for the cooling phase of the measurement, when the temperatures are already relatively small. However, if the trend is a linear change with time, it can be easily corrected for. Trend effects, their identification and correction for them should be further developed.

63

As a whole, the first results obtained with the TERO logging device are promising. Thermal conductivity estimates are stable and repeatable regardless of applied misfit criteria and which part of the experiment the estimate is based on. On the other hand, the estimation of thermal diffusivity (or heat capacity) suffers of strong coupling with contact layer thickness. The estimation methods require further elaboration in future studies.

64

65

REFERENCES Carslaw H.S. and Jaeger J.C. 1959. Conduction of heat in solids. Oxford University Press, Oxford, 510 p. Dennis, J. E. and Schnabel, R. B., 1996. Numerical Methods for Unconstrained Optimization and Nonlinear Equations. (Classics in Applied Mathematics 16) SIAM Society for Industrial & Applied Mathematics, Philadephia, 378 p. Jarny Y., Ozisik M.N. and Bardon J.P. 1991. A general optimisation method using adjoint equation for solving multidimensional inverse heat conduction. Int. J. Heat Mass Transfer 34, 2911-2919. Kjørholt H. 1992. Thermal properties of rocks. Teollisuuden Voima Oy, TVO/Site investigations, work report 92-56, 13 p. Korpisalo A. 2005. User’s Guide: TERO Graphical interface in Matlab/Femlab environment. Geological Survey of Finland, Espoo Office, Geophysical Research, Report Q17/2005/1, 88 p. Kukkonen, I., 2000. Thermal properties of the Olkiluoto mica gneiss: Results of laboratory measurements. Posiva Oy, Working Report 2000-40, 28 p. Kukkonen I. and Lindberg A. 1995. Thermal conductivity of rocks at the TVO investigation sites Olkiluoto, Romuvaara and Kivetty. Nuclear Waste Commission of Finnish Power Companies, Report YJT-98-08, 29 p. Kukkonen I. and Lindberg A. 1998. Thermal properties of rocks at the investigation sites: measured and calculated thermal conductivity, specific heat capacity and thermal diffusivity. Posiva Oy, Working Report 98-09e, 29 p. Kukkonen I. and Suppala I. 1999. Measurement of thermal conductivity and diffusivity in situ: Literature survey and theoretical modelling of measurements. Posiva Oy, Report 99-1, 69 p. Kukkonen I., Suppala I., Sulkanen K. and Koskinen T. 2000. Measurement of thermal conductivity and diffusivity in situ: measurements and results obtained with a test instrument. Posiva Oy, Working Report 2000-25, 28 p. Kukkonen I., Suppala I. and Koskinen T. 2001. Measurement of rock thermal properties in situ: numerical models of borehole measurements and development of calibration techniques. Posiva Oy, Working Report 2001-23, 47 p. Madsen K., Nielsen H. B. and Tingleff O. 2004. Methods for Non-Linear Least Squares Problems, IMM, DTU, 2nd Edition, April 2004. Available at http://www.imm.dtu.dk/courses/02611/nllsq.pdf

66

Nielsen H. B. 1999. Damping Parameter in Marquardt’s Method. IMM, DTU. Report IMM-REP-1999-05. Available at http://www.imm.dtu.dk/~hbn/publ/TR9905.ps.Z Stråhle, A. 1996. Borehole-TV measurements at the Olkiluoto site, Finland, 1996. Vol. 1. Report and Appendices for OL-KR1; Vol. 2. Appendices for OL-KR2 and OL-KR4. Posiva Oy, Working Report PATU-96-59E. Sundberg J., Kukkonen I. and Hälldahl L, 2003. Comparison of thermal properties measured by different methods. Swedish Nuclear Fuel and Waste Management Co, Report SKB R-03-18, 37 p. Suppala I., Kukkonen I. and Koskinen T., 2004. Kallion termisten ominaisuuksien reikäluotauslaitteisto TERO (Drill hole tool ”TERO” for measuring thermal conductivity and diffuxivity in situ). Posiva Oy, Working Report 2004-20, 43 p.

67

APPENDIX A: Photographs of laboratory samples from borehole OL-KR2 Depth (m) of each sample is indicated. Samples indicated with A, B, and C or alternatively, AA, BB, and CC refer to sub-samples prepared from the same piece of core at indicated depth. Large diameter (42 mm) samples represent thermal conductivity in direction of borehole axis. Small diameter (25 mm) samples represent thermal conductivity in direction perpendicular to the borehole axis (see also Fig. 10).

68

69

70

71

72

73

74

75

APPENDIX B: Borehole video images of measurement sections in borehole OL-KR2 The video images were adopted from digital data reported by Stråhle (1996).

76

TERO measurement interval Borehole: OL-KR2 Depth: 326.0 – 327.5 m

Depth 1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

325.2

325.4

325.6

325.8

326.0

326.2

326.4

326.6

326.8

327.0

327.2

327.4

327.6

327.8

328.0

328.2

328.4

77


Depth 1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

334.2

334.4

334.6

334.8

335.0

335.2

335.4

335.6

335.8

336.0

336.2

336.4

336.6

336.8

337.0

337.2

337.4

78


Depth 1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

339.2

339.4

339.6

339.8

340.0

340.2

340.4

340.6

340.8

341.0

341.2

341.4

341.6

341.8

342.0

342.2

342.4

79

TERO measurement interval Borehole: OL-KR2 Depth: 345.5 – 347.0 m Depth

1m:10m BIPS

0° 0° 180°90° 270°

3D 0°

344.6

344.8

345.0

345.2

345.4

345.6

345.8

346.0

346.2

346.4

346.6

346.8

347.0

347.2

347.4

347.6

347.8

348 0

80


Depth

1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

349.2

349.4

349.6

349.8

350.0

350.2

350.4

350.6

350.8

351.0

351.2

351.4

351.6

351.8

352.0

352.2

352.4

81


Depth 1m:10m

BIPS

0° 0°180°90° 270°

3D 0°

364.2

364.4

364.6

364.8

365.0

365.2

365.4

365.6

365.8

366.0

366.2

366.4

366.6

366.8

367.0

367.2

367.4

82


Depth 1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

376.2

376.4

376.6

376.8

377.0

377.2

377.4

377.6

377.8

378.0

378.2

378.4

378.6

378.8

379.0

379.2

379.4

83


Depth

1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

379.0

379.2

379.4

379.6

379.8

380.0

380.2

380.4

380.6

380.8

381.0

381.2

381.4

381.6

381.8

382.0

382.2

382 4

84


Depth 1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

385.2

385.4

385.6

385.8

386.0

386.2

386.4

386.6

386.8

387.0

387.2

387.4

387.6

387.8

388.0

388.2

388.4

85


Depth 1m:10m

BIPS

0° 0°180°90° 270°

3D 0°

388.0

388.2

388.4

388.6

388.8

389.0

389.2

389.4

389.6

389.8

390.0

390.2

390.4

390.6

390.8

391.0

391.2

391 4

86


Depth 1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

445.6

445.8

446.0

446.2

446.4

446.6

446.8

447.0

447.2

447.4

447.6

447.8

448.0

448.2

448.4

448.6

448.8

449 0

87


Depth

1m:10m BIPS

0° 0° 180°90° 270°

3D 0°

466.2

466.4

466.6

466.8

467.0

467.2

467.4

467.6

467.8

468.0

468.2

468.4

468.6

468.8

469.0

469.2

469.4

88


Depth

1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

469.8

470.0

470.2

470.4

470.6

470.8

471.0

471.2

471.4

471.6

471.8

472.0

472.2

472.4

472.6

472.8

473.0

473.2

89


Depth

1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

475.2

475.4

475.6

475.8

476.0

476.2

476.4

476.6

476.8

477.0

477.2

477.4

477.6

477.8

478.0

478.2

478.4

90


Depth 1m:10m

BIPS

0° 0° 180°90° 270°

3D

0°

480.2

480.4

480.6

480.8

481.0

481.2

481.4

481.6

481.8

482.0

482.2

482.4

482.6

482.8

483.0

483.2

483.4

91


Depth

1m:10m BIPS

0° 0° 180°90° 270°

3D 0°

485.2

485.4

485.6

485.8

486.0

486.2

486.4

486.6

486.8

487.0

487.2

487.4

487.6

487.8

488.0

488.2

488.4

92


Depth 1m:10m

BIPS

0° 0° 180°90° 270° 3D

0°

490.2

490.4

490.6

490.8

491.0

491.2

491.4

491.6

491.8

492.0

492.2

492.4

492.6

492.8

493.0

493.2

493.4

93


Depth

1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

501.2

501.4

501.6

501.8

502.0

502.2

502.4

502.6

502.8

503.0

503.2

503.4

503.6

503.8

504.0

504.2

504.4

94


Depth 1m:10m

BIPS

0° 0° 180°90° 270°

3D

0°

505.4

505.6

505.8

506.0

506.2

506.4

506.6

506.8

507.0

507.2

507.4

507.6

507.8

508.0

508.2

508.4

508.6

95


Depth 1m:10m

BIPS

0° 0° 180°90° 270°

3D 0°

509.8

510.0

510.2

510.4

510.6

510.8

511.0

511.2

511.4

511.6

511.8

512.0

512.2

512.4

512.6

512.8

513.0

513.2

96

TERO measurement interval Borehole: OL-KR2 Depth: 526.0 – 527.5 m Depth

1m:10m BIPS

0° 0° 180°90° 270°

3D 0°

525.2

525.4

525.6

525.8

526.0

526.2

526.4

526.6

526.8

527.0

527.2

527.4

527.6

527.8

528.0

528.2

528.4

1 (2)

LIST OF REPORTS POSIVA-REPORTS 2005 POSIVA 2005-01 Plan for Safety Case of Spent Fuel Repository at Olkiluoto Timo Vieno, VTT Processes Ari T. K. Ikonen, Posiva Oy February 2005 ISBN 951-652-133-9 POSIVA 2005-02 Disposal Canister for Spent Nuclear Fuel Design Report Heikki Raiko, VTT Processes July 2005 ISBN 951-652-134-7 POSIVA 2005-03 Olkiluoto Site Description 2004

Posiva Oy May 2005 ISBN 951-652-135-5

POSIVA 2005-04 Thermal Condition of Open KBS-3H Tunnel Kari Ikonen, VTT Processes May 2005 ISBN 951-652-136-3

POSIVA 2005-05 Dissolution of Unirradiated UO2 and UO2 Doped with 233U Under Reducing Conditions Kaija Ollila, VTT Processes Virginia Oversby, VMO Konsult June 2005 ISBN 951-652-137-1

POSIVA 2005-06 Thermal Analysis of Repository for Spent EPR-type Fuel Kari Ikonen, VTT Processes September 2005 ISBN 951-652-138-X

POSIVA 2005-07 Simulation of Hydraulic Disturbances Caused by the Decay Heat of the Repository in Olkiluoto Jari Löfman, VTT Processes October 2005 ISBN 951-652-139-8 POSIVA 2005-08 Simulation of Hydraulic Disturbances Caused by the Underground Rock Characterisation Facility in Olkiluoto Jari Löfman, VTT Processes Ferenc Mészáros, The Relief Laboratory November 2005 ISBN 951-652-140-1

2 (2)

POSIVA 2005-09 TERO Borehole Logging Device and Test Measurements of Rock Thermal Properties in Olkiluoto Ilmo Kukkonen, Ilkka Suppala, Arto Korpisalo, Teemu Koskinen Geological Survey of Finland November 2005 ISBN 951-652-141-X

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