Development of a Hydrogeological Discrete Fracture Network Model for the Olkiluoto Site Descriptive Model Working Report

Transkriptio

1 Working Report Development of a Hydrogeological Discrete Fracture Network Model for the Olkiluoto Site Descriptive Model 2008 Lee Hartley, Jaap Hoek, David Swan David Roberts, Steve Joyce Sven Follin August 2009 POSIVA OY Olkiluoto FI EURAJOKI, FINLAND Tel Fax

2 Working Report Development of a Hydrogeological Discrete Fracture Network Model for the Olkiluoto Site Descriptive Model 2008 Lee Hartley, Jaap Hoek, David Swan David Roberts, Steve Joyce Serco TAS Sven Follin SF GeoLogic AB August 2009 Base maps: National Land Survey, permission 41/MML/09 Working Reports contain information on work in progress or pending completion. The conclusions and viewpoints presented in the report are those of author(s) and do not necessarily coincide with those of Posiva.

3 ABSTRACT The work reported here (2008 OHDFN) constitutes the hydrogeological discrete fracture network (Hydro-DFN) model for the Olkiluoto site descriptive model The report collates the structural-hydraulic information gathered in 40 long (KR) and 16 short (KRB) sub-vertical boreholes drilled from the surface. This information was compared with the structural-hydraulic information gathered in seven short (PH) subhorizontal pilot boreholes drilled from the ONKALO tunnel. The report presents: An interpretation of the hydraulic information (fracture core data and Posiva flow log (PFL) data) in the context of structural subdomains defined in the Geo-DFN developed from surface, borehole and pilot-hole data. The derivation of a Hydro-DFN model for each sub-domain, which were further sub-divided by depth, suitable for describing flow and transport properties in the rock between the deterministically defined hydro zones. Predictions of frequencies, orientations and transmissivities of water conducting fractures in two pilot holes (PH) not drilled at the time of this work (PH8 and PH9). Equivalent continuum porous medium (ECPM) hydraulic properties for the rock between hydro zones in sub-domains in the immediate vicinity of the repository. Transport properties based on particle tracking through the rock between hydro zones in sub-domains in the immediate vicinity of the repository. Site-scale groundwater flow and transport pathway statistics. Site-scale ECPM model paramaterisation in support of the FEFTRA ECPM site modelling. The analyses carried out provide an input to the hydrogeological DFN descriptions of the bedrock in between hydro zones needed for the construction of 3D groundwater flow models of the Olkiluoto site as well as in the subsequent safety performance assessment. It would be useful to review if the methodology reported here could be refined with a view to integrate with hydrochemistry, which was never part of the study. Keywords: Hydrogeology, discrete fracture network, hydraulic properties, modelling

4 Hydrogeologisen rakoverkkomallin kehittäminen vuoden 2008 Olkiluodon paikkamalliin TIIVISTELMÄ Tässä raportissa kuvataan Olkiluodon 2008 paikkamallin osaksi muodostettu hydrogeologinen rakoverkkomalli (Hydro-DFN). Geometrialtaan ja hydraulisilta ominaisuuksiltaan malli perustuu tietoon pinnalta kairatuista 40:tä pitkästä (KR) ja 16:ta lyhyestä (KRB) kairanreiästä. Näistä kerättyä rakoilutietoa on verrattu seitsemästä ONKALOn pilottireiästä (PH) kerättyyn aineistoon. Raportissa esitetään tulkinta hydraulisista havainnoista geologisessa rakoverkomallissa määritellyissä kallioperän voluumeissa kairansydän ja Posivan virtausmittariaineistoon perustuen. Olkiluodon geologisessa rakoverkkomallissa tutkimusalueen kallioperä on jaettu pinta-, kairanreikä- ja pilottireikähavaintojen pohjalta kahteen alivolyymiin. Hydro-DFN malli on esitetty kallioperän eri alivolyymeille. Geologisen rakoverkkomallin alivolyymijaon lisäksi Hydro-DFN malli on jaettu syvyysvyöhykkeisiin, joita käytetään determinististen vyöhykkeiden välissä olevan taustarakoilun virtaus- ja kulkeutumisominaisuuksien kuvaamiseen. Vettäjohtavien rakojen tiheydet sekä asento- ja vedenjohtokykyjakaumat on ennustettu kahdelle ONKALOn pilottireiälle (PH8 ja PH9), joita ei tämän työn tekemisen aikaan vielä ollut kairattu. Rakoverkkomalliin perustuen esitetään arvio ekvivalenteista hydraulisista ominaisuuksista (ECPM) determinististen rakenteiden välisessä taustarakoilussa kallioperän eri alivolyymeissa loppusijoitustilan ympäristössä. Raportissa arvioidaan myös kulkeutumisominaisuuksia taustarakoilussa loppusijoitustilan välittömässä läheisyydessä, sekä virtaus- ja kulkeutumisominaisuuksia tutkimuspaikan mittakaavassa. Rakoverkkomallinnuksen avulla on myös arvioitu ECPM mallin parametrisointia tutkimuspaikan mittakaavassa huokoisen väliaineen mallinnuksen (FEFTRA) tueksi. Mallinnuksen tuloksia voidaan käyttää hyväksi muodostettaessa rakoverkkomalliin perustuva tutkimuspaikan mittakaavan pohjaveden virtausmalli. Tätä kautta työn tuloksia voidaan käyttää hyväksi myös turvallisuusanalyysissä. Avainsanat: Hydrogeologia, rakoverkkomalli, hydrauliset ominaisuudet, mallinnus

5 1 TABLE OF CONTENTS ABSTRACT TIIVISTELMÄ 1 INTRODUCTION Background Objectives and scope Objectives Scope Structure of this report Phase I Phase II Addendum work NOMINAL MODEL AREAS OF OLKILUOTO HYDRO ZONES AND FRACTURE DOMAINS Model of hydro zones Model of fracture domains PRIMARY DATA Single-hole hydraulic tests Quality assurance assessment KR and KRB boreholes Pilot holes FRACTURE DATA ANALYSIS Assumptions Methodology Fracture orientation Hard sectors Contoured stereonets showing all fractures and the PFL data Discrete stereonets showing the PFL transmissivities Fracture intensity Depth zones HYDROGEOLOGICAL DFN MODELLING Overview Fracture set definitions Model domain Modelling approach Case A power-law size distribution Case B log-normal size distribution Step Step Step Comparison of the two fracture size distribution models Simulation of Posiva Flow Log (PFL-f) tests Modelling approach Comparison of the three fracture transmissivity-size models Summary of Hydro-DFN models... 66

6 FDb: Depth zone 1 (0 to 50m elevation) FDb: Depth zone 2 ( 50 to 150m elevation) FDb: Depth zone 3 ( 150 to 400m elevation) FDb: Depth zone 4 ( 400 to 1 000m elevation) PREDICTION OF WATER CONDUCTING FRACTURES IN TWO TUNNEL PILOT HOLES PH8 AND PH Pilot holes PH8 and PH Modelling approach Hydro-DFN Prediction Uncertainty assessment REPOSITORY-SCALE EQUIVALENT CONTINUUM POROUS MEDIUM (ECPM) BLOCK PROPERTIES Objectives Model set-up Example visualisations Studied cases Effective hydraulic conductivity Effective kinematic porosity Summary of the upscaling study REPOSITORY-SCALE FRESHWATER FLOW AND TRANSPORT Objectives Model set-up Fraction of deposition holes connected to the DFN Case A-C/SC/UC-FDb-DZ Case A-C/SC/UC-FDb-DZ Case A/B-SC-FDb-DZ Case A/B-SC-FDb-DZ Travel times and F-quotients Directional values for Case A-C/SC/UC-FDb-DZ Minimum values for C/SC/UC in DZ3 and DZ Minimum values for Case A and Case B in DZ3 and DZ On the role of HZ for DFN connectivity Summary SUMMARY AND CONCLUSIONS OF PHASE I General Results from Phase I Hydro zones, fracture domains and Geo-DFN Primary data Key assumptions Fracture orientations Fracture intensity Fracture size Fracture transmissivity Prediction of water conducting fractures Repository-scale ECPM block properties Repository-scale freshwater flow and transport PA properties Discussion Outstanding issues data interpretation

7 3 11 SITE-SCALE EQUIVALENT CONTINUUM POROUS MEDIUM (ECPM) BLOCK PROPERTIES Objectives Model set-up Visualisations Effective hydraulic conductivity Effective kinematic porosity Block property statistics SITE-SCALE FRESHWATER FLOW AND TRANSPORT Objectives Model set-up Example visualisations Case Transport statistics Case 1-1 a single realisation of the model with one particle per start position Case 1-10 a single realisation of the model with ten particles per start position Case ten realisations of the model with ten particles per start position SITE-SCALE SALTWATER FLOW AND TRANSPORT Objectives Model set-up Results SUMMARY AND CONCLUSIONS OF PHASE II General Results from Phase II Upscaling Flow and transport Outstanding issues site modelling Future Hydro-DFN studies REFERENCES Appendix A: Pilot holes PH Appendix B: Repository-scale ECPM properties Appendix C: Repository-scale particle tracking results Appendix D: Hydro zone properties Appendix E: Primary HYDRO-DFN data references Appendix F: On the role of the guard zone technique and different spatial scales for the calculation of ECPM block conductivity

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9 5 1 INTRODUCTION 1.1 Background A hydrogeological discrete fracture network (Hydro-DFN) study of Olkiluoto is required as part of the 2008 site descriptive model (SDM) to provide an integration of fracture geometrical and hydraulic data from boreholes (KR and KRB), pilot holes (PH) and tunnels with site-scale groundwater flow and solute transport modelling. The objective being to give better support to the description of groundwater flow and transport processes and parameters based on detailed data from the field. There are several interfaces to other disciplines required by this type of work. The conceptual framework for fracturing was taken from the geological discrete fracture network (Geo-DFN) as input, along with fracture mapping and Posiva Flow Logging (PFL) of flowing features, and the structural model of deformation zones. 1.2 Objectives and scope Objectives For practical reasons, the work was divided into two phases. The aims for the 2008 Hydro-DFN study of Olkiluoto (2008 OHDFN) include: Phase I (June 2008) To produce a Hydro-DFN model for the sub-domains defined in the Geo-DFN calibrated on surface borehole and pilot-hole data (fracture core data and PFL data) suitable for describing flow and transport properties in the immediate repository target volume To make predictions of frequencies, orientations and transmissivities of water conducting fractures in two pilot holes not drilled at the time of this work. To provide hydraulic properties to support the FEFTRA EPM modelling. To investigate groundwater flow and transport pathway statistics through the bedrock appropriate to the bedrock immediate to the repository. Phase II (November 2008) To investigate groundwater flow and transport pathway statistics through the bedrock on a site-scale. To derive site-scale equivalent continuum porous medium (ECPM) hydraulic and transport properties in support of FEFTRA ECPM modelling. To describe the Hydro-DFN in a supporting document to the SDM 2008.

10 Scope Phase I Performing a statistical analysis of fracturing observed in 56 sub-vertical surface boreholes (KR and KRB) and 7 sub-horizontal tunnel pilot holes (PH) with particular focus on water conducting fractures. Specifying an appropriate conceptual model for a Hydro-DFN, e.g. defining appropriate hydraulic fracture domains based on spatial trends including depth and fracture domain. Parameterise a Hydro-DFN suitable for describing flow and transport properties in the immediate repository target volume. Making predictions on frequencies, orientations and transmissivities of water conducting fractures in two planned tunnel pilot holes. Calculate 50m ECPM block-scale properties for a bedrock volume appropriate to the repository-scale. Investigate groundwater flow and transport pathway statistics through the bedrock immediate to the repository volume. Phase II Parameterise all remaining hydraulic fracture domains calibrated on deep borehole and pilot-hole data (fracture core data and PFL data). Calculate 50m ECPM block-scale properties for a bedrock volume appropriate to the site-scale. Produce a site-scale Hydro-DFN model including the hydro-structural features for calculating groundwater flow and transport pathway statistics through the bedrock. Reporting the findings and responding to review comments. 1.3 Structure of this report Phase I Section 2 presents an overview of the nominal model areas of Olkiluoto. Section 3 presents the deterministically modelled hydrogeological zones (also called hydro zones and denoted by HZ) and a suggested division of the bedrock in between the hydro zones into two fracture domains. These are here referred to as FDa and FDb. FDa occurs above the suite of zones referred to as HZ20A-B, whereas FDb occurs below this suite of zones. The division is in line with the hanging wall and footwall bedrock concept suggested in the geological DFN model.

11 7 Section 4 presents an overview of the primary data gathered with the PFL method in the KR, KRB and PH boreholes. The presentation is made with regard to the modelled hydro zones and fracture domains. Section 4 also provides a list of reasons why it was not possible to use all the all PFL data coinciding with the two fracture domains in the Hydro-DFN modelling reported here. Section 5 collates the fracture data gathered in the KR and KRB boreholes with regard to fracture type (all, open, PFL), fracture set (orientation; NS, EW, SH), bedrock segment (HZ, FDa, FDb) and elevation (depth zone; DZ1, DZ2, DZ3, DZ4). The primary output of the data compilation is the computed Terzaghi corrected linear (1D) fracture intensities. The linear intensities are used as estimates of the fracture surface area per unit volume of bedrock. Appendix A collates the fracture data gathered in the PH boreholes. The PH borehole statistics are for verification tests in the Hydro-DFN modelling reported here, see Section 7. Section 6 concerns numerical simulations with the objective to derive optimal model parameter values for fracture size and fracture transmissivity in fracture domain FDb using data from the KR and KRB boreholes. The modelling is done in sequence, fracture size model parameter values being determined first. - The size analysis explores two different distribution models, power-law (Case A) and log-normal (Case B), based on a decision by Posiva /Löfman and Poteri 2008/. Optimal parameter values for each size model are determined with regard to the computed Terzaghi corrected linear fracture intensities. A key component in the optimisation is the requirement of fracture connectivity. - The transmissivity analysis explores three different models relating fracture transmissivity and fracture size: correlation without uncertainty, correlation with uncertainty (semi-correlation) and no correlation (random uncertainty). The optimisation is made with regard to several criteria, the most important of which being the histogram of measured specific capacities (Q/s, also called specific flow rates). Hydro-DFN model parameters are collated with regard to the four depth zones DZ1-DZ4 in fracture domain FDb. Section 7 presents predictions of frequencies, orientations and transmissivities of water conducting fractures in two, planned tunnel pilot holes, PH8 and PH9. Section 8 presents ECPM effective hydraulic properties for the bedrock immediate to the repository. Section 9 presents groundwater flow and transport pathway statistics through the bedrock immediate to the repository. Section 10 discusses the findings during Phase I Phase II Section 11 presents ECPM equivalent hydraulic properties for the Olkiluoto sitescale bedrock.

12 8 Section 12 presents freshwater flow and transport pathway statistics for the Olkiluoto site-scale bedrock. Section 13 presents saltwater flow and transport pathway statistics for the Olkiluoto site-scale bedrock. Section 14 discusses the findings during Phase II Addendum work Update of kinematic porosities used in Phase I The effective kinematic porosity is calculated as the cumulative volume of the flowing pore space divided by the block volume. In Phase I, the contribution to the flowing pore space was calculated from the following function (cf. section 8.6): e t = 0.46 T (8-3) where e t is the transport aperture and T is the fracture transmissivity. In Phase II, the contribution to the flowing pore space was calculated from the cubic law for the connected fractures (cf. section 11.5): e h = (T / ( g)) 1/3 (11-2) e t = 4 e h (11-3) Posterior to the completion of the flow modelling work, it was decided to update the kinematic porosities derived in the ECPM effective hydraulic properties for the bedrock immediate to the repository. The update is reported as an addendum to Appendix B. Upscaling of equivalent block conductivities In Phase I (Chapter 8), the guard zone technique in ConnectFlow /Jackson et al. 2000/ was used where flow is calculated in a domain, 150 m, but only the flux through central 50 m block is used to calculate the equivalent hydraulic conductivity tensor, K eff. In Phase II (Chapter 11), the guard zone technique was not used while the equivalent hydraulic conductivity tensor was calculated for the 50 m block. It was suggested in Chapter 14 that it is the use of the guard zone technique that cause the lower mean hydraulic conductivities in depth zones 2-4 of the repository-scale model compared those of the site-scale model, cf. Table In Chapter 14, it was also suggested that the dependence of upscaled hydraulic properties on spatial scale needs to be studied further to quantify the uncertainty in groundwater fluxes depending on the choice of spatial resolution in ECPM models. In conclusion, while completing this modelling report it was decided to investigate the issues further to better quantify the origin of the differences seen. The upscaling cases studied are: 50 m, 30 m and guard zone (50 m). The results are reported in the addendum in Appendix F.

13 9 2 NOMINAL MODEL AREAS OF OLKILUOTO Olkiluoto is situated on an island in the southwest of Finland within the municipality of Eurajoki about 200 km west of Helsinki. Figure 2-2 shows a map of the Olkiluoto area. The site area is located in the centre of island and the ONKALO area is located in the centre of the site area. Figure 2-1. A plane view of the nominal model areas of Olkiluoto. The location of the Olkiluoto site area is shown in the centre. Reproduced from /Mattila et al. 2008/. Figure 2-2 shows a close up of the site (investigation) area together with boreholes and investigation trenches. The site area is modelled in 3D. Figure 2-3 shows a 3D view of the Olkiluoto island showing the model volume of the geological site model (GSM). The present version of the GSM (version 1) is described in /Mattila et al. 2008/ and is an update of the initial version (version 0) described in /Paulamäki et al. 2006/. Version 1 combines the results of geological surface mapping, drill core studies and tunnel mapping, with interpretations of geophysical data from airborne and ground surveys, and geophysical borehole measurements. The development has greatly benefited from the discussions with the end users of the model, i.e. rock mechanics, hydrogeology and hydrogeochemistry, during the many integration meetings after the release of the initial version. It should be noted that the GSM activities run parallel with activities related to modelling of a much smaller model volume, whose upper surface is represented by the ONKALO area (see Figure 2-1). The aim of the ONKALO model, which essentially contains the ONKALO access tunnel and will contain the future ONKALO rock characterisation facility, is to support the rock engineering effort and provide rock mechanics and hydrogeological predictions as tunnelling proceeds.

14 10 Figure 2-2. Map of the Olkiluoto site (investigation) area with surface boreholes OL- KR1 to OL-KR43. (Data from OL-KR1 to OL-KR40 are used in the work reported here.) Reproduced from /Mattila et al. 2008/. Figure 2-3. A 3D view of Olkiluoto island showing the model volume of the geological site model described in /Mattila et al. 2008/. The sub-vertical boreholes drilled from the surface and the ONKALO access tunnel are shown within the model volume. Reproduced from /Mattila et al. 2008/.

15 11 3 HYDRO ZONES AND FRACTURE DOMAINS A cornerstone of the bedrock hydrogeological description concerns the hydraulic characterisation of the more intensely fractured deformation zones and the less fractured bedrock in between (outside) these zones. The approach taken by Posiva combines a deterministic representation of the hydrogeologically active deformation zones (HZ) with a stochastic representation of the less fractured bedrock outside these zones using a hydrogeological discrete fracture network (Hydro-DFN) concept. The HZ and Hydro- DFN models are parameterised hydraulically with data from single-hole Posiva Flow Log (PFL) pumping tests. 3.1 Model of hydro zones From a hydrogeological perspective, the geological deformation zones describe the potential pathways for fluid flow. The hydro zone (HZ) model presented by /Ahokas et al. 2007/ and /Vaittinen et al. 2009/ describes site-scale hydrogeologically active deformation zones, where high transmissivities are common and hydraulic connections between boreholes are detected as pressure and flow responses during pumping tests and other field activities. One zone is based on anomalous low head observations. These studies suggest that the most important hydrogeological zones in the Olkiluoto site area are zones HZ19A-C, HZ20A-B and HZ21. However, due to known heterogeneity of hydraulic properties, no particular definition for measured transmissivity has been determined for the definition of a hydro zone. Figure 3-1 shows the site-scale hydro zones provided for the work reported here. Figure 3-1. Visualisation of the site-scale hydro zone (HZ) model based on /Vaittinen et al. 2009/.

16 Model of fracture domains The idea of the fracture domain concept is to homogenise the spatial variations observed in the fracture data in between zones. Ideally, the rock units within a well defined hydrogeological fracture domain show less variation in the fracture characteristics (orientation, intensity (frequency), size, spatial distribution) and fracture transmissivity than between fracture domains. The attempted division of the bedrock within the Olkiluoto site area into two fracture domains is based on a notion suggested in the geological DFN modelling. According to /Buoro et al. 2009/, the bedrock in the surroundings of the ONKALO tunnel can be separated into an upper rock block (hanging wall bedrock) and an intermediate zone, and a lower rock block (footwall bedrock). The extension of the intermediate zone is approximately given by the extension of the of two large gently-dipping geological deformation zones, BFZ080 and BFZ098. According to /Ahokas et al. 2007/ and /Vaittinen et al. 2008/, zone BFZ080 and zone BFZ098 intersect almost the same borehole sections (intervals) as zone HZ20A and zone HZ20B. The transmissivity of the two hydrogeological zones vary typically between 10 6 and 10 5 m 2 /s /Ahokas et al. 2007/ and /Vaittinen et al. 2008/. Following the notion of a hanging wall segment and a footwall segment, the bedrock above zone HZ20A is here referred to as fracture domain above (FDa) and bedrock below zone HZ20B as fracture domain below (FDb). For the sake of the work reported here, an algorithm was defined and used to determine if a particular fracture in between the hydrogeological zones occur in fracture domain FDa or in FDb. The algorithm was defined by manually fitting a plane to the bottom of zone HZ20B and computing the normal equation of that plane: cos( ) x cos( ) y cos( ) z p 0 (3-1) where cos( ), cos( ) and cos( ) are the direction cosines of the normal to the fitted plane and p is the distance from the plane to the origin of the coordinate system. For fractures above the plane, i.e. fractures in FDa, the left hand side of Equation (3-1) > 0 and vice versa. The values of cos( ), cos( ), cos( ) and p are shown in Table 3-1. Figure 3-2 shows a visualisation of the boreholes and the positions of the PFL data provided for hydrogeological DFN modelling in the work reported here. Fracture domain FDa occurs above the highlighted zones HZ20A and HZ20B, whereas fracture domain FDb occurs below. Table 3-1. Parameter values of the normal equation of the plane used to determine whether a particular fracture occurs in fracture domain FDa or fracture domain FDb. cos( ) cos( ) cos( ) p

17 13 Figure 3-2. Visualisation of the boreholes and the positions of the PFL data based on /Vaittinen et al. 2009/. Fracture domain FDa occurs above the highlighted zones HZ20A and HZ20B, whereas fracture domain FDb occurs below. View towards the southwest.

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19 15 4 PRIMARY DATA This section describes the data used in the construction of Hydro-DFN models of the bedrock in between the hydrogeological zones, i.e. fracture domains FDa and FDb and their associated depth zones (cf. section 3). Hydraulic data (fracture transmissivities) are determined with the Posiva Flow Log (PFL) and the associated geometrical data (fracture positions and orientations) are determined from drill core mapping and/or borehole TV images. The PFL method is a geophysical logging device developed to detect continuously flowing fractures in sparsely fractured crystalline bedrock by means of difference flow logging, see Figure 4-1. The physical limitations of the measurement device and the principles for operation are explained in the measurement reports. The practical lower measurement limit (threshold) of the PFL method in terms of transmissivity is typically T 10 9 m 2 /s. For an example, a view of high transmissivities (T > 10 6 m 2 /s) observed in KR1-KR39 is presented in Figure Single-hole hydraulic tests Fracture transmissivities are measured systematically with PFL method in the following boreholes: 40 KR surface boreholes: (OL-)KR1 to KR40 16 KRB surface boreholes: (OL-)KR15B-20B, KR22B-23B, KR25B, KR27B, KR29B, KR31B, KR33B, KR37B, KR39B-40B 7 PH tunnel boreholes: (ONK-)PH1-PH7 Due to practical reasons, the names of the boreholes are in this report given without OLand ONK-prefixes, e.g. OL-KR1 is in this report referred to as KR1, OL-KR15B as KR15B, ONK-PH1 as PH1, etc. Moreover, the tunnel boreholes PH1-7 are here referred to as pilot holes. The pilot holes are sub-horizontal boreholes, whereas the surface boreholes are sub-vertical. Appendix E lists the references that describe the data acquisition in each of the used boreholes. An overview of the hydrogeological database is found in /Tammisto et al. 2009/. Table 4-1 shows the number of PFL data in each borehole with regard to fracture domains and hydro zones. >T< denotes the total number of PFL data in the two fracture domains, FDa and FDb, that were not possible to use in the Hydro-DFN modelling for one or several reasons, e.g. missing position data, orientation data or transmissivity data, see Section 4.2.

20 16 Pump Winch Computer Measured flow EC electrode Flow sensor -Temperature sensor is located in the flow sensor Single point resistance electrode Rubber disks Flow along the borehole Figure 4-1. Schematic drawing of the down-hole equipment used for difference flow logging in Olkiluoto. Reproduced from /Sokolnicki and Rouhiainen 2005/. View from southwest Boreholes KR1-KR39 Red=T>1E-5 Purple=T>1E-6 Figure 4-2. Position of PFL transmissivities in boreholes KR1-KR39 (T >10 5 m 2 /s are shown in red, 10 6 <T<10 5 m 2 /s are shown in purple). View towards the northeast. Discs representing transmissivities do not show the orientation of fractures i.e. they all are perpendicular to the axis of a borehole. Reproduced from /Ahokas et al. 2007/.

21 17 Table 4-1. Compilation of the number of PFL data in each borehole with regard to fracture domains (FDa and FDb) and hydro zones (HZ). >T< denotes the total number of PFL data in the two fracture domains that were not possible to use in the Hydro- DFN modelling for one or several reasons, e.g. a missing transmissivity value, position and/or orientation. KR FDa FDb HZ >T< KR 15 15B 16 16B 17 17B 18 18B 19 19B 20 20B FDa FDb HZ >T< KR 22B 23 23B B B B FDa FDb HZ >T< KR 31B B B B 40 40B FDa FDb HZ >T< PH Sum KR Sum KRB Sum PH FDa FDb HZ >T< (18% of FDa+b) 61 (28% of FDa+b) 68 (43% of FDa+b)

22 Quality assurance assessment The observations made for the KR, KRB and PH boreholes during the data quality assurance assessment are listed below. The list shows the reasons behind the figures denoted by >T< in Table KR and KRB boreholes There are fracture records in the primary fracture data supplied by Posiva. Of these, records are defined as PFL records. The PFL records are defined with a non-null value in the column marked up as Prg_tec depth. Of the PFL records, 192 PFL records were discarded because they had not been associated with a fracture in the core/image logs (lacked an M_FROM value), which is needed to calculate position, i.e. elevation, from the borehole trajectory files (called PTH files), thus leaving PFL records with useable elevations. A further 18 PFL records were discarded because they did not have an angle of inclination to the core (either an ALPHA or ALPHA_CORE value), required to calculate a Terzaghi corrected value, thus leaving useable PFL records. A further 146 PFL records were then discarded because we could not determine their orientation because the records lacked both a DIP/DIP_CORE and/or a DIR/DIR_CORE value, thus leaving useable PFL records. In conclusion, a total of PFL records were used in the fracture frequency calculations reported here in section 5. It should also be noted that in terms of fracture transmissivity, in 14 of the PFL records, a transmissivity value was not specified, but these records were still used in the fracture frequency calculations Pilot holes There are fracture records in the primary fracture data supplied by Posiva. Of these, 226 records are defined as PFL records. The PFL records are defined with a non-null value in the column marked up as Prg_tec depth. Of the 226 PFL records, 40 PFL records were discarded because they had not been associated with a fracture in the pilot hole core/image logs (i.e. they lacked CORE DEPTH value), which meant that they could not be matched with the corresponding M_FROM value, thus leaving 186 PFL records with useable elevations. A further 7 records were discarded because they did not have an inclination to the core (i.e. either an ALPHA or ALPHA_CORE value), required to calculate a Terzaghi corrected value, thus leaving 179 useable PFL records. A further 21 PFL records were then discarded because we could not determine their orientation because the records lacked both a DIP/DIP_CORE value and/or DIR/DIR_CORE value, thus leaving 158 useable PFL records.

23 19 In conclusion, a total of 158 PFL records were used in the fracture frequency calculations reported here in Appendix A. It should also be noted that in terms of fracture transmissivity, in 4 of the 158 PFL records a transmissivity value was not specified, but these records were still used in the fracture frequency calculations.

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25 21 5 FRACTURE DATA ANALYSIS This section considers the orientation and intensity data of the fractures mapped in the cored-drilled KR and KRB boreholes shown in Figure 3-2. The fracture statistics are collated in a variety of ways to try to discover any patterns in the occurrence and nature of the flowing connected open fractures detected with PFL method, see Figure Assumptions The following assumptions have been made in the data compilation: The location of the fractures has been determined by borehole core logs and borehole TV images. In those cases, where both types of log data exist, the borehole TV images were used to determine the location. The locations of the first and last fracture mapped in the borehole core logs approximate well the total length of borehole mapped. The errors in the orientation data in the borehole TV images are small. The measurement process for recording length down the borehole for the occurrence of PFL data are sufficiently consistent with the measurement process for the borehole TV images that the correlations of flows and individual fractures made in preparation of /Tammisto et al. 2009/ are valid. Fracture sets of continuously flowing fractures can be categorised based on orientation only, and the definitions of the mean pole and trend defined in the geological DFN for all fractures are of relevance to the hydraulic fractures. However, it is noted that the only significant result of that work used here is the hard sector classification. Three fracture sets are defined in the geological DFN by /Buoro et al. 2009/, two sub-vertical (NS and EW) and one sub-horizontal (SH). Roughly, fracture with dips 50º (plunges <40 ) belong to the two sub-vertical sets; fractures with dips <50º (plunge 40 ) are assigned to the sub-horizontal set (see Section for details). The Terzaghi correction /Terzaghi 1965/ can be used to estimate fracture intensities unbiased by the direction of a sample borehole. Having calculated unbiased (corrected) 1D fracture intensities, P 10,corr, for individual boreholes, these can be combined over boreholes of varying trajectories to estimate average values of the fracture surface area per unit volume of bedrock, P 32, i.e. P 32 P 10,corr (5-1) Stereonets are plotted as equal area lower hemisphere plots. The maximum correction factor used in the Terzaghi correction process is 7, corresponding to a minimum angle of 8 between a fracture and the axis of the core. The PFL-anomalies identified in each borehole are comparable, i.e. have similar practicable lower detection limit, i.e m 2 /s. (The geometric mean of the minimum interpreted transmissivities over the boreholes is 10 9 m 2 /s).

26 22 The frequency of open fractures is the upper limit of the intensity of potential flowing fractures. The open fractures are a subset of all fractures. Based on /Tammisto et al. 2009/, the number of open fractures is here defined as: open = all tight 24 % of filled (5-2) A flowing fracture requires connectivity between transmissive fractures. An open fracture is in this regard a potentially flowing fracture. The connected open fractures (cof) are a subset of the open fractures and the PFL data represent a subset of the connected open fractures. That is, the PFL data represent connected open fractures with transmissivities greater than the practicable lower detection limit, see Figure 5-1: P 10,all > P 10,open > P 10,cof > P 10,PFL (5-3) Figure 5-1. The frequency of 1) all fractures intersecting the borehole, 2) open fractures, 3) connected open fractures (cof) and 4) flowing fractures that have a transmissivity greater than c m2/s. BC1 and BC2 are constant-head boundary conditions. Reproduced from /Follin et al. 2007/.

27 Methodology The workflow for analysis and collation of fracture geological and hydrogeological information follows the steps: 1. The fracture categories to be quantified include: all fractures, open fractures (Equation (5-2)), and fractures associated with the PFL data. The database analysed is here referred to as /Tammisto et al. 2009/. 2. Group the fracture categories (all, open and PFL) according to whether they are inside a hydro-structural zone (HZ), or in one of the two fracture domains (FDa and FDb) using Equation (3-1). 3. Calculate linear (1D) fracture intensities, P 10, in each borehole according to various sub-sets of types of fracture. 4. Calculate Terzaghi corrected linear fracture intensities, P 10,corr, in each borehole according to various sub-sets of types of fracture. 5. Investigate possible correlations between fracture intensity and fracture domain, inside or outside a hydro-structural zone, and by depth. 6. Calculate average fracture intensities across boreholes by using borehole length weighted averages, and use Terzaghi corrected fracture intensities to limit the bias due to borehole orientations. 7. Generate equal area lower hemisphere stereonets for each rock subdivision to investigate variations in fracture orientations between boreholes, and consider variations in fracture orientation by depth. 8. Use Terzaghi corrected stereographic density plots for each rock subdivision to identify major sets and compare these with the hard sector definitions of sets defined in geological DFN model for Olkiluoto /Buoro et al. 2009/. 9. Generate stereographic pole plots for the fractures associated with PFL data colouring the poles according to the interpreted transmissivity to identify the orientation of fractures with the greatest hydrogeological significance. 10. Collate fracture intensities for various fracture sub-sets with each of the three fracture sets (NS, EW and SH) identified in the geological DFN model for Olkiluoto /Buoro et al. 2009/. 11. Calculate fracture intensities within each zone identified in the HZ model for Olkiluoto /Ahokas et al. 2007; Vaittinen et al. 2008/ The objectives of this analysis are to collate basic statistics of the three fracture categories (all, open and PFL) in a variety of ways to guide and support the development of a conceptual model for a hydrogeological DFN (Hydro-DFN).

28 Fracture orientation Hard sectors The three fracture sets derived in the geological DFN model for Olkiluoto /Buoro et al. 2009/ were used as a starting point. However, it is noted that the only significant result of that work used here is the hard sector 1 classification, see Figure 5-2. The hard sectors are given by a non-symmetric curve on the lower hemisphere stereonet. The curve separates the fractures into one sub-horizontal set and two sub-vertical sets. The curve is defined by four points with the following values of pole trend and plunge in degrees: Point 1: 320/30. Point 2: 30/35. Point 3: 130/40. Point 4: 220/40: The sub-horizontal set (SH) set is given by fractures with a plunge inside of the curve. The sub-vertical east-west (EW) set is defined by fractures north of points 1 and 2, and by fractures south of points 3 and 4. The sub-vertical north-south (NS) set is defined by fractures west of points 4 and 1, and by fractures east of points 1 and 2. Figure 5-2. All fracture poles in the geological DFN model coloured with regard to the hard sector definitions defined in /Buoro et al. 2009/. 1 Using a stereographic projection (stereoplot), the orientation data (fracture poles) is manually separated into three different sets by use of curved boundaries on the stereo plot, these are the hard sectors. The data inside the hard sectors are analysed for orientation and clustering. The analyses are based on the distribution of the intensity of fractures (amount of fractures) inside the hard sectors. The definition of the orientation of the representative vector (fracture set pole) of the fractures inside the hard sectors is based on the areas with high fracture intensity only.

29 25 In the work reported here, the following hard sector algorithm (VBA code) was used to determine the orientation set belonging of the fracture data (all, open and PFL): (5-4) The hard sector boundaries used here are shown in Figure 5-3. Table 5-1 collates the number of fractures for each set with regard to bedrock segment (HZ, FDa, FDb) and fracture type (all, open and PFL). Figure 5-3. All fracture poles in the hydrogeological DFN model coloured with regard to the hard sector definitions defined in the work reported here.

30 26 Table 5-1. Summary of the number of fractures for each set with regard to bedrock segment (HZ, FDa, FDb) and fracture type (all, open, PFL) based on the hard sector algorithm in (5-4). The values representing open fractures are derived with Equation (5-2). The PFL data encountered in FDa constitutes 8.0 % of all intersected fractures in this fracture domain. The 632 PFL data encountered in FDb constitutes 3.6 % of all intersected fractures in this fracture domain. Segment HZ FDa FDb Type all open PFL all open PFL All open PFL 1 EW NS HZ Contoured stereonets showing all fractures and the PFL data Figure 5-4 through Figure 5-7 show the stereonets of all fractures and the PFL data, respectively, with regard to the two fracture domains FDa and FDb, i.e. in the bedrock in between the hydro zones (HZ). The border between FDa and FDb is defined by Equation (3-1). The stereonets are plotted as Terzaghi corrected Fisher concentration plots using equal area lower hemisphere projection. Concentration plots are used since they indicate which sets have the highest density of fractures. The Terzaghi correction is used to reduce the bias due to the orientation of the borehole to make comparisons between boreholes of different orientation more meaningful than simple pole plots. The measure of concentration is a relative one defined in terms of % of total per 1 % area, meaning that for each 1 % area on the lower hemisphere, the number of poles within that area are counted and divided by the total number of poles to give the percentage. The contoured stereonets in Figure 5-4 through Figure 5-7 suggest: The stereonets for all fractures indicate that the sub-horizontal SH set is dominant in both fracture domains, but the two mean pole trends differ. In FDa, the mean pole trend of the SH set is c. 325, whereas it is c. 355 in FDb. Noteworthy, the two mean pole trends of the sub-vertical EW set appear to differ in a similar fashion as well; c. 345 in FDa and c. 005 in FDb. By contrast, the two mean pole trends of the sub-vertical NS set appear to be fairly similar, c. 85 in both FDa and FDb. The stereonets for the PFL data resemble by and large the stereonets for all fractures. Noteworthy, there is a fairly large amount of PFL data centred on trend c. 170 and plunge c. 50 in fracture domain FDb. It is noted that the stereonets for the open fractures are not shown since they closely resemble the stereonets for all fractures. Furthermore, the stereonets for the fracture data within the hydro zones (HZ) are not shown since the fracture networks within the HZ are not modelled in the work reported here. The symbols shown in Figure 5-4 through Figure 5-7 indicate the trend and plunge of the mean poles of the three fracture sets. The contour lines centred on these points encompass c. 68 % of the data within each fracture set. The evaluated Fisher distribution parameter values for each fracture

31 27 set (NS, EW, SH), fracture type (all, PFL) and bedrock segment (FDa, FDb) are shown in Table 5-2. Table 5-2. Summary of the evaluated Fisher distribution parameters values for the stereonets shown in Figure 5-4 through Figure 5-7. Segment, data FDa, all FDa, PFL FDb, all FDb, PFL EW, Trend ( ) EW, Plunge ( ) EW, Concentration (-) NS, Trend ( ) NS, Plunge ( ) NS, Concentration (-) SH, Trend ( ) SH, Plunge ( ) SH, Concentration (-) Figure 5-4. Contoured stereonet for fracture domain FDa: all fractures outside the hydro zones (HZ) described in /Tammisto et al. 2009/. The symbol denotes the trend and plunge of the mean poles of the three fracture sets. The contour lines centred on these points encompass c. 68 % of the data within each set. The corresponding Fisher distribution parameter values are shown in Table 5-2.

32 28 Figure 5-5. Contoured stereonet for fracture domain FDa: PFL data outside the hydro zones (HZ) described in /Tammisto et al. 2009/. The symbol denotes the trend and plunge of the mean poles of the three fracture sets. The contour lines centred on these points encompass c. 68 % of the data within each set. The corresponding Fisher distribution parameter values are shown in Table 5-2. Figure 5-6. Contoured stereonet for fracture domain FDb: all fractures outside the hydro zones (HZ) described in/tammisto et al. 2009/. The symbol denotes the trend and plunge of the mean poles of the three fracture sets. The contour lines centred on these points encompass c. 68 % of the data within each set. The corresponding Fisher distribution parameter values are shown in Table 5-2.

33 29 Figure 5-7. Contoured stereonet for fracture domain FDb: PFL data outside the hydro zones (HZ) described in /Tammisto et al. 2009/. The symbol denotes the trend and plunge of the mean poles of the three fracture sets. The contour lines centred on these points encompass c. 68 % of he data within each set. The corresponding Fisher distribution parameter values are shown in Table Discrete stereonets showing the PFL transmissivities Section considered the relationship between the orientations of all fractures with respect to the orientation of the PFL data in terms of hard sectors. Here, we considered if these sectors are also useful in interpreting the orientations of high-transmissivity, flowing features, i.e. if there is any anisotropy in flow. By a high-transmissivity, we mean here transmissivities greater than 10 6 m 2 /s. Figure 5-8 shows two stereographic pole plots of the PFL data associated with fracture domains FDa and FDb.

34 30 Figure 5-8. PFL data outside the hydro zones (HZ) described in /Tammisto et al. 2009/. Top: Fracture domain FDa. Bottom: Fracture domain FD. The poles are coloured by log10 (transmissivity) and use an equal area lower hemisphere projection. The symbol denotes the trend and plunge of the mean poles of the three fracture sets. The contour lines centred on these points encompass c. 68 % of the data within each set. The corresponding Fisher distribution parameter values are shown in Table 5-2. The two plots suggest: For both fracture domains, there is huge spread in the PFL data. However, the frequency of high-transmissivity flowing connected open fractures is dominated by the sub-horizontal SH fracture set. For FDa, SH fractures tend to dip SE, a small handful of sub-vertical, hightransmissive flowing fractures strike NW or N and dip towards W.

35 31 For FDb, SH fractures typically tend to dip S or N, a small handful of sub-vertical, high-transmissive flowing fractures strike NW or N and dip towards E. 5.4 Fracture intensity Depth zones The variation of the fracture intensity with depth was analysed by dividing the two fractured domain into twenty 50 m thick intervals by depth (elevation). Figure 5-9 shows the Terzaghi corrected linear (1D) intensity of all fractures, P 10, all, corr, and the PFL data, P 10, PFL, corr. The maximum magnitude of the Terzaghi correction factor (weight) was set to 7 (cf. the geological DFN model by /Buoro et al. 2009/). The corrected intensity plots for fracture domains FDa and FDb are shown in Figure Figure 5-11 shows the average hydraulic conductivity for each 50-m interval. For the sake of comparison, we show in Figure 5-12 the corrected intensities of all fractures, P 10,all,corr, and the PFL data, P 10,PFL,corr, in the hydro zones (HZ) and the two fracture domains combined. The plots shown in Figure 5-9 to Figure 5-12 suggest: The corrected intensity of all fractures shows a moderate decrease with depth in both the hydro zones and in the two fracture domains combined. By contrast, the corrected intensity of the PFL data shows a significant decrease with depth in these bedrock segments. For all of the studied elevations, the corrected intensity of all fractures in the hydro zones is greater than the corrected intensity of all fractures in the two fracture domains combined. For an example, the corrected intensity of all fractures in the hydro zones is c. four times the corrected intensity in the two fracture domains combined at 400m elevation. For all of the studied elevations, the corrected intensity of the PFL data in the hydro zones is c. ten times the corrected intensity of the PFL data in the two fracture domains combined. There is a depth trend in the average hydraulic conductivity down to c. 600 m elevation. Above this elevation, the average hydraulic conductivity in the hydro zones is c. two orders of magnitudes greater than the average hydraulic conductivity in the two fracture domains combined. Fracture domain FDb appears to be slightly more fractured and hydraulically conductive than fracture domain FDa for all depths above 550 m elevation. Below this elevation, there are no data gathered in fracture domain FDa. In order to create fairly homogeneous sub-volumes with regard to the depth trend in the Terzaghi corrected intensity of flowing fractures (corrected frequency of PFL data) seen, it was decided to subdivide each fracture domain into four depth zones DZ1-4 as follows, see Figure 5-13: o DZ1: 0 to 50 m elevation o DZ3: 150 to 400 m elevation DZ2: 50 to 150 m elevation DZ4: 400 to m elevation

36 32 8 Fracture intensity of all fractures by depth P10corr (m -1 ) Elevation (m) Fracture intensity of PFL fractures by depth P10corr (m -1 ) Elevation (m) Figure 5-9. Terzaghi corrected intensity of all fracture data, P10,all,corr, and the PFL data, P10,PFL,corr, by elevation in terms of 50-m thick intervals. The maximum magnitude of the Terzaghi correction factor (weight) was set to 7. Top: P10,all,corr. Bottom: P10,PFL,corr. Note the difference in scale of the ordinate axes.

37 33 Fracture intensity of PFL fractures above HZ20B by depth P10corr (m -1 ) Elevation (m) Fracture intensity of PFL fractures below HZ20B by depth P10corr (m -1 ) Elevation (m) Figure Terzaghi corrected intensity of the PFL data, P10,PFL,corr, by elevation in terms of 50-m thick intervals. The maximum magnitude of the Terzaghi correction factor (weight) was set to 7. Top: Fracture domain FDa. Bottom: Fracture domain FDb.

38 T/ L (m/s) T/ L (m/s) 34 Hydraulic conductivity above HZ20B by depth 1.E-04 1.E-05 1.E-06 1.E-07 1.E-08 1.E-09 1.E-10 1.E-11 1.E Elevation (m) Hydraulic conductivity below HZ20B by depth E-05 1.E-06 1.E-07 1.E-08 1.E-09 1.E-10 1.E Elevation (m) Figure Average hydraulic conductivity by elevation in terms of 50-m thick intervals. Top: Fracture domain FDa. Bottom: Fracture domain FDb.

39 T/ L (m/s) T/ L (m/s) Fracture intensity of all fractures by depth in HZ 8 Fracture intensity of all fractures by depth 30 7 P10corr (m -1 ) Elevation (m) Fracture intensity of PFL fractures by depth in HZ P10corr (m -1 ) Elevation (m) Fracture intensity of PFL fractures by depth P10corr (m -1 ) E Elevation (m) Hydraulic conductivity by depth in HZ P10corr (m -1 ) E Elevation (m) Hydraulic conductivity by depth E-05 1.E-05 1.E-06 1.E-06 1.E-07 1.E-07 1.E-08 1.E-09 1.E-10 1.E-11 Elevation (m) 1.E-08 1.E-09 1.E-10 1.E Elevation (m) Figure Plot of three types of data by elevation in terms of 50-m thick intervals. Top row: Terzaghi corrected intensity of all fracture data. Middle row: Terzaghi corrected intensity of the PFL data. Bottom row: Average hydraulic conductivity for each 50-m interval. The maximum magnitude of the Terzaghi correction factor (weight) was set to 7. Left column: Hydro zones (HZ). Right column: Fracture domains FDa and FDb combined.

40 36 FDa HZ20A+B DZ1: 0 to 50 m DZ2: 50 to 150 m Segment PFL Segment PFL FDa 648 FDa 449 FDb 131 FDb 211 HZ 26 HZ 97 DZ3: 150 to 400 m Segment PFL FDa 102 FDb 218 HZ 66 FDb DZ4: 400 to m Segment FDa FDb HZ PFL Figure Schematic visualisation of the number of PFL data by bedrock segment (FDa, FDb, HZ) and depth zone (DZ1-4). Table 5-3 shows the sample lengths and numbers of fractures with regard to bedrock segment (FDa, FDb, HZ) and fracture type (all, open, PFL). Figure 5-14 shows the Terzaghi corrected intensity, P 10,corr, by bedrock segment (FDa, FDb, HZ) and fracture type (all, open, PFL). Table 5-4 shows the sample lengths and numbers of fractures with regard to bedrock segment (FDa, FDb, HZ), depth zone (DZ1-4) and fracture type (all, open, PFL). Figure 5-15 shows the Terzaghi corrected intensity, P 10,corr, by bedrock segment (FDa, FDb, HZ) and depth zone (DZ1-4) of all fractures and the PFL data, respectively. This demonstrates that there is either no or only a weak depth trend in fracture intensity of all fractures, but a consistent decrease in the intensity of water conducting fractures detected by PFL with depth for FDa, FDb and HZ.

41 37 Table 5-3. Summary of sample lengths and numbers of fractures with regard to bedrock segment (FDa, FDb, HZ) and fracture type (all, open, PFL). Segment FDa FDb HZ borehole length all fractures all corr P 10, all, corr open fractures open corr P 10, open, corr PFL data PFL corr P 10, PFL, corr P10,corr (m 1 ) FDa FDb HZ all open PFL Fracture category Figure Terzaghi corrected intensity, P 10,corr, by bedrock segment (FDa, FDb, HZ) and fracture type (all, open, PFL).

42 38 Table 5-4. Summary of sample lengths and numbers of fractures with regard to depth zone (DZ1-4), fracture type (all, open, PFL) and bedrock segment (FDa, FDb, HZ). Depth zone DZ1: (0 to 50) masl DZ2: ( 50 to 150) masl Segment FDa FDb HZ FDa FDb HZ BH length all fractures all corr P 10, all, corr open fractures open corr P 10, open, corr PFL data PFL corr P 10, PFL, corr T PFL / BH length 2.07E E E E E E-06 Max T PFL 6.03E E E E E E-04 Min T PFL 5.21E E E E E E-09 Mean (Log(T)) St. dev. (Log(T)) Depth zone DZ3: ( 150 to 400) masl DZ4: ( 400 to 1000) masl Segment FDa FDb HZ FDa FDb HZ BH length all fractures all corr P 10, all, corr open fractures open corr P 10, open, corr PFL data PFL corr P 10, PFL, corr T PFL / BH length 1.99E E E E E E-07 Max T PFL 2.96E E E E E E-05 Min T PFL 2.04E E E E E-09 Mean (Log(T)) St. dev. (Log(T))

43 39 7 P10,all,corr (m 1 ) FDa FDb HZ 0 DZ1 (0 to 50) DZ2 ( 50 to 150) DZ3 ( 150 to 400) D4Z ( 400 to 1000) Depth zone P10,PFL,corr (m 1 ) FDa FDb HZ DZ1 (0 to 50) DZ2 ( 50 to 150) DZ3 ( 150 to 400) D4Z ( 400 to 1000) Depth zone Figure Terzaghi corrected intensity, P 10,corr, by depth zone (DZ1-4) and bedrock segment (FDa, FDb, HZ). Top: all fractures. Bottom: PFL data.

44 40

45 41 6 HYDROGEOLOGICAL DFN MODELLING 6.1 Overview A flowing fracture requires connectivity between transmissive fractures. An open fracture is in this regard a potentially flowing fracture. A sealed fracture is regarded as impervious. Partly open fractures (i.e. partial break in the core) are classified as open fractures. The connected open fractures (cof) are a subset of the open fractures and the PFL data represent a subset of the connected open fractures. That is, the PFL data represent connected open fractures with transmissivities greater than the practicable lower detection limit, see Figure 5-1: P 10,all > P 10,open > P 10,cof > P 10,PFL (6-1) The key input to Hydro-DFN simulations is the open fracture surface area per unit volume of bedrock, P 32. Since P 32 is based on a volume sample, it is not dependent on a sample direction as with linear (P 10 ) and area (P 21 ) samples, i.e. it is unbiased. However, P 32 is not readily measured directly. In practice, P 32 can be estimated from P 10,corr and adjusted if necessary by calibration against numerical simulations. Besides models for fracture orientation and fracture intensities, a Hydro-DFN model consists of descriptions for: the spatial distribution of the fracture centres in space, the fracture size distribution, and the fracture transmissivity distribution. Here, we have assumed that the locations of the fracture centres in space can be mimicked by a Poisson process. Fracture trace lengths (not sizes) can be measured as seen on outcrops and in tunnels. Because it is not possible to directly measure, fracture size is normally derived via mathematical modelling. Table 6-1 shows the two size models attempted here based on a decision by Posiva /Löfman and Poteri 2008/. Case A used a power-law size model, whereas Case B used a log-normal size model. Table 6-1. Two fracture size models were attempted in the work reported here based on a decision by Posiva /Löfman and Poteri 2008/. Case Potentially water conducting fractures Not water conducting fractures A Open fractures All other fractures B PFL fractures All other fractures Fracture size model and parameter values Power-law fracture size distribution with the location parameter equal to the borehole radius The shape parameter to be determined as part of Hydro-DFN flow simulations to match PFL intensities Log-normal fracture size distribution with a log 10 standard deviation around ¼ order of magnitude The log 10 mean to be determined as part of Hydro- DFN flow simulations to match PFL intensities

46 42 The key parameters for a power-law fracture size distribution, measured in terms of the radius r of a disc, are the shape parameter (k r ) and the location parameter (r 0 ). The distribution, f(r), is often defined only in a truncated range, between r min and r max. kr krr0 f ( r) (6-2) kr 1 r where r max r r min r 0, r 0 > 0, and k r >0. In comparison, the key parameters for a log-normal fracture size distribution, measured in terms of the radius r of a disc, are the mean (m) and the standard deviation (s) of the common logarithm (log 10 ) of r. The distribution, f(r), is often defined only in a truncated range, between r min and r max : 2 1 (log r m) f ( r) exp (6-3) ½ 2 r ln(10) s (2 ) 2s where r max r r min and s 0. In both cases, the quantitative calibration of fracture transmissivity was attempted for three different size-transmissivity models, see Table 6-2. Table 6-2. Transmissivity parameters used for all sets when matching measured PFL-f flow distributions. Type Description Relationship Parameters Correlated Power-law relationship log(t) = log(a r b ) a, b Semi-correlated Log-normal distribution about a power-law correlated mean log(t) = log(a r b ) + σ log(t) N(0,1) a, b, σ log(t) = 1 Uncorrelated Log-normal distribution about a specified mean log(t) = μ log(t) + σ log(t) N(0,1) μ log(t), σ log(t) To assess the goodness of fit for the tested fracture transmissivity models, the following statistics were calculated: Average total flow to the abstraction borehole over 40 realisations; Histogram of flow rate to borehole divided by drawdown (notated Q/s) as an average over 40 realisations. The comparison of histogram shape was quantified by the correlation coefficient of the number of flowing features with each histogram bin (½ order of magnitude in Q/s);

47 43 Bar and whisker plot of minimum, mean minus standard deviation, mean, mean plus standard deviation, maximum of log(q/s) for the inflows within each fracture set taken over all realisations; The average numbers of fractures within each set giving inflows to the abstraction borehole above the measurement limit for the PFL-f tests. In the work reported here, the same transmissivity assignments were used for each fracture set and at each depth in order to quantify how well a simplistic model could reproduce the data. That is, in the first instance we try to explain variations in flow by variations in fracture intensity and the resultant network connectivity. Moreover, we have constrained the Hydro-DFN modelling in section 6 to treat the conditions in the bedrock below the hydro zones HZ20A and HZ20B mainly, i.e. fracture domain FDb. However, we do report one Hydro-DFN model for fracture domain FDa, see section Fracture set definitions All modelling performed in this study uses the hard sector definition of fracture sets defined by the script in Equation (5-4). The Univariate Fisher distribution parameters used to model the PFL fracture orientations obsereved in fracture domain FDb are given in Table 6-3. The corresponding data for fracture domain FDa are provided for the sake of comparison. It is noted that the settings differ slightly compared to Table 5-2. Table 6-3. Parameters values used in the Univariate Fisher distribution for fracture orientations in fracture domains FDa and FDb. Fracture domain Set Trend Plunge Concentration FDa EW FDa NS FDa SH FDb EW FDb NS FDb SH Model domain Because of the variations in borehole orientation, all calibration of the Hydro-DFN models derived was performed on the basis of comparing the estimated P 32,open and P 32,PFL values deduced from Terzaghi corrected measurements, i.e.: P 32,open P 10,open,corr (6-4a) P 32,PFL P 10,PFL,corr (6-4b) with the equivalent simulated Terzaghi corrected values of open fractures, i.e.:

48 44 P 10,open,corr P 10,open,sim,corr (6-5a) P 10,PFL,corr P 10,cof,sim,corr (6-5b) The model domain extended 400m in each of the horizontal directions and m in the vertical direction. The simulated borehole was 1 km long, inserted through the middle of the model, 40 m below the top of the model and 100 m above the bottom. The lateral model extension of 400 m was chosen as an approximate average horizontal spacing between the deterministically modelled hydro zones. Apart from the vertical boundaries, the model domain contained no other hydro zones. The borehole geometry was chosen to represent the deep core drilled boreholes which are typically 1 km long and cased in the upper 40 m. The top of the casing is positioned at an elevation of 0m in the mode. An example of the model set up is shown in Figure 6-1. Figure 6-1. Example of a DFN model used in the calibration. The right picture shows all the fractures and the left just the domain and central vertical borehole. The fractures are coloured according to the depth zone in which their centres are generated. Here, a Poisson point process is assumed for the generation of fracture centres. 6.4 Modelling approach Case A power-law size distribution The methodology used for deriving a Hydro-DFN model for each fracture domain for Case A involves the following steps: 1. Perform DFN simulations based on Equation (6-4a) using an average power-law size model with k r = 2.6 and r 0 = 0.038m based on previous experience /Follin et al.

49 /. Check that the geometrical data for each fracture set given in Table 6-3 can be used in DFN simulations to yield on average the measured fracture intensity of open fractures specified in Table Based on step 1, perform connectivity analyses to test if the average power-law size model can mimic the Terzaghi corrected frequency of PFL data measured in the boreholes, i.e. P 10,cof,sim,corr P 10,PFL,corr. 3. Based on step 2, optimise the average power-law size model for each fracture set, i.e. to give a frequency of connected open fractures consistent with the set specific frequencies of PFL data measured in the boreholes, i.e. P 10,cof,sim,corr = P 10,PFL,corr. 4. Perform DFN flow simulations to calibrate hydraulic parameters and possible relationships between fracture size and transmissivity. The parameters are derived for each set, each depth zone and each rock domain. A direct correlation between fracture size and transmissivity is considered, as well as alternatives based on a semi-correlated and a completely uncorrelated model Case B log-normal size distribution The methodology used for deriving a Hydro-DFN model for each fracture domain for Case B involves the following steps: 1. Perform DFN simulations based on Equation (6-4b) using an average log-normal size model with a mean length m log(r) = 0.45, and standard deviation m log(r) = Check that the geometrical data for each fracture set given in Table 6-3 can be used in DFN simulations to yield on average the measured fracture intensity of PFL data specified in Table Based on step 1, perform connectivity analyses to make sure that the average lognormal size model indeed reproduces the Terzaghi corrected frequency of PFL data measured in the boreholes, i.e. Equation (6-4b). 3. Based on step 2, optimise the average log-normal size model for each fracture set, i.e. to give a frequency of connected open fractures consistent with the set specific frequencies of PFL data measured in the boreholes i.e. P 10,cof,sim,corr = P 10,PFL,corr. 4. Perform DFN flow simulations to calibrate hydraulic parameters and possible relationships between fracture size and transmissivity. The parameters are derived for each set, each depth zone and each rock domain. A direct correlation between fracture size and transmissivity is considered, as well as alternatives based on a semi-correlation and a completely uncorrelated model. (The DFN flow simulations run to calibrate hydraulic parameters and possible relationships between fracture size and transmissivity are presented in section 6.6.) Step 1 Comparisons of the generated and measured Terzaghi corrected fracture intensities (for the individual fracture sets and for all sets combined) based on an ensemble over 40 realisations of the Hydro-DFN for the FDb fracture domain are presented in Figure 6-2

50 46 and Figure 6-3 for Case A and Case B, respectively. As can be seen, the fracture intensities for the generated realisations are in good agreement with the measured values. The intensities for the generated realisations are slightly lower than the measured intensities for some sub-vertical sets. A maximum Terzaghi weight of 7 was used in this analysis. Increasing this maximum weight might have improved the match, but then the corrected intensity might have become overly sensitive to the contribution from a few fractures near-parallel to the borehole. This was not done for this study given the small magnitude of the discrepancies.

51 P10 corr (1/m) P10 corr (1/m) P10 corr (1/m) P10 corr (1/m) -50 to 0 m asl -150 to -50 m asl MEASURED (all BH) SIMULATED (kr=2.6,r0=0.04) MEASURED (all BH) SIMULATED (kr=2.6,r0=0.04) ALL EW NS SH ALL EW NS SH Fracture set Fracture set -400 to -150 m asl to -400 m asl MEASURED (all BH) SIMULATED (kr=2.6,r0=0.04) MEASURED (all BH) SIMULATED (kr=2.6,r0=0.04) ALL EW NS SH Fracture set ALL EW NS SH Fracture set Figure 6-2. Comparisons by depth of the generated and measured open fracture intensities (P 10,open, corr ) in a borehole for each fracture set and for the Case A (power-law) fracture size model, for the FDb fracture domain.

52 P10 corr (1/m) P10 corr (1/m) P10 corr (1/m ) P10 corr (1/m ) -50 to 0 masl -150 to -50 masl MEASURED(all BH) SIMULATED(m=0.45, s=0.25) MEASURED(all BH) SIMULATED(m=0.45, s=0.25) ALL EW NS SH ALL EW NS SH Fracture set Fracture set -400 to -150 masl to -400 m asl MEASURED (all BH) SIMULATED (m=0.45,s=0.25) MEASURED (all BH) SIMULATED(m=0.45,s=0.25) ALL EW NS SH ALL EW NS SH Fracture set Fracture set Figure 6-3. Comparisons by depth of the generated and measured PFL fracture intensities (P 10,PFL, corr ) in a borehole for each fracture set and for the Case B (log-normal) fracture size model, for the FDb fracture domain.

53 Step 2 The approach used in the connectivity analyses is to generate realisations of the open fractures (Case A) within the specified domain without any borehole present initially. The intersections between any two fractures and between a fracture and a boundary of the domain are calculated. Then, fractures that either have no connection via the network to a boundary of the domain, or ones that have only one intersection (i.e. a dead-end) are removed. Finally, the vertical borehole is inserted through the remaining connected network to obtain the intensity of connected open fractures. This procedure avoids retaining, and counting, fractures that only form connections via the borehole. For the Case B models, we fixed the fracture size distribution parameters so that no more than a small proportion of the fractures generated are disconnected from the rest of the fracture network. Examples of the connectivity analysis are shown in Figure 6-4 and Figure 6-5 below. They demonstrates how small fractures tend not to contribute to connectivity and are far less likely to form potential flow paths, leaving areas of rock through which there is little flow or no flow. This effect becomes more exaggerated for parts of the rock with low intensity of open fractures, as found at greater depth Step 3 Figure 6-6 and Figure 6-7 show the results from the optimisation of the average power-law size and log-normal size models for each fracture set, i.e. to give a frequency of connected open fractures consistent with the set specific frequencies of PFL data measured in the boreholes, i.e. Equation (6-5b). The error bars indicate the standard deviation in P 10,cof,corr over 40 realisations. Table 6-4 and Table 6-5 summarise the parameters used in the calibrated size models of Case A (power-law) and Case B (lognormal) for fracture domain FDb. 6.5 Comparison of the two fracture size distribution models In Figure 6-8, Figure 6-9 and Figure 6-10, we compare the different fracture size distributions at the initial fracture generation stage and the connectivity analysis stage of the modelling process. In summary, we make the following observations: For both size models, the connected open fracture size distribution approaches the generated fracture size distribution for sufficiently large fracture sizes The Case A and Case B size models produce different connected fracture size distributions with their current fracture size distribution parameters. In particular the Case B size model has a higher proportion of large connected fractures (50 m) and far fewer connected fractures smaller than (10 m) compared to the Case A size model. The Case A connected open fracture size distributions could possibly be approximated by log-normal distributions, but with different mean and variance parameters than we have used in Case B model.

54 50 The Case A connected open fracture size distributions provide some justification for increasing the mean size of the connected fractures as depth increases. This trend is perhaps counter-intuitive as the power-law size distributions of open fractures generated in Case A do not vary very much with depth, e.g. k r is constant over the bottom three depth zones.

55 Figure 6-4. Illustration of fracture connectivity for Case A (power-law) fracture size distribution and the FDb fracture domain. Top left: The open fractures generated. Top right: A slice through the open fractures generated. Bottom left: Connected open fractures. Bottom right: A slice through the connected open fractures. 51

56 Figure 6-5. Illustration of fracture connectivity for Case B (log-normal) fracture size distribution and FDb rock domain. Top left: The fractures generated. Top right: A slice through the fractures generated. Bottom left: The connected fractures. Bottom right: A slice through the connected fractures. 52

57 P10 cof,corr (1/m) P10 cof,corr (1/m) P10 cof,corr (1/m) P10 cof,corr (1/m) -50 to 0 m asl -150 to -50 m asl MEASURED (all BH) SIMULATED (kr=2.6,r0=0.04) SIMULATED (calibrated) MEASURED (all BH) SIMULATED (kr=2.6,r0=0.04) SIMULATED (calibrated) ALL EW NS SH ALL EW NS SH Fracture set Fracture set -400 to -150 masl to -400 m asl MEASURED (all BH) MEASURED (all BH) SIMULATED (kr=2.6,r0=0.04) SIMULATED (kr=2.6,r0=0.04) SIMULATED (calibrated) SIMULATED (calibrated) ALL EW NS SH ALL EW NS SH Fracture set Fracture set Figure 6-6. Illustration of the Terzaghi corrected connected open fracture intensities, P 10,cof,corr, for the individual fracture sets with the measured fracture intensities of PFL in FDb, for the power-law fracture size distribution. The error bars indicate the standard deviation in P 10,cof,corr over 40 realisations.

58 P10 cof,corr (1/m) P10 cof,corr (1/m) P10 cof,corr (1/m ) P10 cof,corr (1/m ) -50 to 0 masl -150 to -50 masl MEASURED(all BH) SIMULATED(m=0.05) SIMULATED(m=0.45) SIMULATED(m=1.45) SIMULATED(calibrated) MEASURED(all BH) SIMULATED(m=0.05) SIMULATED(m=0.45) SIMULATED(m=1.45) SIMULATED(calibrated) ALL EW NS SH Fracture set ALL E-W N-S SubH Fracture set -400 to -150 masl to -400 masl MEASURED (all BH) SIMULATED(m=0.05) SIMULATED(m=0.45) SIMULATED(m=1.45) SIMULATED(calibrated) MEASURED (all BH) SIMULATED(m=0.05) SIMULATED (m=0.45) SIMULATED(m=1.45) SIMULATED(calibrated) ALL E-W N-S SubH ALL E-W N-S SubH Fracture set Fracture set Figure 6-7. Comparison of the Terzaghi corrected connected open fracture intensities, P 10,cof,corr, for the individual fracture sets with the measured fracture intensities of PFL in FDb, for the log-normal fracture size distribution. The standard deviation s log(r) = 0.25 for all cases. The error bars indicate the standard deviation in P 10,cof,corr over 40 realisations.

59 55 Table 6-4. Summary of the parameters used in the calibrated Case A (power-law) fracture size model for FDb. (masl denotes metres above sea level). Elevation (masl) Set Pole orientation (trend, plunge), conc. Case A power-law (k r, r 0 ) r min = r 0 r max = 564 m Intensity P 32,open (m, - ) (m 2 /m 3 ) 50 to 0 EW (185.5, 5.3), 10.4 (2.6, 0.04) 0.44 N-S (90.7, 7.5), 8.1 (2.6, 0.04) 0.40 SH (301.3, 85), 6.1 (2.6, 0.04) to 50 EW (185.5, 5.3), 10.4 (2.7, 0.04) 0.50 NS (90.7, 7.5), 8.1 (2.7, 0.04) 0.49 SH (301.3, 85), 6.1 (2.7, 0.04) to 150 EW (185.5, 5.3), 10.4 (2.7, 0.04) 0.32 NS (90.7, 7.5), 8.1 (2.7, 0.04) 0.37 SH (301.3, 85), 6.1 (2.7, 0.04) to 400 EW (185.5, 5.3), 10.4 (2.7, 0.04) 0.22 NS (90.7, 7.5), 8.1 (2.7, 0.04) 0.24 SH (301.3, 85), 6.1 (2.7, 0.04) 0.62 Table 6-5. Summary of the parameters used in the calibrated Case B (log-normal) fracture size model for FDb. Elevation (masl) Set Pole orientation (trend, plunge), conc. Case B log-normal (m log(r), s log(r) ) r min = 0.56m r max = 564 m 3D intensity of fractures P 32,PFL (-, - ) (m 2 /m 3 ) 50 to 0 EW (185.5, 5.3), 10.4 (0.45, 0.25) 0.12 NS (90.7, 7.5), 8.1 (0.45, 0.25) 0.07 SH (301.3, 85), 6.1 (0.45, 0.25) to 50 EW (185.5, 5.3), 10.4 (0.45, 0.25) 0.07 NS (90.7, 7.5), 8.1 (0.45, 0.25) 0.06 SH (301.3, 85), 6.1 (0.45, 0.25) to 150 EW (185.5, 5.3), 10.4 (0.45, 0.25) 0.01 NS (90.7, 7.5), 8.1 (0.45, 0.25) 0.02 SH (301.3, 85), 6.1 (0.45, 0.25) to 400 EW (185.5, 5.3), 10.4 (1.45, 0.25) 0.00 NS (90.7, 7.5), 8.1 (1.45, 0.25) 0.00 SH (301.3, 85), 6.1 (1.45, 0.25) 0.02

60 Log( fracture intensity ) Log( fracture intensity ) Log( fracture intensity ) Log( fracture intensity ) to -0 masl P32 specified P10,cor casea - generated P10, cor casea - connected Log( fracture radius ) to -50 masl P32 specified P10,cor casea - generated P10, cor casea - connected Log( fracture radius ) to -150 masl P32 specified P10,cor casea - generated P10, cor casea - connected Log( fracture radius ) to -400 masl P32 specified P10,cor casea - generated P10, cor casea - connected Log( fracture radius ) 56 Figure 6-8. Fracture size distributions for the Case A (power-law) size distribution model by depth zone for FDb. The fracture size distribution parameters are taken from Table 6-4.

61 Log( fracture intensity ) Log( fracture intensity ) Log( fracture intensity ) Log( fracture intensity ) to -0 masl P32 specified P10,cor caseb - generated P10, cor caseb - connected Log( fracture radius ) to -50 masl P32 specified P10,cor caseb - generated P10, cor caseb - connected Log( fracture radius ) to -150 masl P32 specified P10,cor caseb - generated P10, cor caseb - connected Log( fracture radius ) to -400 masl P32 specified P10,cor caseb - generated P10, cor caseb - connected Log( fracture radius ) 57 Figure 6-9. Fracture size distributions for the Case B (log-normal) size distribution model by depth zone for FDb. The fracture size distribution parameters are taken from Table 6-5.

62 Log( fracture intensity ) Log( fracture intensity ) Log( fracture intensity ) Log( fracture intensity ) to -0 masl P10,cor caseb - connected P10, cor casea - connected Log( fracture radius ) to -50 masl P10,cor caseb - connected P10, cor casea - connected Log( fracture radius ) to -150 masl P10,cor caseb - connected P10, cor casea - connected Log( fracture radius ) to -400 masl P10,cor caseb - connected P10, cor casea - connected Log( fracture radius ) 58 Figure A comparison of the fracture size distributions of connected fractures for the calibrated Case A and Case B models for FDb. The fracture size parameters are taken from Table 6-4 and Table 6-5.

63 Simulation of Posiva Flow Log (PFL-f) tests Modelling approach The final stage of modelling is to account for the role of fracture transmissivity in determining both the intensity of flowing features detected by the PFL tests and the magnitudes of inflows measured in the boreholes as they are pumped. It is important at this point to recollect what is actually measured with the PFL tests. For each PFL transmissivity value identified, the change in flux (inflow) and head (drawdown) after several days of pumping relative to conditions prior to pumping are calculated. A transmissivity value is interpreted for the PFL-anomaly based on an assumed radius of influence of c. 19 m. The choice of 19m reflects that tests are performed over several days, and hence should represent an effective transmissivity of the whole fracture intersected, and possibly adjoining parts of the network, but 19m is otherwise arbitrary. Consequently, the interpreted values of transmissivity should not be viewed as necessarily the transmissivity of an individual fracture, or the transmissivity of the fracture local to the borehole intersect. They are more indicative of the effective transmissivity over a larger scale. This remark influences the way we use the PFL-f data in the Hydro-DFN modelling. The Hydro-DFN is parameterised in terms of the transmissivity of individual fractures, and may depend on the size of the fracture according to which transmissivity model is used. Steady-state DFN flow simulations of the PFL-f test configuration are used to predict the distribution of inflows to the boreholes. The idealised boundary conditions used are zero head on the top and vertical boundaries, and a drawdown of 10m along the whole 1km of borehole. Otherwise, the geometrical model configuration is the same as the connectivity simulations described in the previous section. In the field, the drawdown is typically 10m near the top, but gradually decreases, and hence the normalised flow-rate of flux, Q, divided drawdown, s, is used for the comparison of inflows. 40 realisations are performed for each simulation case. In order to investigate variations with depth, the calculated values of flow rates, Q/s, and the measurements from PFL are both divided according to the four depth zones, and then used as ensembles to compare the distribution between modelled and measured results. Three main measures are used to quantify how well the model simulates the data: A histogram of the distribution of flow-rates, Q/s, is compared with a bin size of half an order of magnitude. The total flow to the borehole, sum of Q/s. The numbers of PFL-anomalies associated with each fracture set and the distribution of Q/s for each set. This distribution is quantified in terms of the mean, plus/minus one standard deviation, minimum and maximum of log(q/s). Each of these is compared for each depth zone. For the data, statistics are calculated over the ensemble of measurements made in all boreholes for intervals within each depth zone. The statistics (such as total flow and numbers of PFL-anomalies) are then rescaled according to the thickness of the depth zone divided by the total length of

64 60 borehole sections measured within that depth zone. For the model, ensemble statistics are calculated over the 40 realisations. Hence, the statistical variability between realisations is used as an analogue of the spatial variability between boreholes. The parameterisation of the Hydro-DFN model is non-unique as a number of decisions have to be made in setting it up the model, including the relationship of transmissivity to fracture size, the fracture size distribution and the interpretation of fracture intensities for potentially open or flowing fractures. The various options are listed below. Three models for the relationship of the fracture transmissivity to fractures size are considered correlated, semi-correlated and uncorrelated. The uncorrelated and correlated models are two extremes, but a semi-correlated model, somewhere in between, is included as it is likely be more physically realistic. The non-uniqueness of the fracture size distribution is addressed by performing two cases of size models, Case A and Case B. In Case A, the fracture size distribution is based on a power-law and the source of the P 32,open fracture intensity is P 10, open, corr for open fractures. In Case B, the fracture size distribution is log-normal and the source of the P 32,PFL fracture intensity is P 10, PFL, corr for PFL fractures Comparison of the three fracture transmissivity-size models The quality of the match to the observed distributions of PFL flows for the variant in FDb with a semi-correlated transmissivity model is illustrated for Case A by Figure 6-11 through Figure 6-13 and for Case B by Figure 6-14 through Figure 6-16 below. The match to the observed flow is poorest for the deepest depth zone (below -400 masl). However, it should be noted that there are very few features carrying flow at this depth, so the distributions of PFL detected inflows are not very well defined. It was possible to find parameters for each of the three relationships between transmissivity and fracture size that would give an acceptable match to observations. Because the different types of relationship are parameterised in different ways, it is not easy to compare the different relationships.

65 Number of inflows per 250m Number of inflows per 600m Number of inflows per 50m Number of inflows per 100m < to to -9 Number of intersections in range -50 masl to 0 masl (per 50m) -9 to to -8-8 to to -7-7 to to -6-6 to -5.5 log(q/s) [m 2 /s] -5.5 to -5-5 to -4.5 Model (mean of 10 realisations) Data (PFL_f) -4.5 to -4-4 to to -3 > -3 Number of intersections in range -150 masl to -50 masl (per 100m) 5.0 Model (mean of 10 realisations) 4.5 Data (PFL_f) < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to -5.5 log(q/s) [m 2 /s] -5.5 to -5-5 to to -4-4 to to -3 > -3 Number of intersections in range -400 masl to -150 masl (per 250m) Number of intersections in range masl to -400 masl (per 600m) Model (mean of 10 realisations) Data (PFL_f) Model (mean of 10 realisations) Data (PFL_f) < to to -9-9 to to -8-8 to to -7-7 to to -6 log(q/s) [m 2 /s] -6 to to -5-5 to to -4-4 to to -3 > < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to -5.5 log(q/s) [m 2 /s] -5.5 to -5-5 to to -4-4 to to -3 > -3 Figure Histogram comparing the distribution of the magnitude of inflows divided by drawdown, Q/s, at abstraction boreholes in FDb. The model has a semi-correlated transmissivity, with a power-law fracture size distribution. The PFL-f measurements are treated as ensemble over all boreholes sections within FDb. The simulations represent the combined results of 10 realisations of the Hydro-DFN model. The numbers of intersections are normalized to the length of borehole in the heading of each graph.

66 Fracture set Fracture set Fracture set Fracture set Inflows in range -50 masl to 0 masl (per 50m) Inflows in range -150 masl to -50 masl (per 100m) 1.9 PFL E-W 2.0 PFL E-W 0.9 Model E-W 1.2 Model E-W 1.4 PFL N-S 1.7 PFL N-S 1.2 Model N-S 1.2 Model N-S 13.6 PFL SH 11.9 PFL SH 13.3 Model SH 9.7 Model SH log (Q/s) [m 2 /s] log (Q/s) [m 2 /s] Inflows in range -400 masl to -150 masl (per 250m) Inflows in range masl to -600 masl (per 600m) 1.1 PFL E-W 0.8 PFL E-W 0.7 Model E-W 0.3 Model E-W PFL N-S 0.5 PFL N-S 0.9 Model N-S 0.4 Model N-S 8.9 PFL SH 6.6 PFL SH 8.3 Model SH 4.1 Model SH log (Q/s) [m 2 /s] log (Q/s) [m 2 /s] Figure Bar and whisker plots comparing statistics taken over each fracture set for the individual inflows, Q/s, for the PFL-f data from borehole sections within FDb against statistics for an ensemble over 10 realisations of the Hydro-DFN model. The model has a semicorrelated transmissivity, with a power-law fracture size distribution. The centre of the bar indicates the mean value, the ends of the bar indicate 1 standard deviation, and the error bars indicate the minimum and maximum values. For the data statistics are taken over the identified flowing fractures within each set. For the model, statistics are taken over the fractures generated within each set and over 10 realisations. The numbers of fractures are normalized to the length indicated in the graph heading.

67 1.1E E E E E E-06 Flow (Q/s) [m 2 /s] 1.7E E Total normalized flow to borehole section 2.0E E E E-05 model PFL_f 1.2E E E E E E E to 0 masl -150 to -50 masl -400 to -150 masl to -400 masl Depth interval Figure Histogram comparing the sum of individual flows, Q/s, for the PFL-f data from borehole sections within FDb, against statistics for an ensemble over 10 realisations of the Hydro-DFN model. The model has a semi-correlated transmissivity, with a power-law fracture size distribution. For the data, statistics are taken over the identified flowing fractures. For the model, the median value of total flow is taken over 10 realisations. The flows are normalized to the borehole length indicated by the range on the horizontal axis.

68 Number of inflows per 250m Number of inflows per 600m Number of inflows per 50m Number of inflows per 100m < to to -9 Number of intersections in range -50 masl to 0 masl (per 50m) -9 to to -8-8 to to -7-7 to to -6-6 to -5.5 log(q/s) [m 2 /s] -5.5 to -5-5 to -4.5 Model (mean of 10 realisations) Data (PFL_f) -4.5 to -4-4 to to -3 > -3 Number of intersections in range -150 masl to -50 masl (per 100m) 5.0 Model (mean of 10 realisations) 4.5 Data (PFL_f) < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to -5.5 log(q/s) [m 2 /s] -5.5 to -5-5 to to -4-4 to to -3 > -3 Number of intersections in range -400 masl to -150 masl (per 250m) Number of intersections in range masl to -400 masl (per 600m) Model (mean of 10 realisations) Data (PFL_f) Model (mean of 10 realisations) Data (PFL_f) < to to -9-9 to to -8-8 to to -7-7 to to -6 log(q/s) [m 2 /s] -6 to to -5-5 to to -4-4 to to -3 > < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to -5.5 log(q/s) [m 2 /s] -5.5 to -5-5 to to -4-4 to to -3 > -3 Figure Histogram comparing the distribution of the magnitude of inflows divided by drawdown, Q/s, at abstraction boreholes in FDb. The model has a semi-correlated transmissivity, with a log-normal fracture size distribution. The PFL-f measurements are treated as ensemble over all boreholes sections within FDb. The simulations represent the combined results of 10 realisations of the Hydro-DFN model. The numbers of intersections are normalized to the length of borehole in the heading of each graph.

69 Fracture set Fracture set Fracture set Fracture set Inflows in range -50 masl to 0 masl (per 50m) Inflows in range -150 masl to -50 masl (per 100m) 1.9 PFL E-W 2.0 PFL E-W 1.1 Model E-W 1.1 Model E-W 1.4 PFL N-S 1.7 PFL N-S 0.7 Model N-S 0.7 Model N-S 13.6 PFL SH 11.9 PFL SH 16.2 Model SH 11.1 Model SH log (Q/s) [m 2 /s] log (Q/s) [m 2 /s] Inflows in range -400 masl to -150 masl (per 250m) Inflows in range masl to -600 masl (per 600m) 1.1 PFL E-W 0.8 PFL E-W 0.4 Model E-W 0.4 Model E-W PFL N-S 0.5 PFL N-S 0.8 Model N-S 0.1 Model N-S 8.9 PFL SH 6.6 PFL SH 8.9 Model SH 4.5 Model SH log (Q/s) [m 2 /s] log (Q/s) [m 2 /s] Figure Bar and whisker plots comparing statistics taken over each fracture set for the individual inflows, Q/s, for the PFL-f data from borehole sections within FDb against statistics for an ensemble over 10 realisations of the Hydro-DFN model. The model has a semicorrelated transmissivity, with a log-normal fracture size distribution. The centre of the bar indicates the mean value, the ends of the bar indicate 1 standard deviation, and the error bars indicate the minimum and maximum values. For the data, statistics are taken over the identified flowing fractures within each set. For the model, statistics are taken over the fractures generated within each set and over 10 realisations. The numbers of fractures are normalized to the length indicated in the graph heading.

70 2.0E E E E E E-06 Flow (Q/s) [m 2 /s] 1.7E E Total normalized flow to borehole section 2.0E E E E-05 model PFL_f 1.2E E E E E E E to 0 masl -150 to -50 masl -400 to -150 masl to -400 masl Depth interval Figure Histogram comparing the sum of individual flows, Q/s, for the PFL-f data from borehole sections within FDb, against statistics for an ensemble over 10 realisations of the Hydro-DFN model. The model has a semi-correlated transmissivity, with a log-normal fracture size distribution. For the data statistics are taken over the identified flowing fractures. For the model, the median value of total flow is taken over 10 realisations. The flows are normalized to the borehole length indicated by the range on the horizontal axis. 6.7 Summary of Hydro-DFN models The inferred Hydro-DFN model parameters are here collated with regard to fracture bedrock segment FDb, elevation (depth zone DZ1-DZ4) and size model (power-law, log-normal).

71 FDb: Depth zone 1 (0 to 50 m elevation) Table 6-6. Summary of Hydro-DFN parameters for the simulations of flow in fracture domain FDb, depth zone DZ1 using a power-law size model (Case A). Set Pole orientation (trend, plunge) concentration Case A power-law (k r, r 0 ) Intensity P 32,open r min = r 0 r max = 564 m Transmissivity model C: (a,b) SC: (a, b, σ log(t)) UC: (µ log(t), σ log(t)) EW (185.5, 5.3) 10.4 (2.5, 0.04) 0.44 NS (90.7, 7.5) 8.1 (2.5, 0.04) 0.40 SH (301.3, 85) 6.1 (2.6, 0.04) 1.96 (-, m) (m 2 /m 3 ) T (m 2 s -1 ) C: ( , 1.2) SC: (6 10 8, 0.7, 0.8) UC: ( , 1.3) C: ( , 1.2) SC: (6 10 8, 0.7, 0.8) UC: ( , 1.3) C: ( , 1.2) SC: ( , 0.7, 0.8) UC: ( , 1.3) Table 6-7. Summary of Hydro-DFN parameters for the simulations of flow in fracture domain FDb, depth zone DZ1 using a log-normal size model (Case B). Set Pole orientation (trend, plunge) concentration Case B log-normal (m log(r), s log(r) ) Intensity P 32,PFL r min = 0.56m r max = 564 m Transmissivity model SC: (a, b, σ log(t) ) (m,m ) (m 2 /m 3 ) T (m 2 s -1 ) EW (185.5, 5.3) 10.4 (0.45, 0.25) 0.12 SC: ( , 0.7, 1.2) NS (90.7, 7.5) 8.1 (0.45, 0.25) 0.07 SC: ( , 0.7, 1.2) SH (301.3, 85) 6.1 (0.45, 0.25) 0.41 SC: (7 10 9, 0.7, 1.2)

72 FDb: Depth zone 2 ( 50 to 150 m elevation) Table 6-8. Summary of Hydro-DFN parameters for the simulations of flow in fracture domain FDb, depth zone DZ2 using a power-law size model (Case A). Set Pole orientation (trend, plunge) concentration Case A power-law (k r, r 0 ) Intensity P 32,open r min = r 0 r max = 564 m Transmissivity model C: (a,b) SC: (a, b, σ log(t)) UC: (µ log(t), σ log(t)) EW (185.5, 5.3) 10.4 (2.6, 0.04) 0.50 NS (90.7, 7.5) 8.1 (2.6, 0.04) 0.49 SH (301.3, 85) 6.1 (2.7, 0.04) 1.61 (-, m) (m 2 /m 3 ) T (m 2 s -1 ) C: ( , 1.1) SC: (1 10 8, 0.7, 0.7) UC: ( , 1.3) C: ( , 1.1) SC: (1 10 8, 0.7, 0.7) UC: ( , 1.3) C: (1 10 8, 1.1) SC: (3 10 8, 0.7, 0.7) UC: (2 10 7, 1.3) Table 6-9. Summary of Hydro-DFN parameters for the simulations of flow in fracture domain FDb, depth zone DZ2 using a log-normal size model (Case B). Set Pole orientation (trend, plunge) concentration Case B log-normal (m log(r), s log(r) ) Intensity P 32,PFL r min = 0.56 m r max = 564 m Transmissivity model SC: (a, b, σ log(t) ) (-, - ) (m 2 /m 3 ) T (m 2 s -1 ) EW (185.5, 5.3) 10.4 (0.45, 0.25) 0.07 SC: ( , 0.7, 1) NS (90.7, 7.5) 8.1 (0.45, 0.25) 0.06 SC: ( , 0.7, 1) SH (301.3, 85) 6.1 (0.45, 0.25) 0.17 SC: ( , 0.7, 1)

73 FDb: Depth zone 3 ( 150 to 400 m elevation) Table Summary of Hydro-DFN parameters for the simulations of flow in fracture domain FDb, depth zone DZ3 using a power-law size model (Case A). Set Pole orientation (trend, plunge) concentration Case A power-law (k r, r 0 ) Intensity P 32,open r min = r 0 r max = 564 m Transmissivity model C: (a,b) SC: (a, b, σ log(t)) UC: (µ log(t), σ log(t)) (-, m) (m 2 /m 3 ) T (m 2 s -1 ) EW (185.5, 5.3) 10.4 (2.65, 0.04) 0.32 NS (90.7, 7.5) 8.1 (2.65, 0.04) 0.37 SH (301.3, 85) 6.1 (2.65, 0.04) 0.88 C: ( , 1) SC: ( , 0.7, 0.7) UC: ( , 1) C: ( , 1) SC: ( , 0.7, 0.7) UC: ( , 1) C: (4 10 9, 1.1) SC: (7 10 9, 1.1, 0.7) UC: (2 10 7, 1) Table Summary of Hydro-DFN parameters for the simulations of flow in fracture domain FDb, depth zone DZ3 using a log-normal size model (Case B). Set Pole orientation (trend, plunge) concentration Case B log-normal (m log(r), s log(r) ) Intensity P 32,PFL r min = 0.56 m r max = 564 m Transmissivity model SC: (a, b, σ log(t) ) (-, - ) (m 2 /m 3 ) T (m 2 s -1 ) EW (185.5, 5.3) 10.4 (0.45, 0.25) 0.01 SC: ( , 0.7, 1) NS (90.7, 7.5) 8.1 (0.45, 0.25) 0.02 SC: ( , 0.7, 1) SH (301.3, 85) 6.1 (0.45, 0.25) 0.05 SC: (1 10 9, 1, 1.2)

74 FDb: Depth zone 4 ( 400 to m elevation) Table Summary of Hydro-DFN parameters for the simulations of flow in fracture domain FDb, depth zone DZ4 using a power-law size model (Case A). Set Pole orientation (trend, plunge) concentration Case A power-law (k r, r 0 ) Intensity P 32,open r min = r 0 r max = 564 m Transmissivity model C: (a,b) SC: (a, b, σ log(t)) UC: (µ log(t), σ log(t)) EW (185.5, 5.3) 10.4 (2.7, 0.04) 0.22 NS (90.7, 7.5) 8.1 (2.7, 0.04) 0.24 SH (301.3, 85) 6.1 (2.7, 0.04) 0.62 (-, m) (m 2 /m 3 ) T (m 2 s -1 ) C: ( , 1) SC: ( , 0.7, 0.7) UC: ( , 1) C: ( , 1) SC: ( , 0.7, 0.7) UC: ( , 1) C: (4 10 9, 1.1) SC: ( , 1.1, 0.7) UC: (2 10 7, 1) Table Summary of Hydro-DFN parameters for the simulations of flow in fracture domain FDb, depth zone DZ4 using a log-normal size model (Case B). Set Pole orientation (trend, plunge) concentration Case B log-normal (m log(r), s log(r) ) Intensity P 32,PFL r min = 0.56 m r max = 564 m Transmissivity model SC: (a, b, σ log(t) ) (-, - ) (m 2 /m 3 ) T (m 2 s -1 ) EW (185.5, 5.3) 10.4 (1.45, 0.25) 0.00 SC: ( , 0.7, 1) NS (90.7, 7.5) 8.1 (1.45, 0.25) 0.00 SC: ( , 0.7, 1) SH (301.3, 85) 6.1 (1.45, 0.25) 0.02 SC: ( , 1, 1.2)

75 71 7 PREDICTION OF WATER CONDUCTING FRACTURES IN TWO TUNNEL PILOT HOLES PH8 AND PH9 7.1 Pilot holes PH8 and PH9 The locations of pilot holes PH8 and PH9 with regard to pilot holes PH1-7 presented in Appendix A are shown in Figure 7-1. Pilot hole PH8 is planned to be drilled at a location that partly coincides with fracture domain FDa and partly with fracture domain FDb, whereas PH9 is planned to be drilled at a location that fully coincides with fracture domain FDb. Pilot holes PH1-7 are all located in FDa. combined over boreholes of varying trajectories to estimate average values PH8-9 to make predictions in two sub-horizontal pilot boreholes Figure 7-1. Location of pilot holes in PH1-9. The modelling approach is to use the average Terzaghi corrected statistics deduced from the sub-vertical KR and KRB boreholes to predict the frequency and magnitudes of water conducting fractures in two sub-horizontal boreholes. 7.2 Modelling approach The modelling approach shown in Figure 7-1 uses the average Terzaghi corrected statistics of water conducting fractures deduced from the sub-vertical KR and KRB boreholes to predict the frequency and magnitudes of water conducting fractures in two sub-horizontal boreholes, PH8 and PH9. The success of this modelling approach is of course uncertain as it implies that the statistics of the 56 sub-vertical KR and KRB boreholes, 16 of which are very shallow, homogenised over the defined sub-domains capture the same hydrogeological conditions as encountered by two specific, subhorizontal boreholes close to repository depth.

76 Hydro-DFN In order to predict the frequency and magnitudes of water conducting fractures in pilot hole PH8, it is necessary to compute the Hydro-DFN properties for FDa, since parts of PH8 is located in depth zone 3 of this bedrock segment. (The Hydro-DFN model presented in section 6 treats fracture domain FDb only.) Table 7-1 shows the properties for the Case A fracture size model and the semi-correlated transmissivity model. Table 7-1. Summary of Hydro-DFN parameters for fracture domain FDa, depth zones DZ1-4 using a power-law size model (Case A) and a semi-correlated transmissivity model (SC). DZ Set Pole orientation (trend, plunge) concentration Case A power-law (k r, r 0 ) Intensity P 32,open r min = r 0 r max = 564 m Transmissivity model SC: (a, b, σ log(t)) (-,m ) (m 2 /m 3 ) T (m 2 s -1 ) EW (175.1,3.5) 10 (2.5, 0.04) 0.32 SC: ( , 0.7, 0.9) 1 NS (269.4,0.2) 7.4 (2.5, 0.04) 0.40 SC: ( , 0.7, 0.9) SH (304.3,78) 7.3 (2.5, 0.04) 1.68 SC: ( , 0.7, 0.9) EW (175.1,3.5) 10 (2.5, 0.04) 0.32 SC: ( , 0.7, 1.1) 2 NS (269.4,0.2) 7.4 (2.5, 0.04) 0.35 SC: ( , 0.7, 1.1) SH (304.3,78) 7.3 (2.5, 0.04) 1.21 SC: ( , 0.7, 1.1) EW (175.1,3.5) 10 (2.65, 0.04) 0.26 SC: ( , 0.7, 1.2) 3 NS (269.4,0.2) 7.4 (2.65, 0.04) 0.26 SC: ( , 0.7, 1.2) SH (304.3,78) 7.3 (2.65, 0.04) 0.73 SC: ( , 0.7, 1.2) EW (175.1,3.5) 10 (2.7, 0.04) 0.16 SC: (2 10 9, 0.7, 0.7) 4 NS (269.4,0.2) 7.4 (2.7, 0.04) 0.22 SC: (2 10 9, 0.7, 0.7) SH (304.3,78) 7.3 (2.7, 0.04) 0.33 SC: (2 10 9, 0.7, 0.7) 7.4 Prediction Figure 7-2 and Figure 7-3 show the means over 40 realisations of the number of inflows with regard to log(q/s) for pilot hole PH8. Figure 7-2 shows the means for the uppermost part of PH8, which is located in DZ3 in FDa (26 % of PH8 or c. 179 m of borehole). Figure 7-3 shows the means for the lowermost part of PH8, which is located in DZ3 in FDb (74 % of PH8 or c. 522 m of borehole). The difference in length in the two fracture domains is not sufficient to explain the difference in the number of inflows. From section 5.4 we conclude that P 10,PFL,corr is about 60% higher in DZ3 of FDb than in FDa, 0.08 m 1 vs m 1, see Table 5-4 and Figure The figures suggest it is near certain at least one fracture of transmissivity > m 2 /s will be encountered in PH8, but it is much less likley that any fractures > m 2 /s will be seen.

77 Number of inflows Number of inflows 73 Number of intersections in range -400 to -150 masl Model (mean of 40 realisations) < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5-5 to to -4-4 to to -3 > -3 log(q/s) [m 2 /s] Figure 7-2. Number of inflows with regard to log(q/s) for the uppermost part of pilot hole PH8, which is located in DZ3 in FDa. Mean over 40 realisations. Error bars show the 5th and 95th percentiles over the 40 realisations. Number of intersections in range -400 to -150 masl Model (mean of 40 realisations) < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5-5 to to -4-4 to to -3 > -3 log(q/s) [m 2 /s] Figure 7-3. Predicted number of inflows with regard to log(q/s) for the lowermost part of pilot hole PH8, which is located in DZ3 in FDb. Mean over 40 realisations. Error bars show the 5th and 95th percentiles over the 40 realisations.

78 Number of inflows 74 Figure 7-4 shows the means over 40 realisations of the number of inflows with regard to log(q/s) for pilot hole PH9, which is c. 280 m long and located in DZ3 in FDb. This suggests that it is reasonably likely no fractures will be encountered with transmissivity > m 2 /s. Number of intersections in range -400 to -150 masl 6.0 Model (mean of 40 realisations) < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5-5 to to -4-4 to to -3 > -3 log(q/s) [m 2 /s] Figure 7-4. Predicted number of inflows with regard to log(q/s) for pilot hole PH9, which is located in DZ3 in FDb. Mean over 40 realisations. Error bars show the 5th and 95th percentiles over the 40 realisations. 7.5 Uncertainty assessment As a means to address the uncertainties in the methodology as well as in comparison between sub-vertical versus sub-horizontal statistics, we made two additional prediction tests. In the first of the two additional prediction tests, we predicted the number of inflows to pilot holes PH2 and PH6, respectively. In the second one, we predicted the number of inflows to pilot holes PH1+PH2 combined and to PH combined. The first prediction test checks the approach used to predict the number of inflows to pilot holes PH8 and PH9, whereas the second prediction tests the suitability of the modelling approach as such. That is, if the first prediction test fails to do the job, whereas the second prediction test is more successful, we may conclude that the spatial variability between pilot holes is probably very large and that the average predictions shown in Figure 7-2 through Figure 7-4 only indicate the range of possible conditions that may be encountered, but not the pattern that is likely to be seen in an individual pilot hole. An interesting question is then how many (if any) of the 40 realisations carried out are close to the measured distribution. Figure 7-5 and Figure 7-6 show the results from the first prediction test and Figure 7-7 and Figure 7-8 show the results from the second. The outcome looks like the expectation. That is, the second prediction test is more successful. Table A-4 shows that

79 Number of inflows 75 P 10,PFL,corr is 0.24 m 1 in PH1 and 0.85 m 1 in PH2, which make an average of m -1. In comparison, Table 5-4 shows that the average value of P 10,PFL,corr for the 56 subvertical boreholes is 0.50 m 1 in DZ1. By the same token, Table B-4 shows that the average value of P 10,PFL,corr for PH is m 1, and Table 5-4 shows that the average value of P 10,PFL,corr for the 56 sub-vertical boreholes in DZ2 is 0.24 m 1. The conclusion is that the Hydro-DFN model can be used to predict the distribution of tunnel inflows taken as an ensemble gathered from tunnel sections totalling at least 1km, but predicting the inflows that may seen within individual tunnel sections on order of a few hundred metres is much more uncertain. A noteworthy difference is that there are several transmissivities of large magnitudes (>10 4 m 2 /s) among the 56 sub-vertical boreholes, whereas the highest values recorded for pilot holes PH1-7 is ca 100 times smaller. Examples of relevant question that may be raised here are if this difference is due to: local diffferences in the near-surface geological conditions investigated by PH1 and PH2 boreholes, and differences in the transmissivity between different fracture set in the superficial bedrock (e.g. the SH set has a higher probability of intersectin the sub-vertical KR/KRB boreholes). Number of intersections in range -50 to -0 masl Model (mean of 40 realisations) Data (PFL_f) < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5-5 to to -4-4 to to -3 > -3 log(q/s) [m 2 /s] Figure 7-5. Measured vs. predicted number of inflows with regard to log(q/s) for pilot hole PH2, which is located in DZ1 in FDa. Mean over 40 realisations. Error bars show the 5th and 95th percentiles over the 40 realisations.

80 Number of inflows Number of inflows 76 Number of intersections in range -150 to -50 masl Model (mean of 40 realisations) Data (PFL_f) < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5-5 to to -4-4 to to -3 > -3 log(q/s) [m 2 /s] Figure 7-6. Measured vs. predicted number of inflows with regard to log(q/s) for pilot hole PH6, which is located in DZ2 in FDa. Mean over 40 realisations. Error bars show the 5th and 95th percentiles over the 40 realisations. Number of intersections in range -50 to 0 masl Model (mean of 40 realisations) Data (PFL_f) < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5-5 to to -4-4 to to -3 > -3 log(q/s) [m 2 /s] Figure 7-7. Measured vs. predicted number of inflows with regard to log(q/s) for pilot holes PH1+2, which are located in DZ1 in FDa. Mean over 40 realisations. Error bars show the 5th and 95th percentiles over the 40 realisations.

81 Number of inflows 77 Number of intersections in range -150 to -50 masl Model (mean of 40 realisations) Data (PFL_f) < to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5-5 to to -4-4 to to -3 > -3 log(q/s) [m 2 /s] Figure 7-8. Measured vs. predicted number of inflows with regard to log(q/s) for pilot holes PH , which are located in DZ2 in FDa. Mean over 40 realisations. Error bars show the 5th and 95th percentiles over the 40 realisations.

82 78

83 79 8 REPOSITORY-SCALE EQUIVALENT CONTINUUM POROUS MEDIUM (ECPM) BLOCK PROPERTIES 8.1 Objectives Effective hydraulic conductivity tensors, K eff, and kinematic porosities, eff, were calculated for a 50m block size (approximately the grid size in FEFTRA) in the bedrock immediate to the repository using the statistics derived in the previous sections. The results were generated for one realisation as the objective was to provide preliminary hydraulic properties in support of the ECPM modelling with FEFTRA. 8.2 Model set-up The model domain is a cube with size (500 m) 3. The model domain is sub-divided into m blocks. Each 50 m block considered in the statistics has a guard zone of 50m in each direction to prevent fractures that are unconnected to the wider fracture network contributing to the conductivity of the block. Hence, a maximum of 9 3 (729) 50 m blocks are considered in each simulation. The fracture size distribution and transmissivity values are taken from the flow calibration. Different depth zones are considered in separate models. If there are no connected fractures generated inside a block then that block will have zero hydraulic conductivity. These cases are excluded from the calculation of hydraulic conductivity statistics. The fraction of blocks that have at least some connected fractures is presented in the results as the percolation fraction. 8.3 Example visualisations Figure 8-1 shows the front side of the fractures generated in the semi-correlated Case A model, 400 to 150 m elevation. Figure 8-3 shows a 2D slice through the centre of this realisation. Figure 8-2 and Figure 8-4 show the K xx (E-W) hydraulic conductivities corresponding to two E-W slices through the centres of the 2 models. 8.4 Studied cases Table B-1 through Table B-11 and Figure B-11 through Figure B-21 show upscaling results for the following combinations of models and depth zones, see Table 8-1.

84 80 Table 8-1. Summary of stucied combinations of size and transmissivity models. Model description Fracture size distribution model T model DZ (masl) Case A: power-law SC DZ1: 0 to 50 Case A: power-law SC DZ2: 50 to 150 Case A: power-law SC DZ3: 150 to 400 Case A: power-law SC DZ4: 400 to Case A: power-law C DZ3: 150 to 400 Case A: power-law C DZ4: 400 to Case A: power-law UC DZ3: 150 to 400 Case A: power-law UC DZ4: 400 to Case B: log-normal SC DZ2: 50 to 150 Case B: log-normal SC DZ3: 150 to 400 Case B: log-normal SC DZ4: 400 to Figure 8-1. Vertical WE visualisation of the fractures generated in the semi-correlated Case A model, 400 to 150 m of elevation. The fractures are coloured by transmissivity.

85 81 Figure 8-2. Kxx hydraulic conductivities for the vertical WE slice shown in Figure 8-1. Figure 8-3. A vertical WE slice through the centre of the model region shown in Figure 8-1.

86 82 Figure 8-4. Kxx hydraulic conductivities for the vertical WE slice shown in Figure Effective hydraulic conductivity In ConnectFlow, a symmetric positive definite 6 component tensor is calculated. The effective hydraulic conductivity, K eff, is calculated as either: K eff = (K xx K yy K zz ) 1/3 (8-1) where K xx = K 11, K yy = K 22, and Kz z = K 33, or slightly more rigorous K eff = (K max K int K min ) 1/3 (8-2) i.e. the geometric mean of the principal components (or eigenvalues of the matrix). The results reported here are based on Eq. (8-2).The statistics found in Appendix B show: The 10, 25, 50, 75, 90 percentiles of K eff based on all cells whether K eff is zero or not. The geometric mean and standard deviation of those values that have K eff >10 13 m/s (k eff = m 2 ). The percentage of cells that have K eff >10 13 m/s. 8.6 Effective kinematic porosity The effective kinematic porosity is calculated as the cumulative volume of the flowing pore space divided by the block volume. In Phase I, the contribution to the flowing pore space was calculated from the following function (N.B. it was modified in Phase II):

87 83 e t = 0.46 T (8-3) where e t is the transport aperture and T is the fracture transmissivity. The physical basis for Eq. (8-3) is uncertain, cf. /Dershowitz et al. 2003/. 8.7 Summary of the upscaling study Table 8-2 and Table 8-3 together with and Figure 8-5 through Figure 8-7 summarise the upscaling results shown in Appendix B. We make the following observations: The median ratio of (max[k xx, K yy ]/K zz ) is a factor of 2 or 3 at all depth zones, and for all the modelling variants. For the semi-correlated power-law model, the geometric mean effective conductivity decreases with depth from around m/s for DZ1, m/s for DZ2, m/s for DZ3 to m/s for DZ4. Likewise, the geometric mean kinematic porosity decreases with depth from around for DZ1-2, for DZ3 to for DZ4. The spread around the mean values increases with depth. The percolation fraction decreases with depth from around 1.0 for DZ1-2 to around 0.9 for DZ3, to around 0.4 for DZ4. These fractions do not vary much with the modelling variant. The models with log-normal fracture size distribution show a slightly higher mean conductivity and lower spread compared to models with a power-law fracture size distribution, but these differences may not be statistically significant. Table 8-2. Summary of upscaling results for repository-scale 50m K eff. Model description Fracture size distribution T model Depth zone (masl) Parameter m of log(k eff ) [m/s] s of log(k eff ) [m/s] Power-law SC 0 to Power-law SC 50 to Power-law SC 150 to Power-law SC 400 to Power-law C 150 to Power-law C 400 to Power-law UC 150 to Power-law UC 400 to Log-normal SC 50 to Log-normal SC 150 to Log-normal SC 400 to Fraction of percolation

88 84 Table 8-3. Summary of upscaling results for repository-scale 50 m eff. Model description Parameter Fracture size distribution T model Depth zone (masl) m of log( eff ) [m/s] s of log( eff ) [m/s] Power-law SC 0 to Power-law SC 50 to Power-law SC 150 to Power-law SC 400 to Power-law C 150 to Power-law C 400 to Power-law UC 150 to Power-law UC 400 to Log-normal SC 50 to Log-normal SC 150 to Log-normal SC 400 to Fraction of percolation

89 PDF CDF FDb, semi-correlated, CaseA SC casea DZ1 SC casea DZ2 SC casea DZ3 SC casea DZ to to to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5 Block K [m/s] FDb, semi-correlated, CaseA SC casea DZ1 SC casea DZ2 SC casea DZ3 SC casea DZ to to to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5 Block K [m/s] Figure 8-5. Summary of upscaling results for: FDb, Depth zones DZ1-4, Case A (Power-law size distribution), Semi-correlated transmissivity. Top: CDF of K eff. Bottom: PDF of Keff.

90 PDF CDF FDb, semi-correlated, CaseB SC caseb DZ2 SC caseb DZ3 SC caseb DZ to to to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5 Block K [m/s] 0.50 FDb, semi-correlated, CaseB SC caseb DZ2 SC caseb DZ3 SC caseb DZ to to to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5 Block K [m/s] Figure 8-6. Summary of upscaling results for: FDb, Depth zones DZ2-4, Case B (Lognormal size distribution), Semi-correlated transmissivity. Top: CDF of K eff. Bottom: PDF of Keff.

91 PDF CDF FDb, CaseA C casea DZ3 C casea DZ4 SC casea DZ3 SC casea DZ4 UC casea DZ3 UC casea DZ to to to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5 Block K [m/s] FDb, CaseA C casea DZ3 C casea DZ4 SC casea DZ3 SC casea DZ4 UC casea DZ3 UC casea DZ to to to to -9-9 to to -8-8 to to -7-7 to to -6-6 to to -5 Block K [m/s] Figure 8-7. Summary of upscaling results for: FDb, Depth zones DZ3-4, Case A (Power-law size distribution), Correlated transmissivity, Semi-correlated transmissivity and Uncorrelated transmissivity. Top: CDF of K eff. Bottom: PDF of K eff.

92 88

93 89 9 REPOSITORY-SCALE FRESHWATER FLOW AND TRANSPORT 9.1 Objectives Particle tracking simulations were carried out to investigate freshwater flow and transport pathway statistics through the bedrock model in close proximity to the repository volume. The objective is to provide preliminary information about performance assessment (PA) properties of the Hydro-DFN model in close proximity to the repository, i.e. the F-quotient and the travel time t. 9.2 Model set-up The model domain is a cube with size (200 m) 3. The particles are released from an array of 100 points. The array of release points has rows and columns spaced at 5 m intervals. At each release point, a sphere of radius 2.5 m is searched for fractures connected to the flowing fracture network. The radius of 2.5 m was chosen to approximate the height of a canister deposition hole. If no connected fractures intersect the sphere surrounding the release point the particle is not released. Ten particles are released at each release point. If there is more than one fracture within the 2.5 m radius around the release point the choice of release is weighted by the flux through the possible fractures. The fractures are generated according to the fracture size distribution parameters and fracture transmissivity parameters produced in the flow calibration stage of modelling. To make the model computationally tractable the smallest fractures (down to 0.28 m radius) are only generated in the region immediately surrounding the release points. The depth zones 150 to 400 and 400 to masl are considered separately. The transport aperture et of the fractures is assigned according to the relationship shown in Eq. (8-3). It is noted that the physical basis for this relationship is uncertain, cf. /Dershowitz et al. 2003/. Pathlines are calculated for cases when the pressure gradient is in the X (E-W) direction, Y (N-S) direction and Z (vertical) direction. The pressure gradient is 1 % in each case. The model boundary conditions are a linear pressure gradient on each of the six faces. The particle pathlines are calculated for 40 realisations of the model. Table 9-1 shows the models studied. Figure 9-1, Figure 9-2 and Figure 9-3 visualise the model set-up. Figure 9-4 through Figure 9-7 show the fraction of active particles (those for which a connected fracture is found within the release volume) in each DFN model. The results are tabulated in Appendix C. An excerpt of the results for the travel time and the F-quotient is shown in Figure 9-8 through Figure 9-13.

94 90 Table 9-1. Summary of the cases studied. Model ID Fracture size DZ T model distribution model (masl) A-SC-FDb-DZ3 Case A: power-law SC DZ3: 150 to 400 A-SC-FDb-DZ4 Case A: power-law SC DZ4: 400 to A-C-FDb-DZ3 Case A: power-law C DZ3: 150 to 400 A-C-FDb-DZ4 Case A: power-law C DZ4: 400 to A-UC-FDb-DZ3 Case A: power-law UC DZ3: 150 to 400 A-UC-FDb-DZ4 Case A: power-law UC DZ4: 400 to B-SC-FDb-DZ3 Case B: log-normal SC DZ3: 150 to 400 B-SC-FDb-DZ4 Case B: log-normal SC DZ4: 400 to Figure 9-1. Upper left: Model region showing the array of release points. There are 100 release points in 10 rows. Each row is spaced 5m apart. Upper right: Horizontal slice through the model showing the location of the deposition holes. Lower left: For the power-law fracture size distribution model fractures with radii down to 0.28 m are generated in a region surrounding the release points. Outside this region fractures with radii greater than 2.26 m are generated. Lower right: All fractures generated in the model region. The fracture size distribution and fracture transmissivity parameters are taken from the results of the flow calibration; in this case for a semi-correlated transmissivity, case A model (power-law distribution), for the FDb fracture domain in depth zone 150 to 400 masl.

95 91 Figure 9-2. Fractures connected to the deposition holes for a single realisation. This example is for the depth zone 150 to 400 masl. For the depth zone 400 to masl the fracture network is even sparser. Figure 9-3. Ensemble of pathlines produced over 40 realisations for a single release point. The pressure gradient is in the X direction. Semi-correlated transmissivity, Case A (power-law) model, fracture domain FDb, depth zone DZ3 ( 150 to 400 masl).

96 Fraction of particles in DFN (%) Fraction of particles in DFN (%) Fraction of particles in DFN (%) Fraction of deposition holes connected to the DFN Figure 9-4 compares statistics for estimates of the percentage of deposition holes that are intersected by at least one connected fracture for the Case A size model and either correlated, semi-correlated or uncorrelated transmissivity model in depth zone Case A-C/SC/UC-FDb-DZ3 A-C-FDb-DZ3 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max A-SC-FDb-DZ3 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max A-UC-FDb-DZ3 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max Figure 9-4. Histograms showing the percentages of released particles that are connected to the fracture network for Case A, FDb and DZ3. Top: Correlated model (C), Middle: Semi-correlated model (SC), Bottom: Uncorrelated model (UC).m = mean, 50% = median, sd = standard deviation.

97 Fraction of particles in DFN (%) Fraction of particles in DFN (%) Fraction of particles in DFN (%) Case A-C/SC/UC-FDb-DZ4 Figure 9-5 compares statistics for estimates of percentage of deposition holes that are intersected by at least one connected fracture for the Case A size model and either correlated, semi-correlated or uncorrelated transmissivity model in depth zone 4. A-C-FDb-DZ4 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max A-SC-FDb-DZ4 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max A-UC-FDb-DZ4 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max Figure 9-5. Histograms showing the percentages of released particles that are connected to the fracture network for Case A, FDb and DZ4. Top: Correlated model, Middle: Semi-correlated model (SC), Bottom: Uncorrelated model (UC).m = mean, 50 % = median, sd = standard deviation.

98 Fraction of particles in DFN (%) Fraction of particles in DFN (%) Case A/B-SC-FDb-DZ3 Figure 9-6 compares statistics for estimates of percentage of deposition holes that are intersected by at least one connected fracture for the Case A and Case B size model with the semi-correlated transmissivity model in depth zone 3. A-SC-FDb-DZ3 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max B-SC-FDb-DZ3 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max Figure 9-6. Histograms showing the percentages of released particles that are connected to the fracture network for SC, FDb and DZ3. Top: Case A, Bottom: Case B.m = mean, 50 % = median, sd = standard deviation Case A/B-SC-FDb-DZ4 Figure 9-7 compares statistics for estimates of percentage of deposition holes that are intersected by at least one connected fracture for the Case A and Case B size model with the semi-correlated transmissivity model in depth zone 4. The last 4 figures demonstrate the percentage of deposition holes connected to the wider fracture network is much lower in depth zone 4, around 4 %, compared to around 20 % in depth zone 3. Also, the statistics do not depend on the fracture size model used. The reason is that both models have been calibrated to give an intensity of connected open fractures that is based on the measured intensity of water conducting fractures detected by PFL.

99 Fraction of particles in DFN (%) Fraction of particles in DFN (%) 95 A-SC-FDb-DZ4 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max B-SC-FDb-DZ4 70% 60% 50% 40% 30% X Y Z 20% 10% 0% Min m-1sd 50% m m+1sd Max Figure 9-7. Histograms showing the percentages of released particles that are connected to the fracture network for SC, FDb and DZ4. Top: Case A, Bottom: Case B.m = mean, 50 % = median, sd = standard deviation. 9.4 Travel times and F-quotients Figure 9-8 compares percentiles of travel time for released released particles for the Case A size model with the correlated, semi-correlated and uncorrelated transmissivity model in depth zone 3 and three axial flow directions.

100 Travel time in DFN (y) Travel time in DFN (y) Travel time in DFN (y) Directional values for Case A-C/SC/UC-FDb-DZ3 A-FDb-DZ3-x_dir 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1E-01 1E-02 min 10% 25% 50% 75% 90% max x: C x: SC x: UC A-FDb-DZ3-y_dir 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1E-01 1E-02 min 10% 25% 50% 75% 90% max y: C y: SC y: UC A-FDb-DZ3-z_dir 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1E-01 1E-02 min 10% 25% 50% 75% 90% max z: C z: SC z: UC Figure 9-8. Histograms showing the average percentiles of the travel time in three orthogonal directions for Case A, FDb and DZ3: C = the Correlated model, SC = the Semi-correlated model and UC = the Uncorrelated model. Figure 9-9 compares percentiles of F-quotient for released released particles for the Case A size model with the correlated, semi-correlated and uncorrelated transmissivity model in depth zone 3 and three axial flow directions.

101 F-quotient (y/m) F-quotient (y/m) F-quotient (y/m) 97 A-FDb-DZ3-x_dir 1E+11 1E+09 1E+07 1E+05 x: C x: SC x: UC 1E+03 1E+01 min 10% 25% 50% 75% 90% max A-FDb-DZ3-y_dir 1E+11 1E+09 1E+07 1E+05 y: C y: SC y: UC 1E+03 1E+01 min 10% 25% 50% 75% 90% max A-FDb-DZ3-z_dir 1E+11 1E+09 1E+07 1E+05 z: C z: SC z: UC 1E+03 1E+01 min 10% 25% 50% 75% 90% max Figure 9-9. Histograms showing the average percentiles of the F-quotient in three orthogonal directions for Case A, FDb and DZ3: C = the Correlated model, SC = the Semi-correlated model and UC = the Uncorrelated model.

102 Travel time in DFN (y) Travel time in DFN (y) Minimum values for C/SC/UC in DZ3 and DZ4 The minimum of each percentile is calculated over the 3 alternative flow directions (based on axial head gradients) for the travel time in Figure 9-10 and F-quotient in Figure 9-11 for Case a size (power-law), FDb, depths zones 3 and 4. The three transmissivity models give similar results, although the correlated model has tendency toward lower travel time than the other 2 models. F-quotients tend to be less sensitive to the transmissivity model. min(a-fdb-dz3-x/y/z_dir) 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1E-01 1E-02 min 10% 25% 50% 75% 90% max C SC UC 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1E-01 1E-02 min(a-fdb-dz4-x/y/z_dir) min 10% 25% 50% 75% 90% max C SC UC Figure Histograms showing the minimum values of the average percentiles of the travel time in three orthogonal directions for the Correlated (C), the Semi-correlated (SC) and the Uncorrelated (UC) transmissivity model. Top: Case A, FDb and DZ3. Bottom: Case A, FDb and DZ4.

103 F-quotient (y/m) F-quotient (y/m) 99 min(a-fdb-dz3-x/y/z_dir) 1E+11 1E+09 1E+07 1E+05 y: C y: SC y: UC 1E+03 1E+01 min 10% 25% 50% 75% 90% max min(a-fdb-dz4-x/y/z_dir) 1E+11 1E+09 1E+07 1E+05 y: C y: SC y: UC 1E+03 1E+01 min 10% 25% 50% 75% 90% max Figure Histograms showing the minimum values of the average percentiles of the F-quotient in three orthogonal directions for the Correlated (C), the Semi-correlated (SC) and the Uncorrelated (UC) transmissivity model. Top: Case A, FDb and DZ3. Bottom: Case A, FDb and DZ4.

104 Travel time in DFN (y) Travel time in DFN (y) Minimum values for Case A and Case B in DZ3 and DZ4 Here, we compare the minimum of each percentile over the 3 alternative flow directions (based on axial head gradients) for the travel time in Figure 9-12 and F-quotient in Figure 9-13 between case A (power-law) and case B (log-normal) fracture sizes for FDb, semi-correlated transmissivity in depth zones 3 and 4. The statistics are slightly lower for both travel time and F-quotient for case B since this fracture sizes distribution is biased toward longer fractures. min(fdb-dz3-x/y/z_dir) 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1E-01 1E-02 min 10% 25% 50% 75% 90% max A:SC B:SC 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1E-01 1E-02 min(fdb-dz4-x/y/z_dir) min 10% 25% 50% 75% 90% max A:SC B:SC Figure Histograms showing the minimum values of the average percentiles of the travel time in three orthogonal directions for the Semi-correlated transmissivity model. Top: Case A, FDb and DZ3. Bottom: Case B, FDb and DZ3.

105 F-quotient (y/m) F-quotient (y/m) 101 min(fdb-dz3-x/y/z_dir) 1E+11 1E+09 1E+07 A:SC B:SC 1E+05 1E+03 1E+01 min 10% 25% 50% 75% 90% max min(fdb-dz4-x/y/z_dir) 1E+11 1E+09 1E+07 A:SC B:SC 1E+05 1E+03 1E+01 min 10% 25% 50% 75% 90% max Figure Histograms showing the minimum values of the average percentiles of the F-quotient in three orthogonal directions for the Semi-correlated transmissivity model. Top: Case A, FDb and DZ3. Bottom: Case B, FDb and DZ On the role of HZ for DFN connectivity The DFN simulations considered (cf. Figure 9-1 for an example) were not superimposed on the Olkiluoto hydro zone (HZ) model, hence the connectivity analysis carried out prior to the flow and transport simulations tacitly assumed that the HZ model do not significantly alter the entity known as the connected open fracture area per unit volume of rock, P 32,cof. In order to demonstrate the relevance of this assumption, the same DFN realisation was generated twice for Case A, where one of the simulations was superimposed on top of the HZ model and the other was not prior to the connectivity analysis. The simulated repository is located around 400 (c. 400 m depth), which is also the interface between depth zones DZ3 and DZ4. A fraction of the layout area in each depth zone intersected HZ20, see Figure 5-13 and Table 9-2. Table 9-2. Distribution of the simulated repository area with regard to HZ20 and DZ3-4. DZ % above HZ20 % below HZ20 DZ DZ

106 102 The results of the connectivity analysis for the two simulations are shown in Table 9-3 and Figure It is noted that the analysis address the spatial average within the model domain. Local effects along individual boreholes were not studied. We conclude from the numbers shown that the HZ model does not alter P 32,cof. in a significant way. Table 9-3. DFN fracture intensities without and with HZ, respectively. Depth zone P32,o (m 2 /m 3 ) P32,cof without HZ (m 2 /m 3 ) P32,cof with HZ (m 2 /m 3 ) DZ3 - below HZ E E E-02 DZ3 - above HZ E E E-02 DZ4 - below HZ E E E-02 DZ4 - above HZ E E E E E E E E E E-02 P32 - open P32 - connected without HZ P32 - connected with HZ 0.00E+00 DZ3 - below DZ3 - above DZ4 - below DZ4 - above Figure DFN fracture intensities without and with HZ, respectively. 9.6 Summary For the depth interval 150 to 400 masl, a mean of % of deposition holes are intersected by water-conducting fractures. The results for Case B (log-normal fracture size distribution) are higher because the fractures are generally longer. o The worst case model (case B) has a 50-percentile F-quotient about y/m, and 10-percentile F-quotient about y/m. o The F-quotient for vertical flow is around 1-4 times higher than horizontal flow because sub-horizontal fracturing causes more tortuous, longer paths. o The worst case is the semi-correlated transmissivity model. Uncorrelated is similar to SC. The Correlated models are higher by a factor ~5 in the F-quotient for horizontal flow, but similar for vertical. o Uncorrelated models have longer more tortuous paths, increasing the F- quotients. Correlated models typically have higher F-quotients, because a deposition hole is less likely to intersect a high transmissivity feature.

107 103 o The results for Case A and B are similar. There are lower travel times for Case B models, presumably because the fractures tend to be longer. For the depth interval 400 to 1000 masl, a mean of 3-5 % of deposition holes are intersected by water-conducting fractures. The lower percentiles may suffer from statistical convergence for this depth zone. Also the transmissivity distribution in these models may not be as well constrained due to the limited number of PFLanomalies. o The worst case model has a 50-percentile F-quotient of about y/m, 10- percentile about y/m. o The correlated transmissivity model has the lowest F-quotients. Here, there are only a few possible connections and those that are have a high transmissivity. This suggests that the importance of the correlation of the transmissivity to the fracture size depends on fracture intensity. o The semi-correlated models and uncorrelated models give 50-percentile F- quotients of around y/m. Again, these two models are quite similar o Case B is similar to Case A, but with shorter travel times. The HZ model does not alter P 32,cof. of the connected DFN in a significant way for the studied size model (Case).

108 104

109 SUMMARY AND CONCLUSIONS OF PHASE I 10.1 General The work described in section 2 to section 9 refers to Phase I of the 2008 hydrogeological discrete fracture network model of Olkiluoto. Phase I collates the structural-hydraulic information gathered in 40 long (KR) and 16 short (KRB) subvertical boreholes drilled from the surface. The information is compared with the structural-hydraulic information gathered in seven short sub-horizontal pilot boreholes (PH) drilled from the ONKALO tunnel. In conclusion, Phase I contains: A Hydro-DFN model for the sub-domains defined in the Geo-DFN has been calibrated based on fracture core data and PFL data suitable for describing flow and transport properties in the immediate repository target volume. Predictions of frequencies, orientations and transmissivities of water conducting fractures in two pilot holes not drilled at the time of this work. Preliminary ECPM effective hydraulic properties in support of the FEFTRA modelling. Preliminary transport properties. These were deduced by means of freshwater flow and transport simulations through DFN realisations of the bedrock immediate to the repository neglecting the influence of any hydro zones Results from Phase I Hydro zones, fracture domains and Geo-DFN The bedrock is divided into two fracture domains, FDa and FDb. FDa occurs above the suite of zones referred to as HZ20A-B, whereas FDb occurs below this suite of zones. The division is in line with the hanging wall and footwall bedrock concept suggested in the geological DFN model Primary data The primary data consists of fracture transmissivities determined with the PFL and the associated fracture positions and orientations determined from drill core mapping and/or borehole TV images. The modelling is based on the information gathered in the following boreholes: 40 KR boreholes: (OL-)KR1 to KR40 16 KRB boreholes: (OL-)KR15B-20B, KR22B-23B, KR25B, KR27B, KR29B, KR31B, KR33B, KR37B, KR39B-40B Table 4-1 shows the number of PFL data in each borehole with regard to fracture domains and hydro zones. >T< denotes the total number of PFL data in the two fracture domains, FDa and FDb, that were not possible to use in the Hydro-DFN modelling for

110 106 one or several reasons, e.g. missing position data, orientation data or transmissivity data, see section Key assumptions Section 5 lists a number of assumptions that are used in the data analyses and in the modelling. Three key assumptions are: 1. The Terzaghi correction /Terzaghi 1965/ can be used to estimate fracture intensities unbiased by the direction of a sample borehole. Having calculated unbiased (corrected) 1D fracture intensities, P 10,corr, for individual boreholes, these can be combined over boreholes of varying trajectories to estimate average values of the fracture surface area per unit volume of bedrock, P 32, i.e.: P 32 P 10,corr (10-1) 2. The frequency of open fractures is the upper limit of the intensity of potential flowing fractures. The open fractures are a subset of all fractures. The number of open fractures is here defined as: open = all tight 24% of filled (10-2) 3. A flowing fracture requires connectivity between transmissive fractures. An open fracture is in this regard a potentially flowing fracture. The connected open fractures (cof) are a subset of the open fractures and the PFL data represent a subset of the connected open fractures. That is, the PFL data represent connected open fractures with transmissivities greater than the practicable lower detection limit, see Figure 5-1: P 10,all > P 10,open > P 10,cof > P 10,PFL (10-3) Fracture orientations The contoured stereonets shown in Figure 5-4 through Figure 5-7 suggest: The stereonets for all fractures indicate that the sub-horizontal SH set is dominant in both fracture domains, but the two mean pole trends differ. In FDa, the mean pole trend of the SH set is c. 325, whereas it is c. 355 in FDb. Noteworthy, the two mean pole trends of the sub-vertical EW set appear to differ in a similar fashion as well; c. 345 in FDa and c. 005 in FDb. By contrast, the two mean pole trends of the sub-vertical NS set appear to be fairly alike, c. 85 in both FDa and FDb. The stereonets for the PFL data resemble by and large the stereonets for all fractures. Noteworthy, there is a fairly large amount of PFL data centred on trend c. 170 and plunge c. 50 in fracture domain FDb Fracture intensity The plots shown in Figure 5-9 to Figure 5-12 suggest:

111 107 The corrected intensity of all fractures shows a moderate decrease with depth in both the hydro zones and in the two fracture domains combined. By contrast, the corrected intensity of the PFL data shows a significant decrease with depth in these bedrock segments. For all of the studied elevations, the corrected intensity of all fractures in the hydro zones is greater than the corrected intensity of all fractures in the two fracture domains combined. For an example, the corrected intensity of all fractures in the hydro zones is c. four times the corrected intensity in the two fracture domains combined at 400 m elevation. For all of the studied elevations, the corrected intensity of the PFL data in the hydro zones is c. ten times the corrected intensity of the PFL data in the two fracture domains combined. There is a depth trend in the average hydraulic conductivity down to c. 600m elevation. Above this elevation, the average hydraulic conductivity in the hydro zones is c. two orders of magnitudes greater than in the average hydraulic conductivity in the two fracture domains combined. Fracture domain FDb appears to be slightly more fractured and hydraulically conductive than fracture domain FDa for all depths above 550 m elevation. Below this elevation, there are no data gathered in fracture domain FDa. In order to create fairly homogeneous sub-volumes with regard to the depth trend in the Terzaghi corrected intensity of flowing fractures (corrected frequency of PFL data) seen, it was decided to subdivide each fracture domain into four depth zones DZ1-4 as follows: o DZ1: 0 to 50 m elevation o DZ3: 150 to 400 m elevation DZ2: 50 to 150 m elevation DZ4: 400 to m elevation Fracture size In Figure 6-8, Figure 6-9 and Figure 6-10, we compare the two fracture size distributions studied, Case A (power-law) and Case B (log-normal), at the initial fracture generation stage and the connectivity analysis stage of the modelling process. In summary, we make the following observations: For both size models, the connected open fracture size distribution approaches the generated fracture size distribution for sufficiently large fracture sizes. The Case A and Case B size models produce different connected fracture size distributions with their current fracture size distribution parameters. In particular the Case B size model has a higher proportion of large connected fractures (50 m) and far fewer connected fractures smaller than (10 m) compared to the Case A size model.

112 108 The Case A connected open fracture size distributions could possibly be approximated by log-normal distributions, but with different mean and variance parameters than we have used in Case B model. The Case A connected open fracture size distributions provide some justification for increasing the mean size of the connected fractures in the Case B model as depth increases. This trend is perhaps counter-intuitive as the power-law size distributions of open fractures generated in Case A do not vary very much with depth, e.g. k r is constant over the bottom three depth zones Fracture transmissivity A quantitative calibration of fracture transmissivity was made for three different sizetransmissivity models, see Table 6-2. The quality of the match to the observed distributions of PFL flows for the variant in FDb with a semi-correlated transmissivity model is illustrated for Case A by Figure 6-11 through Figure 6-13 and for Case B by Figure 6-14 through Figure 6-16 below. The match to the observed flow is poorest for the deepest depth zone (below 400masl). However, it should be noted that there are very few features carrying flow at this depth, so the measured distributions of PFL is not well resolved. It was possible to find parameters for each of the three relationships between transmissivity and fracture size that would give an acceptable match to observations. Because the different types of relationship are parameterised in different ways, it is not easy to compare the different relationships Prediction of water conducting fractures The modelling approach shown in Figure 7-1 uses the average Terzaghi corrected statistics of water conducting fractures deduced from the sub-vertical KR and KRB boreholes to predict the frequency and magnitudes of water conducting fractures in two sub-horizontal boreholes, PH8 and PH9. The success of this modelling approach is of course uncertain as it implies that the statistics of the 56 sub-vertical KR and KRB boreholes, 16 of which are very shallow, represent the same hydrogeological conditions as encoutered by two specific, sub-horizontal boreholes close to repository depth. As a means to address the uncertainties in the methodology as well as in comparison between sub-vertical versus sub-horizontal statistics, we made two prediction tests. In the first test, we predicted the number of inflows to pilot holes PH2 and PH6, respectively. In the second one, we predicted the number of inflows to pilot holes PH1+PH2 combined and to PH combined. The first prediction test checks the approach used to predict the number of inflows to pilot holes PH8 and PH9, whereas the second prediction tests the suitability of the modelling approach as such. That is, if the first prediction test fails to do the job, whereas the second prediction test is more successful, we may conclude that the spatial variability between boreholes is probably very large and that the average predictions shown in Figure 7-2 through Figure 7-4 only indicate the range of possible conditions that may be encountered, but not the pattern that is likely to be seen in an individual

113 109 pilot hole. An interesting question is then how many (if any) of the 40 realisations carried out are close to the measured distribution. A noteworthy difference is that there are several transmissivities of large magnitudes (>10 4 m 2 /s) among the 56 sub-vertical boreholes, whereas the highest values recorded for pilot holes PH1-7 is ca 100 times smaller. The question raised here is if this difference shows that the SH set is more transmissive, or if the identification of hightransmissive hydro zones in the 56 sub-vertical boreholes should be revisited Repository-scale ECPM block properties Block-scale hydraulic conductivity tensors are calculated for a 50 m block size in the bedrock immediate to the repository using the statistics derived in the previous sections. The objective is to provide preliminary hydraulic properties in support of the ECPM modelling with FEFTRA. We make the following observations: The median ratio of (max[k xx, K yy ]/K zz ) is a factor of 2 or 3 at all depth zones, and for all the modelling variants. For the semi-correlated power-law model, the geometric mean effective conductivity decreases with depth from around m/s for DZ1, m/s for DZ2, m/s for DZ3 to m/s for DZ4. Likewise, the geometric mean kinematic porosity decreases with depth from around for DZ1-2, for DZ3 to for DZ4. The spread around the mean values increases with depth. The percolation fraction decreases with depth from around 1.0 for DZ1-2 to around 0.9 for DZ3, to around 0.4 for DZ4. These fractions do not vary much with the modelling variant. The models with log-normal fracture size distribution show a slightly higher mean conductivity and lower spread compared to models with a power-law fracture size distribution, but these differences may not be statistically significant Repository-scale freshwater flow and transport PA properties For the depth interval 150 to 400 masl, a mean of % of deposition holes are intersected by water-conducting fractures. The results for Case B (log-normal fracture size distribution) are higher than for Case A (power-law) because the fractures are generally longer. For the depth interval 400 to 1000 masl, a mean of 3-5 % of deposition holes are intersected by water-conducting fractures. The lower percentiles may suffer from statistical convergence for this depth zone. Also the transmissivity distribution in these models may not be as well constrained due to the limited number of PFL-anomalies. The results suggest that the importance of the correlation of the transmissivity to the fracture size depends on fracture intensity.

114 Discussion In the work reported here, the same transmissivity assignments were used for each fracture set and at each depth in order to quantify how well a simplistic model could reproduce the data. That is, in the first instance we try to explain variations in flow by variations in fracture intensity and the resultant network connectivity. The limited success in the prediction of the frequency of transmissivities of water conducting fractures in individual pilot holes PH2 and PH6 suggest that there may be a necessity to introduce further complexity such as anisotropy between sets and spatial heterogeneity. Alternatively, there may be a limited number of PFL data measured in the sub-vertical boreholes that should be associated with hydro zones instead of fracture domain FDa. Moreover, we have constrained the Hydro-DFN modelling presented here to treat the conditions in the bedrock below the hydro zones HZ20A and HZ20B mainly, i.e. fracture domain FDb. It is noted that in section 7, we present a limited Hydro-DFN model for FDa, i.e. Case A (power-law size distribution) and a semi-correlated transmissivity model. The differences between FDa and FDb are marginal. The simulation domain used in the connectivity analyses presented in Section 9 does not contain any hydro zones, which means that the deduced DFN connectivity is governed by the geometrical and hydraulic properties of the connected open fractures vis-à-vis the distance to the vertical boundaries of the model domain. The simulation results shown in Section 9.4 do not suggest, however, that the inclusion of hydro zones significantly alter the net P 32,cof of the connected DFN. The site-scale freshwater and saltwater DFN flow and transport PA simulations reported in Sections 1 and 13, respectively, includes hydro zones Outstanding issues data interpretation In order to minimise uncertainties in the interpretation of the hydraulic data the following recommendations are made: A consistent core classification of open fractures be made that could guide the identification of potential water-conducting fractures; Attempts be made to resolve the problems encountered with assigning some detected PFLs due to missing information relating them to features seen in the fracture database based on the core and image logs. In implementing the Hydro-DFN on a site-scale only localised fracture domains based on HZ20 are defined in the Geo-DFN. This means extrapolating the fracture domains far beyond the extent of HZ20. A more extensive description of the geological structural model is required for site modelling. Other information should be used in confirmatory testing of the developed Hydro-DFN based on some of the following data: hydraulic interference tests,

115 111 tunnel hydraulic tests, integration with palaeo-hydrogeology, and tracer/dilution tests. The core classification of open fractures has several values. Besides providing information about the nature of the fractures openness (apertures), which is of interest in the transport modelling, the Terzaghi corrected linear frequency of open fractures, P 10,open,corr, constitutes an estimate of the upper bound of the potential 3D intensity of flowing fractures. The difference flow logging measurements carried out with the Posiva Flow Log may be regarded as a means to determine the intensity of connected open fractures that have a transmissivity value greater that the lower measurement limit of the Posiva Flow Log, P 10,PFL,corr. In general terms, the transmissivity threshold of these data is of the order of 10 9 m 2 /s. The difference in intensity between P 10,open,corr and P 10,PFL,corr may be regarded as a measure of the intensity of fracture with transmissivities below the lower measurement limit of the Posiva Flow Log. However, not all open fractures are connected. The difference in fracture intensity between P 10,open,corr and P 10,PFL,corr can be split into three subgroups: isolated open fractures with transmissivities less than the lower measurement limit of the Posiva Flow Log, isolated open fractures with transmissivities greater than the lower measurement limit of the Posiva Flow Log connected open features, and connected open fractures with transmissivities less than the lower measurement limit of the Posiva Flow Log. The Hydro-DFN approach used in this report models the connectivity and specific inflow rates, Q/s) of open fractures in cored boreholes that have a transmissivity value greater than the practical lower measurement limit of the Posiva Flow Log. In Section 4.2.1, it was concluded that a fraction of the flowing fractures in the KR/KRB boreholes and the PH boreholes detected with the Posiva Flow Log were not used in the flow modelling because their geometrical or hydraulic properties were unknown for one reason or the other. The effect of this data discrimation was not evaluated in the work reported here, though. Such an analysis could be made if required but it would invoke some uncertainties, e.g. for those PFL data that lack positions it is difficult to determine the correct depth zone belonging, and for those data that lack orientation it is impossible to determine the Terzaghi corrected intensity. In general terms, however, it can be stated that the effect of a lower value of P 10,PFL,corr is that the sizes of the flowing DFN fractures become larger than for a higher value of P 10,PFL,corr. Finally, it is important at this point to recollect what is actually measured with the PFL tests. For each PFL transmissivity value identified, the change in flux (inflow) and head (drawdown) after several days of pumping relative to conditions prior to pumping are calculated. A transmissivity value is interpreted for the PFL-anomaly based on an assumed radius of influence of c. 19 m. The choice of 19m reflects that tests are performed over several days, and hence should represent an effective transmissivity of

116 112 the whole fracture intersected, and possibly adjoining parts of the network, but 19 m is otherwise arbitrary. Consequently, the interpreted values of transmissivity should not be viewed as necessarily the transmissivity of an individual fracture, or the transmissivity of the fracture local to the borehole intersect. They are more indicative of the effective transmissivity over a larger scale. This remark influences the way we use the PFL-f data in the Hydro-DFN modelling.

117 SITE-SCALE EQUIVALENT CONTINUUM POROUS MEDIUM (ECPM) BLOCK PROPERTIES 11.1 Objectives Equivalent block hydraulic conductivity tensors, K eff, and kinematic porosities, eff, based on an underlying Hydro-DFN model were calculated for a 50m block size using the statistics derived in the previous sections. The objective was to provide hydraulic properties from a single site-scale Hydro-DFN realisation in support of the site-scale ECPM modelling with FEFTRA Model set-up The model domain is similar to that used in the FEFTRA site-scale modelling /Löfman et al. 2009/. It is based on a rectangle in North and East axial directions that encompasses the FEFTRA model. The bottom of the model is at masl. The lineaments are not used. The domain was sub-divided into a total of blocks of side 50m, and for each equivalent hydraulic properties were computed in the same fashion as previously done on the repository scale, cf. Section 8. The only exception was that the guard zone method used in Section 8 was not applied on the site-scale model. The results shown here represent one realisation of a DFN model with two fracture domains, FDa and FDb (in Section 8 we studied one fracture domain at a time), a power-law size model (Case A) and a semi-correlated transmissivity model (SC). If there were no connected fractures generated inside a block then that block was assigned a zero hydraulic conductivity. These cases are excluded from the calculation of hydraulic conductivity and kinematic porosity statistics. The fraction of blocks that have at least some connected fractures is presented in the results as the percolation fraction Visualisations Figure 11-1 shows the surface of the site-scale model domain coloured by the upscaled conductivity values K xx (E-W). Figure 11-2 shows three vertical slices through the model domain coloured by the upscaled conductivity values K xx. Figure 11-3 through Figure 11-6 show four horizontal slice through the site-scale model domain again coloured by the upscaled conductivity values K xx. The slices were chosen to cut through depth zones 1-4 to demonstrate the decrease in hydraulic conductivity with depth. The contrast in the ECPM properties that is visible in some of the slices is due to the slight difference in the Hydro-DFN properties between fracture domains FDa (SE corner) and FDb (NW corner), cf. Table Effective hydraulic conductivity The upscaling methodology produces a directional hydraulic conductivity tensor, fracture kinematic porosity and other transport properties (such as the fracture surface

118 114 area per unit volume). In CONNECTFLOW a flux-based upscaling method is used that requires several flow calculations through a DFN model in different directions. To calculate the equivalent hydraulic conductivity for a block, the pressure and flow distribution in the fractures that have any part of their area within the block is calculated for a linear head gradient in each of the axial directions. Due to the variety of connections across the network, several flow-paths are possible, and may result in crossflows non-parallel to the head gradient. Cross-flows are a common characteristic of DFN models and can be approximated in an ECPM by an anisotropic hydraulic conductivity. In 3D, CONNECTFLOW uses six components to characterise the symmetric hydraulic conductivity tensor. Using the DFN flow simulations, the fluxes through each face of the block are calculated for each head gradient direction. The hydraulic conductivity tensor is then derived by a least-squares fit to these flux responses for the fixed head gradients. A detailed description of the upscaling method to calculate the ECPM hydraulic conductivity tensor is given in /Jackson et al. 2000/. Briefly, the method can be summarised by the following steps: Define a sub-block within a DFN model; Identify the fractures that are either completely inside or cut the block; Calculate the connections between these fractures and their connection to the faces of the block; Remove isolated fractures and isolated fracture clusters, and dead-end fractures if specified; Specify a linear head gradient parallel to each coordinate axis on all the faces of the block; Calculate the flow through the network and the flux through each face of the block for each axial head gradient; Fit a symmetric anisotropic hydraulic conductivity tensor that best fits (leastsquares) the flux response of the network; Fracture kinematic porosity is calculated as the sum (over all fractures that are connected on the scale of the block) of fracture area within the block multiplied by the transport aperture of the fracture. One important aspect of this approach is that the properties are calculated on a particular scale, that of the blocks, and that a connectivity analysis of the network is performed only on the scale of the block. Bulk flows across many blocks will depend on the correlation and variability of properties between blocks. By diagnonalising the resulting hydraulic conductivity tensor into the 3 principal components (or eigenvalues of the matrix), the effective hydraulic conductivity, K eff, was calculated as the geometric mean of these eigenvalues:

119 115 K eff = (K max K int K min ) 1/3 (11-1) 11.5 Effective kinematic porosity The effective kinematic porosity was calculated as the cumulative volume of the flowing pore space divided by the block volume. The contribution to the flowing pore space was calculated from the cubic law for the connected fractures: e h = (T / ( g)) 1/3 (11-2) e t = 4 e h (11-3) Figure View of the site-model domain showing the K xx component.

120 116 Figure Three vertical slices showing the variation of K xx with depth. Figure Horizontal slice at 25m depth. The conductivities shown represent K xx.

121 117 Figure Horizontal slice at 100m depth. The conductivities shown represent K xx. The contrast in the ECPM properties is due to the slight difference in the Hydro-DFN properties between fracture domains FDa (SE corner) and FDb (NW corner), cf. Table 5-4. Figure Horizontal slice at 275m depth. The conductivities shown represent K xx.

122 118 Figure Horizontal slice at 500m depth. The conductivities shown represent K xx Block property statistics The results from the site-scale upscaling are shown in Figure 11-7 through Figure and summarised in Table 11-1 and Table The statistics encompass: the 10, 25, 50, 75, 90 percentiles of K eff based on all cells whether K eff is zero or not; the mean and standard deviation of log(k eff ) for those values that have K eff >10 13 m/s (k eff = m 2 ); the percentage of cells that have K eff >10 13 m/s. The percentages of percolating blocks in Figure 11-7 compare well with the results for FDb in Table 8-2. Figure demonstrates the depth trend in hydraulic conductivity associated with the depth zones. Comparing Table 11-1 with Table 8-2 the mean effective hydraulic conductivities are higher for the site-scale model than in the block modelling for DZ2-4. There are three contributing factors to these differences: in the site-scale model, statistics are calculated by depth combining fracture domains FDa and FDb whereas they are just based on FDb in Table 8-2; in the site-scale model, long fractures can extend protrude from the depth zone above to raise the conductivity of some blocks within the lower depth zone; the use of a guard zone in the block-scale modelling in Section 8 (i.e. calculating flow through a larger domain) may have resulted in lower conductivities due to scale dependency of the network connectivity. The porosities in Table 11-2 are consistent with those in Table 8-3.

123 Hydraulic conductivity (m/s) 119 ECPM-50m K eff = (K max K int K min ) (1/3) 1E DZ1-4 1E DZ1 DZ2 DZ3 DZ % 100% 100% 87% 33% 1E+04 Available Conductive No. of elements Figure Fraction of percolation for the connected fractures by depth zone. ECPM-50m K eff = (K max K int K min ) (1/3) 1E-06 1E-07 1E-08 1E-09 DZ1 DZ2 DZ3 DZ4 1E-10 1E-11 Keff-10 Keff-25 Keff-50 Keff-75 Keff-90 Percentile Figure The 10, 25, 50, 75, 90 percentiles of K eff. by depth zone.

124 Maximum anisotropy ratio y (-) 120 ECPM-50m (K max / K min ) 1E+03 1E+02 DZ1 DZ2 DZ3 DZ4 1E+01 1E+00 K-max/K-min-10 K-max/K-min-25 K-max/K-min-50 K-max/K-min-75 K-max/K-min-90 Percentile Figure The 10, 25, 50, 75, 90 percentiles of the ratio of K max / K min.by depth zone. ECPM-50m K eff = (K max K int K min ) (1/3) DZ1 DZ2 DZ3 DZ m-log(keff) s-log(keff) Entity Figure The mean and standard deviation of log(k eff ) by depth zone.

125 Kinematic porosity ( ) 121 ECPM-50m 1E-04 1E-05 DZ1 DZ2 DZ3 DZ4 1E-06 Phi-10 Phi-25 Phi-50 Phi-75 Phi-75 Percentile Figure The 10, 25, 50, 75, 90 percentiles of eff by depth zone. ECPM-50m DZ1 DZ2 DZ3 DZ m-log(phi) s-log(phi) Entity Figure The mean and standard deviation of log( eff ) by depth zone.

126 122 Table Summary of upscaling results for site-scale 50 m K eff by depth zone (Note: mixed fracture domains). Model description Fracture size distribution T model Depth zone (masl) Parameter m of log(k eff ) [m/s] s of log(k eff ) [m/s] Power-law SC 0 to Power-law SC 50 to Power-law SC 150 to Power-law SC 400 to Fraction of percolatio n Table Summary of upscaling results fort repository-scale 50 m eff by depth zone (Note: mixed fracture domains). Model description Parameter Fracture size distribution T model Depth zone (masl) m of log( eff ) [ ] s of log( eff ) [ ] Power-law SC 0 to Power-law SC 50 to Power-law SC 150 to Power-law SC 400 to Fraction of percolation

127 SITE-SCALE FRESHWATER FLOW AND TRANSPORT 12.1 Objectives Particle tracking simulations were first carried out for a freshwater system (i.e. neglecting the effects of buoyancy due to variations in salinity). The objective was to provide information about the PA transport properties of the derived Hydro-DFN model on a site-scale in support of the Olkiluoto site descriptive model By PA transport properties we mean the distributions and moments of F [TL 1 ] and t [T], where F is the quotient between the flow wetted surface area and the flow rate and t is the advective travel time for non-sorbing tracers. Here, the integral values of these two entities are of key interest, i.e. their cumulative (or total) values from the release area to the exit positions Model set-up The model domain is an extension of the FEFTRA site-scale model, see section The transmissivity of the hydro zones (cf. Appendix D) and the array of release points were the same as in the FEFTRA site-scale modelling by /Löfman et al. 2009/. The boundary conditions were no-flow on the bottom surface and all vertical sides of the model domain and a specified residual pressure at the trace of fractures on the top boundary that was based on pressure values calculated in FEFTRA on the top surface of that model for present-day conditions. The FEFTRA model specifies head on the top surface equal to the measured watertable where it is available and elsewhere the watertable is assumed to be half of the topographical elevation above sealevel. Nearest neighbour interpolation was used to transfer the FEFTRA pressure in the CPM to the nodes on fracture traces in the DFN model. The fractures were generated according to the fracture size distribution parameters and fracture transmissivity parameters produced in the Hydro-DFN flow calibration stage of modelling. To make the model computationally tractable, the smallest fractures in the large fracture sets were set to 11.2 m radius throughout the domain, except around the repository smaller fractures were also generated, with a minimum size of 0.5 m radius. The model has no tunnels. At each release point a sphere of radius 2.5 m is searched for intersecting fractures connected to the flowing fracture network. The radius of 2.5 m was chosen to approximate the height of a canister deposition hole. If no connected fractures intersect the sphere surrounding the release point the particle is not released. One or ten particles were released at each release point. If there is more than one transport node within the 2.5 m radius around the release point, the choice of destination is weighted by the flux through the possible fractures.

128 124 In the transport calculations, the transport aperture, e t (L) of the fractures was deduced from the cubic law, i.e.: e 12 T 4 e 4 ( ) 0.04 T g 1/ 3 1/ 3 t h (12-1) where e h [L] is the hydraulic aperture and T [L 2 T 1 ] is the fracture transmissivity. The F quotient [TL 1 ] and the advective travel time t [T] of the fractures were calculated as: (12-2) (12-3) where W [L] is the fracture width, L [L] is the fracture length and Q [L 3 T 1 ] is the fracture water flow rate. The latter comes from the solution of the head field and can be written as: Q W v W T J (12-4) where v [L 2 T 1 ] is the water velocity per unit width and J [ ] is the hydraulic head gradient. From (12-1) and (12-2) it can be concluded that the relationship between the F quotient and the advective travel time t depends on the definition of the transport aperture. Rearranging (12-1), (12-2) and (12-3) and solving for the F quotient we get: (12-5) Equation (12-5) merely shows how the problem was formulated in the work reported here. In reality, the transport aperture, e t, is quite uncertain implying that knowing the advective travel time t does not necessarily imply that we also know the F quotient. In conclusion, it is of particular interest to study how the total value of the F quotient and the advective travel time t at the exit position of a pathway, F tot and t tot, relates to the geometrical and hydraulic properties of the initial fractures at the start positions, i.e. the fractures that connected to the canisters. In the calibration of the Hydro-DFN model described in the previous sections we used different relationships between fracture transmissivity T and fracture size r, see Table 6-2. Here, we adopted the semi-correlated model, i.e. the correlation between fracture transmissivity and fracture size is uncertain. In operation, the random deviate log(t) will create a randomness to Equation (12-5). To begin with, particle pathlines were calculated for one realisation of the model with one particle released at each start position (Case 1-1). In a second step, a single realisation with ten particles per start position was studied (Case 1-10), and in the third and final step, particle pathlines were calculated for ten realisations of the model with ten particles released at each start position (Case 10-10).

129 Example visualisations Case 1-1 Figure 12-1 and Figure 12-2 visualise a single realisation of the model, where the hydro zones and the DFN fractures are coloured by their head values. Figure 12-1 shows three vertical slices in the WE direction. Figure 12-2 shows a horizontal slice at 400 m depth through the centre of the model area where the repository is located. The repository area contains a total of canisters /Löfman et al. 2009/. Figure 12-2 shows that the greatest head values at 400m depth appear east of the repository area. Moreover, there is a crest of higher heads running across the repository area in the WE direction, which suggests a groundwater divide with lateral head gradients towards north and southwest. Out of a total canisters, 369 (~5 %) are connected in this particular DFN realisation. These start positions are shown as red dots in Figure Figure 12-4 shows that the exit positions are mainly to the north and to the southwest of the release area, indeed. Apparently, very few of the particles exit where the hydro zones outcrop. Figure 12-5 and Figure 12-6 visualise the pathways between the start positions and the exit positions. The two pictures show the importance of the sea as a boundary condition and that many particles flow in the stochastic DFN rather than in the hydro zones. However, Figure 12-6 shows the importance of hydro zones HZ21 and HZ099 for the pathway to the north, and though exit points do not correspond with the outcrop of HZ21, a large part of their path is in HZ21 until they find a short-cut to the top surface through large sub-vertical stochastic fractures. Figure 12-7 visualises the exit locations for the single realisation of the model with 369 start positions and one particle per start position shown in section Some exit locations cluster on linear features associated with large stochastic fractures. Figure 12-8 visualises the exit positions for ten realisations of the model, each with c start positions, depending on realisation, and ten particles per start position. Figure 12-7 and Figure 12-8 indicate that large stochastic features play a greater role for the positions of the exit positions than the hydro zones do. That is, there is no structure in the exit positions that is common to all realisations, which is what one would expect if the outcropping hydro zones governed the exit positions. Still, the overall spread in the exit positions is fairly concentrated. This implies that the position of the shoreline governs which of the stochastic short-cuts that comes into play.

130 126 Figure Three vertical slices through the site-scale model domain. The hydro zones and the DFN fractures are coloured by their head values. Figure Plan view of the centre of the model area where the repository is located at c. 400 m depth. The hydro zones and the DFN fractures are coloured by their head values.

131 127 Figure A horizontal slice at c. 400m depth through a single realisation of the model with one particle per start position. Red = start positions. The hydro zones and the DFN fractures are coloured by their head values. Figure A horizontal slice at 100m depth through a single realisation of the model with one particle per start position. Red = start positions. Pink = exit positions. The DFN fractures are coloured by their head values. The hydro zones (thicker lines) are coloured by transmissivity. The dashed circles are inserted to guide the eye to find the particles start positions (red circle) and exit positions (black circles). Apparently, very few of the released particles exit where the hydro zones outcrop.

132 128 Figure A horizontal slice at 100m depth through a single realisation of the model with one particle per start position. 369 start positions (red), pathways (blue) and exit positions (pink). The hydro zones (thicker lines) are coloured by transmissivity. Figure Cross sectional of the picture shown in Figure The exit positions are mainly to the north and to the southwest of the release area. The participating hydro zones are HZ21 and HZ099.

133 129 Figure Plan view of the exit positions for a single realisation of the model with 369 start positions and one particle per start position. Figure Plan view of the exit positions for ten realisations of the model with start positions depending on realisation and ten particles per start position. The exit positions of the ten realisations are shown in different colours. Some realisations occur very local.

134 Transport statistics Since the release area is large and hence samples a range of different groundwater flow pathways, the distributions of the F-quotient and advective travel time t for one realisation of the model with one particle per start position (Case 1-1) may not be so different from studying one realisation with ten particles per start position (Case 1-10) or studying ten realisations with ten particles per start position (Case 10-10). For the sake of clarity, however, we begin by showing some results of the F quotient and the advective travel time t for Case 1-1. Secondly, we show some additional results that can be deduced from Case 1-10 only, e.g. the variability in the F quotient depending on the route taken at a given start position intersected by several fractures. Finally, we end by showing some results for Case Case 1-1 a single realisation of the model with one particle per start position The figures in this sub-section display statistics for the realisation of the model shown in Section 12.3, i.e. out of a total of 6816 canister positions 369 (~ 5 %) are connected to the DFN. Figure 12-9 and Figure show the histograms of the (cumulative) F quotient and the advective travel time t. From these histograms, we make the observation that the distributions of F and t look somewhat bimodal. Figure suggests that there is a pronounced correlation in the transport properties between the total F quotient and the total advective travel time t. Figure reveals that there is also a correlation between the total F quotient and the initial water velocity at the start position, and Figure suggests that there is a more limited correlation between the total F quotient and the transmissivity values of the initial facture. The strong correlation between F and t follows from the relationship (12-5), which implies that the spread in F will be in proportion to the cube root of the spread in transmissivity. The range of transmissivities is about 6 orders of magnitude, and so the spread in F is less than 2 orders of magnitude fro a given travel time. Figure shows the contribution to the total F quotient as a function depth. The diagram is divided into eight 50m thick depth intervals and the distribution of the contribution at each depth interval is shown in terms of eleven percentiles. The largest contribution is gained at the repository depth. Above repository depth, the contribution of each 50 m depth interval falls gradually with step changes at the boundary between the depth zones, i.e masl, -150 masl and -50 masl. Hence, retention is primarily in the fracture network immediate to the repository. The analysis of transport properties in Section 9 are therefore expected to give a reasonable approximation of near-field retention, although the site-scale model allows the hydraulic gradient directions and magnitude to be quantified more realistically. The four pie charts shown in Figure visualise the relative contribution of the hydro zones and the three DFN fracture sets to the total F quotient for the chosen segments of the F quotient distribution shown in Figure Apparently, the hydro zones play a minor role and among the DFN fracture sets, apart from in the 0-30 percentiles. The sub-horizontal (SH) set dominates all parts of the F distribution. The same conclusion is drawn from Figure through Figure 12-18, which show three scatter plots.

135 Frequency Frequency % N = 1 p/st pos St pos = 369 log50 = 1.67 Median = 47 y % 80% 70% 60% 50% 40% 30% 20% 10% 0% log (t) [y] Figure Histogram of log(t) for a single realisation of the site model with one particle per start position % N = 1 p/st pos St pos = 369 log50 = 5.85 Median = 7E5 y/m % 80% 70% 60% 50% 40% 30% 20% 10% 0% log (F) [y/m] Figure Histogram of log(f) for a single realisation of the site model with one particle per start position.

136 F [y/m] F [y/m] 132 1E+12 1E+10 1E+08 1E+06 1E+04 1E+02 1E-01 1E+01 1E+03 1E+05 1E+07 t [y] Figure Scatter plot of total F versus total t for a single realisation of the site model with one particle per start position. 1E+12 1E+10 1E+08 1E+06 1E+04 1E+02 1E-10 1E-08 1E-06 1E-04 1E-02 1E+00 1E+02 v i [m 2 /y] Figure Scatter plot of total F versus the initial water velocity vi at the start position for a single realisation of the site model with one particle per start position.

137 Elevation (m) F [y/m] 133 1E+12 1E+10 1E+08 1E+06 1E+04 1E+02 1E-12 1E-10 1E-08 1E-06 1E-04 T i [m 2 /s] Figure Scatter plot of total F versus the initial fracture transmissivity T i at the start position for a single realisation of the site model with one particle per start position. Distribution of F for 369 particles per 50m depth interval E+00 1E+02 1E+04 1E+06 1E+08 1E+10 F-quotient (y/m) Figure Contribution to the total F quotient as a function depth for a single realisation of the model with one particle per start position. The diagram is divided into eight 50m thick depth intervals and the distribution of the contribution at each depth interval is shown in terms of eleven percentiles.

138 Total F (HZ + DFN) [y/m] 134 Interval 0-5% Mean F-quotient = 4.8E+03 y/m Interval 20-30% Mean F-quotient = 8.3E+04 y/m HZ DFN Set 1 - EW DFN Set 2 - NS DFN Set 3 - SH HZ DFN Set 1 - EW DFN Set 2 - NS DFN Set 3 - SH Interval 50-60% Mean F-quotient = 1.3E+06 y/m Interval 80-90% Mean F-quotient = 2.1E+08 y/m HZ DFN Set 1 - EW DFN Set 2 - NS DFN Set 3 - SH HZ DFN Set 1 - EW DFN Set 2 - NS DFN Set 3 - SH Figure Four pie charts that show the relative contribution of the hydro zones and the three DFN fracture sets to the total F quotient for four segments of the F quotient distribution shown in Figure E+08 1E+07 1E+06 F<1E3 y/m 1E+05 1E+04 F<1E4 y/m F<1E5 y/m F<1E6 y/m F<1E7 y/m F<1E8 y/m 1E+03 1E+02 1E+02 1E+03 1E+04 1E+05 1E+06 1E+07 1E+08 Sum of F in DFN [y/m] Figure Scatter plot of the total F vs. the contribution of the DFN realisation.

139 Total F (HZ + DFN) [y/m] Total F (HZ + DFN) [y/m] 135 1E+08 1E+07 1E+06 F<1E3 y/m 1E+05 1E+04 F<1E4 y/m F<1E5 y/m F<1E6 y/m F<1E7 y/m F<1E8 y/m 1E+03 1E+02 1E+02 1E+03 1E+04 1E+05 1E+06 1E+07 1E+08 Sum of F in HZ [y/m] Figure Scatter plot of the total F vs. the contribution of the hydro zones. 1E+08 1E+07 1E+06 F<1E3 y/m 1E+05 1E+04 F<1E4 y/m F<1E5 y/m F<1E6 y/m F<1E7 y/m F<1E8 y/m 1E+03 1E+02 1E+02 1E+03 1E+04 1E+05 1E+06 1E+07 1E+08 Sum of F in SH DFN [y/m] Figure Scatter plot of the total F vs. the contribution of the sub-horizontal fracture set. Figure through Figure show a handful of particles with abnormally low F- quotient, and so it is important to understand under scenarios these can arise. On inspection it was found that these are associated with a case where two long sub-

140 136 horizontal stochastic fractures intersect one another and extend down to the repository. Figure shows a visualisation of the pathways of ten start positions with very low F quotients in Case 1-1 showing the two extensive stochastic sub-horizontal fractures in different colours. The initial fracture is very close to all of the ten start positions. In fact this case only arose in this one amongst the 10 realisations, suggesting it is rare, but still possible in the derived Hydro-DFN model. Figure A visualisation of the pathways of ten start positions with very low F quotients in Case Case 1-10 a single realisation of the model with ten particles per start position The figures in this sub section display statistics for the single realisation of the model shown in Section In contrast to Case 1-1, however, the figures shown here represent ten particles per start position. In the model, it was possible to release 10 particles per start position at 351 of the 369 start positions studied in Case 1-1. Thus, at 18 start positions, the number of particles releases in the model varied between one and nine. Since the number of fractures at the different start positions varies, the probabilities of the ten random releases at a given start position depend on the relative strength of the flow rate of each pathway at that start position.

141 F [y/m] 137 Figure shows the total F quotient versus the fracture transmissivity at the start positions, T i, for a single realisation of the model with ten particles per start position. It is noted that low values of the total F quotients apparently can occur for a wide range of values of the initial fracture transmissivity. By the same token, Figure shows the total F quotient versus the fracture size at the start positions, r i, for a single realisation of the model with ten particles per start position. It is noted that low values of the total F quotients can occur regardless of the size of the initial fracture, presumably because a small fracture can connect directly into a large stochastic fracture or hydro zone. The vertical lines on the right side of this graph correspond to several particles starting in individual large stochastic fractures. Figure shows two histograms of the total advective travel time t. The histogram with blue frequency bars represents the 351 particles at the 351 start positions that have the lowest values of the total F quotient. Likewise, the histogram with purple frequency bars represents the 351 particles at the same start positions that have the highest values of the total F quotient. The histogram shown in Figure suggests that the geometric mean of the ratio of the total F quotients for these two types of particles is less than ten. The pie charts shown in Figure reveal that both types of particles spend the majority of their advective travel time outside the hydro zones and that the sub-horizontal fracture set dominates the advective travel times in the DFN realisation. Figure shows a scatter plot of the total F quotient versus the total advective travel time t for the 141 pathways that have F quotients less than 10 4 y/m. A closer examination at the simulation results shows that the 141 pathways can be associated with 26 start positions. The pie chart shown in Figure reveals that the 141 particles on the average spend 86 % of their total advective travel time t in the subhorizontal DFN fracture set. 1E+12 1E+11 1E+10 1E+09 1E+08 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1E-11 1E-10 1E-09 1E-08 1E-07 1E-06 1E-05 1E-04 T i [m 2 s] Figure Total F quotient versus the fracture transmissivity at the start positions, T i, for a single realisation of the model with ten particles per start position.

142 Frequency F [y/m] 138 1E+12 1E+11 1E+10 1E+09 1E+08 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 1E+00 1E-01 1E+00 1E+01 1E+02 1E+03 r i [m] Figure Total F quotient versus the fracture size at the start positions, r i, for a single realisation of the model with ten particles per start position log (t) [y] N = 10 p/st pos St pos = 351 t (Fmin) t (Fmax) Figure Two histograms showing the total advective travel time t for a single realisation with ten particles per start position. The histogram with blue frequency bars represents the 351 particles at the 351 start positions that have the lowest values of the total F quotient. Likewise, the histogram with purple frequency bars represents the 351 particles at the same start positions that have the highest values of the total F quotient.

143 Frequency N = 10 p/st pos St pos = 351 log 50 = 0.82 Median = % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% log (Fmax/Fmin) [ ] Figure Histogram of the ratio of the total F quotients for the two types of particles shown in Figure Average Time in structure for Fmin (single realisation, 10 p/start position) 9% 12% Average Time in structure for Fmax (single realisation, 10 p/start position) 4% 14% 59% HZ EW NS SH 20% N = 10 p/st pos St pos = 351 F = 665 5E10 y/m t 50 = 17 y F 50 = 1.6E5 y/m 62% 17% HZ EW NS SH N = 10 p/st pos St pos = 351 F = 1.4E3 5E10 y/m t 50 = 139 y F 50 = 2.5E6 y/m Figure Pie charts showing the average travel time in different types of structures for the two types of particles shown in Figure

144 F [y/m] 140 1E+04 1E+03 1E+02 1E-01 1E+00 1E+01 t [y] N = 141 p (3510 p) P = 26 (351) F = y/m t 50 = 0.51 y F 50 = 967 y/m Figure Scatter plot of the total F quotient versus the total advective travel time t for the 141 pathways in Case 1-10 that have F quotients less than 104 y/m. A closer examination at the simulation results shows that the 141 pathways can be associated with 26 start positions. Average Time in structure (single realisation, 10 p/start position) 4% 7% 3% HZ EW NS SH 86% N = 141 p (3510 p) P = 26 (351) F = y/m t 50 = 0.51 y F 50 = 967 y/m Figure The 141 particles shown in Figure spend on the average 86 % of their total advective travel time t in the sub-horizontal DFN fracture set.

145 Depth interval Case ten realisations of the model with ten particles per start position Figure shows the contribution to the total F quotient as a function depth for Case The diagram is divided into eight 50m thick depth intervals and the distribution of the contribution at each depth interval is shown in terms of box and whisker plots. The largest contribution is gained at the repository depth. Above repository depth, the contribution of each 50m depth interval is fairly constant up to c. 100m depth. Hence, there is little difference between this plot and the plot representing Case 1-1 shown in Figure Figure shows the exit locations coloured by total F quotient for Case The randomness in the spatial distribution of the F quotients is obvious, but again the distribution of exit location is concentrated to north or southwest, and only a few discharges occur on land associated with a small lake. Distribution of F for 3712 particles (realisation # 2) per 50m depth interval > -25m [-75, -25] [-125m, -75m] [-175m, -125m] [-225m, -175m] [-275m, -225m] [-325m, -275m] [-375m, -325m] < -375m log (F-quotient) [y/m] Figure Contribution to the total F quotient as a function depth for Case (ten realisations of the model with ten particles per start position). The diagram is divided into eight 50m thick depth intervals and the distribution of the contribution at each depth interval is shown in terms of a box and whisker plot. The red and blue fields represent 1 standard deviation of the ten means of log(f) at each depth interval. The whiskers represent the minimum and maximum values of log(f) of all values over the ten realisations at each depth interval.

146 Figure Spatial distribution of the F quotients of Case (ten realisations and ten particles per start position). 142

147 SITE-SCALE SALTWATER FLOW AND TRANSPORT 13.1 Objectives In order to scope the affects of salinity, variable-density flow calulcations were performed by importing a distribution of fluid density from a FEFTRA coupled groundwater flow and salt transport calculation and then calculating consistent distribution of residual pressure and flow in the site-scale DFN model. Particle tracking simulations were then carried out on the basis of the calculated flow-field taking account of buoyancy. The main objectives was to demonstrate that such calculations can be carried out for a large model domain using a DFN model of the fractured bedrock at Olkiluoto, and assess what types of differences might be seen relative to freshwater simulations Model set-up The model set-up mimics the freshwater system described in Section 1, except that the fluid density field was taken from a variable-density flow solution of an ECPM model studied with FEFTRA /Löfman et al. 2009/ (the base case calibrate model prediction of present-day conditions was used). This approximation prohibited a more quantitative comparison with the freshwater system studied in section 1 as the density field was not based on an ECPM model consistent with the DFN realisation. Hence, the analysis was limited to a comparison of the particle pathways by eye Results Figure 13-1 shows the imported density field from the ECPM model using FEFTRA. A nearest neighbour interpolation method was used to distribute fluid denity within the fracture system. The salinity increases rapidly at around -500masl. Figure 13-2 shows the distribution of pointwater head in the DFN model consistent with this density distribution such as to conserve mass flux, and this is compared with the equivalent pointwater heads without variable-density. Likewise, the distribution of pointwater heads at repository depth is compared with the freshwater head in Figure Figure 13-4 and Figure 13-5 show comparison of particle tracking for the saltwater and freshwater cases. The exit points shown in Figure 13-5 are similar and many of the clusters along linear features associated with large sub-vertical stochastic fractures are common. However, Figure 13-4 indicates that some of the long paths that go toward the southwest follow deeper and longer paths fro the saltwater case than the freshwater case. This is probably due to some particles starting below the saline interface in the southern part of the repository. Such long paths may be a consequence of using a density field not based on consistent hydraulic properties based on the DFN realisations. It is suggested that saltwater simulations should be repeated in the future using density fields calculated with an ECPM model that is based on the same underlying stochastic DFN realisation.

148 144 Figure Vertical cross-sections showing the imported density field from the ECPM model in FEFTRA.

149 145 Figure Vertical cross-sections through the model domain. Top: Pointwater heads. Bottom; Freshwater heads (cf. Figure 12-1).

150 146 Figure Plan view of the centre of the model area where the repository is located at c. 400 m depth. Top: Pointwater head values. Bottom: Freshwater head values (cf. Figure 12-2).

151 147 Figure Perspective view of particle pathways and exit positions. Top: Saltwater case. Bottom: Freshwater case.

152 148 Figure Plan view of the start positions (light blue) and the exit positions (red or purple) for a single realisation of the model with 369 start positions and one particle per start position. Top: saltwater case (red exit points). Bottom: Freshwater case (purple exit points, cf. Figure 12-7).

153 SUMMARY AND CONCLUSIONS OF PHASE II 14.1 General The work described in Sections relate to the Phase II of the 2008 hydrogeological discrete fracture network model of Olkiluoto. Phase II focuses on site-scale DFN modelling and comprises: Site-scale effective block hydraulic conductivity tensors, K eff, and kinematic porosities, eff, in support of the ECPM modelling with FEFTRA; Site-scale PA transport properties. These were deduced by means of freshwater flow and transport simulations through DFN realisations of the bedrock at Olkiluoto; A scoping study of variable-density flow and transport simulations through a DFN realisation of the bedrock at Olkiluoto. The analyses carried out provide input to the integration of site-scale hydrogeological properties with modelling of palaeo-hydrogeology using FEFTRA ECPM models of the Olkiluoto site as well as to subsequent safety performance assessment calculations Results from Phase II Upscaling Table 14-1 and Table 14-2 summarise the upscaling results of Phase I and Phase II for a 50 m block. It is noted that different formulae were used for the derivation of the effective kinematic porosity, cf. Sections 8 and 11. The hydraulic conductivity montonically decreases significantly with depth in both cases. There are a number of important differences in the how the hydraulic conductivities were calculated between Phase I and II: In Phase I, the hydraulic conductivity was calculated only for fracture domain FDb, whereas in Phase II both fracture domains, FDa and FDb, were considered and the statistics calculated only by depth zone. In Phase I, fracture network models were upscaled for each depth zone in isolation, i.e. not in a layered system, while in Phase II extensive higher transmissivity fractures could protrude from one depth zone down to the one below. Finally, in Phase I the guard zone technique in ConnectFlow was used where flow is calculated in a domain, 150 m, but only the flux through central 50 m block is used to calculate the equivalent hydraulic conductivity tensor. These differences in approach are the likely cause of the higher mean hydraulic conductivities in depth zones 2-4 in the site-scale model taken as a whole, which was not invoking the guard zone technique.. However, the fractions of blocks that percolate are similar, as is the standard deviations of log(k eff ), although tends to be higher in the site-scale modelling. These issues could be investigated further to better quantify the origin of the differences. The upscaled porosities are more consistent as they are based on simpler geometrical parameters.

154 150 Table Summary of upscaling results for 50m K eff of Phase I and Phase II. Model description Fracture size distribution T model Depth zone (masl) Parameter values of Phase I / Phase II m of log(k eff ) [m/s] s of log(k eff ) [m/s] Fraction of percolation Power-law SC 0 to / / / 1.00 Power-law SC 50 to / / / 1.00 Power-law SC 150 to / / / 0.87 Power-law SC 400 to / / / / 0.33 Table Summary of upscaling results for 50m eff of Phase I and Phase II. Model description Fracture size distribution T model Depth zone (masl) Parameter values of Phase I / Phase II m of log( eff ) [m/s] s of log( eff ) [m/s] Fraction of percolation Power-law SC 0 to / / / 1.00 Power-law SC 50 to / / / 1.00 Power-law SC 150 to / / / 0.87 Power-law SC 400 to / / / / Flow and transport The results shown in Section 1 indicate that hydro zone form major pathways for particles strting in the northern part of the repository area, but their ultimate exit positions on the top surface is controlled by extensive sub-vertical fractures. There is no systematic pattern in the exit positions that is common to all realisations, which is what one would expect if the outcropping hydro zones governed the exit positions. Still, the overall spread in the exit positions is fairly concentrated. This implies that the position of the shoreline governs which of the stochastic short-cuts that comes into play. The simulations show that the hydro zones play a very minor role in the total F quotient, whereas the sub-horizontal fracture set of the DFN model is the key contribution to F quotient in all transport simulations conducted in the work reported here. The assumption that large stochastic features have constant properties can cause a limited number of particle pathways with very rapid advective travel times and low values of the F quotient. Since the release area is large and hence samples a range of different groundwater flow pathways, the distributions of the F quotient and advective travel time t for one realisation of the model with one particle per start position (Case 1-1) is not very different from studying one realisation with ten particles per start position (Case 1-10) or studying ten realisations with ten particles per start position (Case 10-10).

155 Outstanding issues site modelling Coupled variable-density flow and transport simulations are very computational intensive to carry out using a DFN model. However, the work reported here demonstrates that such simulations are feasible, indeed, but that it is necessary to use a density field consistent with each specific Hydro-DFN realisation. One solution is to perform the coupled groundwater flow and chemistry simulations (palaeo-hydrogeology) using ECPM models that corresponding to particular realisations of an underlying DFN model, and then export the fluid density and pressure boundary conditions back to the DFN model at relevant times to performed detailed PA transport calculations. Releases at future times need to be considered as the hydraulic boundary conditions evolve. The property assignment of the hydro zone model in the work reported here is based on assumptions that are coherent with the corresponding modelling in FEFTRA. However this neglects the role of depth dependency and/or spatial heterogeneity that are likely to be realistic hydraulic characteristics of these features. The dependence of upscaled hydraulic properties on spatial scale needs to be studied further to quantify the uncertainty in groundwater fluxes depending on the choice of spatial resolution in ECPM models. The model domain reported here did not include any additional lineaments Future Hydro-DFN studies It would be useful to review if the methodology reported here could be refined with a view to integrate with hydrochemistry, which was never part of the study. Issues like orientation distributions, definition of sets, choice of depth zones, transmissivity contrasts between sets, combining different borehole orientations, appropriate scales for ECPM models, etc. could be assessed. It is suggested that a pre-study is carried out and that the results of the pre-study are documented in a memorandum and reported prior to the work with 2010 OHDFN.

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157 153 REFERENCES Ahokas H, Vaittinen T, Tammisto E, Nummela J, Modelling of hydro zones for the layout planning and numerical flow model in Working Report , Posiva Oy, Eurajoki. Buoro, A., Dahlbo, K., Wiren, L., Holmén, J., Hermanson, J., & Fox, A. (editor) Geological Discrete-Fracture Network Model (version 1) for the Olkiluoto Site, Finland. Working Report Posiva Oy, Eurajoki. Dershowitz W, Winberg A, Hermanson J, Byegård J, Tullborg E-L, Andersson P, Mazurek M, Äspö Task Force on modelling of groundwater flow and transport of solutes. Task 6c. A semi synthetic model of block scale conductive structures at the Äspö HRL. Äspö Hard Rock Laboratory, International Progress Report IPR-03-13, Svensk Kärnbränslehantering AB. Follin S, Levén J, Hartley L, Jackson P, Joyce S, Roberts D, Swift B, Hydrogeological characterisation and modelling of deformation zones and fracture domains, Forsmark model stage 2.2. SKB R-07-48, Svensk Kärnbränslehantering AB. Jackson CP, Hoch AR and Todman SJ, Self-consistency of a heterogeneous continuum porous medium representation of a fractured medium, Water Resources Research Vol 36. No 1, Pages Löfman J, Mészáros F, Keto V, Pitkänen P, Ahokas H, Modelling of groundwater flow and solute transport in Olkiluoto Update Working Report Posiva Oy. Olkiluoto. (to be published). Löfman J, Poteri A, Groundwater flow and transport simulations in support of RNT-2008 analysis, Posiva Working Report , Posiva Oy. Mattila J, Aaltonen I, Kemppainen K, Wikström L, Paananen M, Paulamäki S, Front K, Gehör S, Kärki A, Ahokas T, Geological model of the Olkiluoto site, Version 1.0, Working Report , Posiva Oy, Eurajoki. Paulamäki S, Paananen M, Gehör S, Kärki A, Front, K, Aaltonen I, Ahokas T, Kemppainen K, Mattila J, Wikström L, Geological model of the Olkiluoto Site, Version 0, Working Report , Posiva Oy, Eurajoki. Sokolnicki M, Rouhiainen P, Difference flow logging of boreholes KLX07A and KLX07B, Subarea Laxemar, Oskarshamn site investigation. SKB P , Svensk Kärnbränslehantering AB. Tammisto E, Palmén J, Ahokas H, Database for hydraulically conductive fractures. Olkiluoto, Finland: Posiva Oy. 109 p. Working report Vaittinen T, Ahokas H, Nummela J, Hydrogeological structure model of the Olkiluoto Site update in Posiva Working Report , Posiva Oy.

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159 155 APPENDIX A: PILOT HOLES PH1-7 Location Figure A-1 and Figure A-2 show the location of the seven pilot holes in the ONKALO access tunnel and the measured PFL transmissivities in these holes. The pilot holes are sub-horizontal compared to the surface boreholes, which are sub-vertical. Moreover, all of the seven pilot holes are located in fracture domain FDa. Figure A-1. Cross section showing the location of the seven pilot holes in the ONKALO access tunnel and the measured PFL transmissivities in these holes. Based on /Tammisto et al. 2009/.