Assessing Geological Controls on Fracture Orientation and Intensity For Discrete Fracture Network ModelingAaron Fox1, Paul La Pointe1, Jan Hermanson2, and Raymond Munier3

1Golder Associates, Inc., Redmond, WA, USA; 2Golder Associates AB, Stockholm, Sweden; 3Svensk Kärnbränslehantering AB (SKB), Stockholm, Sweden

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KLX02 Cumulative Fracture Intensity Plot

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A Domain kt = 1.76

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Discrete fracture networks (DFNs) are an ideal way to characterize systems of foliations, joints, and faults in crystalline rock. The DFN allows for the representation of structural data in terms of statistical distributions, which can then be used for evaluation, prediction, or as input to other models. Golder Associates, through the FracMan software suite, has been successfully completing DFN projects for nuclear, oil and gas, and civil engineering projects for more than 20 years.

This poster describes the results of DFN model development in support of the Swedish Nuclear Fuel and Waste Agency (SKB), which is developing a high-level nuclear waste repository for the country’s spent reactor fuel.

SKB is using the DFN approach, both to guide site characterization efforts and to develop performance assessment models for repository licensing, for three candidate sites in Sweden; the Simpevarp and Laxemar sites within the province of Småland, and the Forsmark site in the province of Uppland. Both proposed sites are underlain by Proterozoic-age igneous and metamorphic rocks of the Fenno-Scandian Shield. Predominant lithologies at the proposed sites are granites, dioritoids, and gabbroids of the Transscandinavian Igneous Belt (~ 1800 Ma).

Mapped deformation zones and rock domains for Laxemar (above) and Simpevarp (below) model sub-areas

The first step in building the DFN was to group joints, faults, and deformation zones into local sets and to assess whether any regional patterns were visible, and what affect lithology had on joint / fault orientations.

Fracture orientation data was obtained from several sources, including:• Borehole core and image logs• Detailed outcrop trace and scan line maps• Geophysical and air photo lineament maps

Fracture set orientation divisions were based on joint orientations in outcrop and the trends of regional structures. Three consistent fracture sets were noted across the entire model region, with several outcrops possessing some less-intense sub-vertical and sub-horizontal local fracture sets.

DFN simulation used to constrain model volumetric fracture intensity (P32)

Deformation zones (faults) with the Laxemar 1.2 model domain

Detailed geologic and structural map of Outcrop ASM000209, Laxemar model sub-area

Moving average (5m window) plot of 1m binned fracture intensity data, borehole KLX02, Laxemar sub-area.

In the Laxemar DFN model, volumetric fracture intensity (P32; the amount of fracture surface area per unit rock volume) is the fundamental measurement of intensity. P32 is a useful measurement of fracture intensity in a stochastic DFN model, as it is independent of fracture orientation, and is generally scale-independent (if the spatial distribution of fracturing is uncorrelated; i.e. not fractal)

P32 values cannot be measured in the field; they are established by first assessing any structural or geologic controls on intensity, and then by developing conversion factors from measured intensity metrics (P10/P21) through Monte Carlo simulation

Borehole image log data was also used to determine the reasonableness of the fracture set divisions established from outcrop trace maps. As an aggregate, the borehole data generally agreed with the outcrop fracturing. In general, overall set orientations were NOT constrained by lithology but by spatial location.

However, we were surprised by the variations in fracture orientation and intensity with depth across the Laxemar region.

We would like to thank the following organizations and individuals for their support, both in this project and in the preparation of this poster

• SKB, for the opportunity to work on a challenging and cutting-edge project.

• The Japan Nuclear Cycle Corporation (JNC), whose support of the FracMan software suite over many years is greatly appreciated.

http://fracman.golder.com or http://www.skb.se/

Note: Detailed references for information presented in this poster are available upon request.

Fracture set orientations were quantified through spherical probability distributions, which describe a fracture set in terms of its mean pole vector (� , �) and a dispersion factor (�) around the mean pole. The Laxemar DFN models utilize a Univariate Fisher distribution, with set assignments based on a hard-sector division algorithm implemented in the FracMan/ISIS module (Golder Associates)

Polar stereoplots, Fisher contours, and rose diagram for outcrop ASM000209 fractures

Kernel and raster point density plot of fracture pole directions as a function of depth, borehole KSH02, Simpevarp model sub-area

A combination of local-scale (1-10 meter) detailed outcrop map data and regional-scale (1000 m +) deformation zone traces were used to develop a size model for the Laxemar DFN models. Though a variety of probability distributions for scalar data were tested for use, analysis suggested that a power law distribution, of the form:

worked the best to recreate trace lengths observed in outcrop from the three identified regional fracture sets. The fundamental assumption behind the use of a power-law scaling relationship, however, is that fractures in outcrop represent part of a ‘tectonic continuum’, with the regional scale deformation zones at the opposite end. Set members at all scales (outcrop fractures, faults, and deformation zones) will have similar orientations and intensities. This working hypothesis is currently being tested through a series of medium scale (1000 – 3000m) ground geophysical surveys conducted earlier this summer but not yet analyzed.

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Area-normalized trace length frequency plot for Regional Set S_A structures, assuming Euclidian scaling, in the Simpevarp sub-area

The power-law size analysis involves determining scaling factors for a distribution of trace lengths measured in outcrop (fractures and deformation zones). The trace length scaling parameters (kt, the trace length dimension, and Xot, the distribution minimum length) are calculated by creating an area-normalized trace length frequency plot for each regional fracture set within each rock domain (right). The area normalization was carried out for

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The DFN model is used to quantify and test assumptions regarding the orientations, sizes, spatial distribution, intensity, and deformation history of rock structures at multiple scales. The end product is a statistical description of geologic structures that can then be directly used for tunnel stability calculations, rippability, heat flow analysis, rock block / key wedge analysis, and contaminant transport simulation.

The DFN models can also be upscaled to equivalent porous media (EPM) blocks for use in regional groundwater or petroleum flow and transport codes such as MODFLOW or ECLIPSE®.In both the Laxemar and the Forsmark

projects, the DFN, when combined with a regional structural and a lithological domain model, serves as a combined geological framework for use in all downstream site-characterization and performance-assessment modeling.

The DFN model accepts and provides feedback to both the rock domain and the tectonic models, and helps to guide future studies by identifying areas of interest, assessing data gaps, and, to an extent, helping to quantify uncertainty for systems-scale models.

Flowchart illustrating the role of the DFN in the repository design and permitting process

These variations, which could not be completely attributed to measured physical parameters such as lithology or degree of alteration are still under study.

The question of structure size in joints and faults is challenging. Borehole data, which is quite useful for addressing spatial variability in structure orientations and intensities, is far less useful for constraining fracture sizes. Detailed outcrop trace maps, along with structures picked from 3D seismic surveys, are by far the most useful tools in determining size distributions for DFN modeling.

However, even trace maps have limitations; the question of trace length censoring as a function of outcrop size, over-estimation of the percentage of smaller fractures, and the difficulty of measuring the size of subhorizontally-dipping structures are all issues.

both Euclidean and fractal intensity scaling assumptions. Once the cumulative number distribution is established, a formal probability distribution for fracture radii can be calculated for each fracture set in each geologic domain for any specified minimum and maximum fracture size (radius).

Cumulative fracture intensity (CFI) plot for all fracture data (not binned), Borehole KLX02, Laxemar sub-area.

During the development of the Laxemar DFN model, the intensity of fracturing was the parameter for which the most complete and detailed data set existed. Even given the large data set, there was significant uncertainty in the spatial variability of calculated intensity models and the geological controls that underlay them.

Fracture intensity measurements took one of two forms:

• P10: the number of fractures per unit length, measured as intersections along a sample line.

• P21: the amount of trace length per unit area, calculated by measuring all the fracture traces exposed in an outcrop and dividing by the outcrop area.

Moving-average fracture intensity, cumulative fracture intensity, and nonparametric statistical tests (Eta) were used to assess the impact of geological factors on fracture intensity.

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Note: triangles indicate values outside of deformation zones; circles indicate values within deformation zones.

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Note: triangles indicate values outside of deformation zones; circles indicate values within deformation zones.

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Note: triangles indicate values outside of deformation zones; circles indicate values within deformation zones.

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Quartz monzonite to monzodiorite, equigranular toweakly porphyriticFine-grained dioritoid (Metavolcanite, volcanite)

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Quartz monzonite to monzodiorite, equigranular toweakly porphyritic

Note: triangles indicate values outside of deformation zones; circles indicate values within deformation zones.

The process of developing a discrete-fracture network model for the Laxemar area sites in support of repository licensing efforts directly led to several insights into the relationships between rock fracturing and geologic conditions:

1) Near-surface stress relief fracturing is generally not observed in the model region; fracture intensity generally increases with depth.

Cross-plot of open fracture P10 versus sealed fracture P10. Arrows show the changes in intensity that occur for fractures ‘inside’ regional deformation zones.

2) Lithology appears to have little effect on the orientations of fractures at the outcrop/borehole scale or at the regional (deformation zones) scale within the Laxemar model domain.

3) Fracture set orientations appear to be highly variable with depth; this was an unexpected result that is currently being investigated further.

4) The higher the degree of rock alteration/weathering, the greater the fracture intensity and the higher the ratio of open (transmissive) to sealed fractures. This effect is especially prominent within mapped regional deformation zones.

5) Overall variations in open and sealed fracture intensity outside of deformation zones is not well understood. Our analyses suggest that only 10% - 20% of the total variation in fracture intensity can be explained by lithology and degree of alteration.

6) The critical uncertainty in the model is whether there is a continuum of fracture sizes from ten’s of meters to kilometers, or whether there are significant “gaps” in fracture sizes. This uncertainty is currently being investigated through geophysical surveys.