Fuel and Waste Management Company (SKB). DFNE 2018 - 139. Discrimination of Discrete Fracture Network models using structural and flow data. Le Goc, R.
DFNE 2018 - 139
Discrimination of Discrete Fracture Network models using structural and flow data Le Goc, R. Itasca Consultants sas, France
Davy, P. GΓ©osciences Rennes, University of Rennes, CNRS, France
Darcel, C. Itasca Consultants sas, France
Selroos, J.-O. Svensk KΓ€rnbrΓ€nslehantering AB EXTENDED ABSTRACT
Fractures are key elements governing permeability and flow paths in crystalline rocks and sedimentary layers with low matrix porosity. The ability to predict those properties, crucial for some industrial applications such as risk assessment of sub-surface nuclear waste disposal, strongly relies on our ability to properly describe the fracture network characteristics. A usual method is to define 3D Discrete Fracture Network (DFN) models to represent the geological environment. This consists in combining various data on fracture geometrical properties (density, size and orientation distributions) and on hydraulic data (borehole flow logs), to produce 3D statistical distributions and upscaling functions of both fracture geometrical and transmissivity distributions. The hydraulic data are mainly used to determine the fracture transmissivity distribution and to calibrate it to direct observations. However, this approach is not univocal and various DFN models can match the observations. We suggest investigating further the potential information contained in borehole flow logs by considering two main flow indicators [1] - the equivalent hydraulic conductivity πΎππ and the flow channeling indicator ππ β and by focusing especially on their scaling and variability properties. These two properties were first used to analyze the 3D flow structure of Hydro DFN models (see below) in permeameter-like conditions [2] and are now adapted to more
realistic site conditions and borehole flow logging, as 1D-like borehole flow indicators [3]: 1
πΎππ (π) = βπππΉπΏ,π π
(1)
πΎππ (π) is the equivalent hydraulic conductivity (or equivalent permeability) over a borehole section of length π, defined from the contribution of all the water inflows in the section, interpreted as equivalent transmissivities πππΉπΏ,π . The subscript ππΉπΏ refers to the Posiva Flow Logβs standard flow measures [4]. The second indicator, ππ is: ππ (π) =
1 (βπππΉπΏ,π ) π
2
(2)
β π2ππΉπΏ,π
where π is the borehole section length and πππΉπΏ,π the measured flowrates in the section. Thus defined, ππ can be viewed as the inverse of the statistical distance between major flow paths. In this paper, we first study the scaling structure resulting from in-situ flow tests and next simulate similar flow test configurations in DFN models to analyze finally the modeled flow structure adequacy between models and data. The test case is the Forsmark site in Sweden, currently investigated by the Swedish Nuclear Fuel and Waste Management Company (SKB).
Flow logging techniques have been evaluated for a long time by SKB. PFL (Posiva Flow Log) testing is currently the SKB standard to characterize the rock mass hydro response at depth. The PFL is a single hole differential flow logging method [5, 6] were pumping sequences at imposed head (typically around Ξβ = 10 m) are performed over 4 to 7 days of pumping to ensure steady-state conditions. Flow rates πππΉπΏ are measured within 1 to 5 m sections surrounded by packers. In addition, overlapping measures offset by 10 cm sections allow the determination of the flow rate with a final resolution of 10 cm. The ratio between the flowrates (πππΉπΏ ) and the head drawdown are interpreted as equivalent transmissivities of individual flowing fractures (πππΉπΏ ) [5, 6]. At Forsmark, twelve deep boreholes have been characterized with PFL testing. The resulting distribution of transmissivities (πππΉπΏ ) at borehole KFM02A is plotted in Figure 1. The distribution of inflows is highly variable in space and the resulting transmissivities spread over four orders of magnitude. It is also noted that most of the inflows are located at depths above 600 m.
Figure 1: Distribution of πππΉπΏ transmissivities at borehole KFM02A in Forsmark.
For all the available data (twelve boreholes) a scale analysis of the flowing sections is performed, as illustrated in Figure 2 for the borehole sections located in the FFM01 Fracture Domain of the Forsmark site [7]. πΎπΊ stands for the geometric mean of πΎππ in the considered section. Two distinct regimes clearly occur. First a decrease of πΎπΊ (πΏ) and,
beyond a certain scale, an increase of πΎπΊ (πΏ). The initial decrease with size is related to the spatial distribution along the borehole of the observed water inflows and to the gaps distribution between locally flowing sections. The final increase of πΎπΊ (πΏ) is related to the nature of the underlying distribution of πππΉπΏ values.
Figure 2: Evolution with borehole section length L of the equivalent permeability geometrical mean πΎπΊ (πΏ) for boreholes sections located in the Forsmark FFM01 fracture domain.
Similar flow testing conditions (i.e. PFL logs) are then applied to a set of candidate DFN models - originally defined from geometrical core logging data of the FFM01 unit [2] and from the UFM modeling framework [8-10]. The simulation setup (dimensions and number of simulations) is adapted to the significant variability expected from the flow data analyses. The resulting average flow structures are compared with the data (Figure 3). The requirements of the DFN model to perform realistic flow tests encompass three main steps: the definition of i) the geometrical DFN (GEO DFN), ii) the proportion of sealed fractures in the system (roughly 75% of the DFN surface is sealed with a null transmissivity) and its spatial distribution and iii) the non-null transmissivity distributions of the remaining fractures. The DFN obtained after step 3 is named the HydroDFN. Some of these elements can be constrained by data and some are assumed. We show in Figure 3 the resulting simulated mean flow structure of the DFN model originally defined in the UFM modeling framework and calibrated only to core log geological data (observed core scale fracture density, fracture sets distribution, proportion of total sealed surface). With this model it is also assumed that the distribution of sealed surfaces
is concentrated in smaller fractures. In addition, the transmissivities are not calibrated to observations but defined from the relation π(ππ , ππ ) to the fracture size ππ and to the normal stress (ππ ) acting on the fracture, with: ππ (3) π(ππ , ππ ) = ππ β
ππ₯π (β ) ππ In these conditions we observe a very good correspondence between the DFN and the data scaling properties.
This work is funded by Svensk KΓ€rnbrΓ€nslehantering AB, the Swedish Nuclear Fuel and Waste Management Company.
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2.
a) G normalized
100
3.
10-1 lc-open, T=f(s,lf)
4.
KFM08A - 200-400 m
10-2
100
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scale
102
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b) 100
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P10q
10-1 10-2 lc-open , T=f(s,lf)
10
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KFM08A - 200-400 m 1/Ls
100
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Figure 3: Core log flow indicators scaling properties a) normalized mean equivalent geometrical permeability πΎππ (πΏ) and b) channeling indicator ππ (πΏ). The DFN model is plotted in red and the reference data are plotted in grey.
The purpose of the study summarized here is to show how well-defined flow indicators, including the critical scaling aspects, can be used to better characterize the rock mass flow structure and provide indicators adapted to discriminating between models.
ACKNOWLEDGMENTS
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