Cho Rley Paper

�Slope Stability Santiago Chile, November 2009

Hydrogeological Characterization and Conceptual Discrete Fracture Network (DFN) Modelling of Slope Pressures within the NW wall, Diavik Diamond Mine, NWT

The Diavik mine is located in the Canadian Shield within a zone of continuous permafrost; however, within the A154 deposit two kimberlite pipes are being mined by open pit and underground methods in unfrozen ground within the confines of a diked portion of what was formerly Lac de Gras lake bottom. The NW wall of the open pit is a critical area for slope stability due to observed water pressure heads that are above overlying bench elevations. Comprehensive drilling, testing, instrumentation and monitoring of the NW wall has revealed a complex fractured rock system that is strongly influenced by the presence of a nearby lake, the heterogeneous nature of the fracture sets, sparsely distributed and widely spaced enhanced permeability fractures and an apparent low permeable veneer of rock on the slope face. To support continuum modelling of groundwater flow and head distribution in the pit slopes (i.e., equivalent porous media or hybrid approaches), a small conceptual study was carried out using a Discrete Fracture Network (DFN) representation of the flow system. This model was built from available borehole televiewer, flow logging and hydrogeologic testing data that resulted in a description of the rock mass as a heterogeneous network of fractures of varying orientation, size, aperture and transmissivity consistent with those observed within the pit.Using relatively simple boundary conditions, steady state flow simulations through the DFN model were undertaken in order to derive a distribution of head within the slope. Simulated piezometers placed within the model were found to reproduce many of the observed complex responses, namely: no conductive structures intersected, boreholes showing nominally hydrostatic responses or boreholes showing considerable apparent over pressure. By progressively increasing the geological reality of the DFN simulations, the major controls and influences on this structurally controlled flow field could be evaluated.Key learnings from this study has been that, particularly in areas where a large body of water is nearby, comprehensive data collection at various scales are required to provide a good understanding of the hydrogeologic conditions in support of slope stability assessment. In addition, DFN modelling is a very useful tool in helping to understand data in ways that are hard to reproduce with conventional continuum modelling. This was particularly important with respect to the high head observations.

Abstract

Don Chorley, Steve Rogers, Marc

Pesendorfer, Willy Zawadzki

Golder Associates Ltd

Sarah Greer

Diavik Diamond Mine Inc,

INTRODUCTION

The Diavik Diamond Mine is located on a 20-sq-km island in Lac de Gras, 300 kilometers northeast of Yellowknife, Northwest Territories of Canada (Figure 1) and is an unincorporated joint venture between DDMI (60%) and Harry Winston Diamond Limited Partnership (40%). Both companies are headquartered in Yellowknife, Canada. DDMI is a wholly owned subsidiary of Rio Tinto plc of London, England, and Harry Winston Diamond Limited Partnership is a subsidiary of Harry Winston Diamond Corporation of Toronto, Canada. DDMI is the operator of the project.

� Santiago Chile, November 2009 Slope Stability

Figure 1 - Site Location.

Presently DDMI is mining the A154 South and A154 North kimberlite pipes through the A154 open pit (Figure 2). The mine is located in the Canadian Shield within the region of continuous permafrost; however, mining operations are taking place in unfrozen ground (talik) within the confines of a diked portion of what was formerly Lac de Gras lake bottom. The kimberlite pipes, A154N and A154S, are hosted within a strong (>100 MPa), moderately fractured rock mass comprised of granite with smaller proportions of pegmatite and metasedimentary rafts in the granite intrusives. The A154S pipe is centrally located at the bottom of the pit while the A154N pipe is exposed over several of the upper benches in the northeast wall of the pit.

Lac de Gras

Figure 2 - Conceptual representation of the mine workings at peak of mining disturbance.

The granite forming the pit slope has been subject to two pipe intrusions and is affected by the presence of Dewey’s Fault Zone, a zone of minor faulting with enhanced permeability. This regional feature trends in a NE-SW direction through the kimberlite pipes and extends northwards beneath the A154 dike and under Lac de Gras, and to the south onto the island.

The location of the Northwest wall (NW wall) is presented on a plan of the ultimate A154 open pit in Figure 3. Presently the Lac de Gras is located behind the dikes on the north and east sides of the pit. The current pit bottom is at El. 9150 m rl (crest at El. ~9400 m rl) with an ultimate pit bottom elevation of 9130 m rl. in late 2009. Underground mining in the A154 pipes and beneath the A154 pit is planned to begin in February 2010. In addition, open pit mining of a third ore body called the A418 kimberlite pipe (Figure 2) began early 2008 with underground mining to begin June 2011.


Figure 3 - Ultimate Configuration of A154 Pit and location of Northwest wall.

The NW wall was identified early in the mining of the A154 pipes as an area where additional slope stability modelling was required as it represents the steepest wall on the lake side of the open pit. Initial geotechnical modelling of the NW wall indicated an acceptable factor of safety for the pit slopes above mine elevation 9280 m; however, below 9280 m elevation the factor of safety was found to approach the minimum acceptable value of 1.3 with the water pressures predicted by the calibrated hydrogeologic models. In light of the sensitivity of the stability to groundwater pressures (particularly for shallow, bench-scale movements) and the uncertainties inherent in predictions of these pressures, including potential effects of pit wall freezing, it was considered prudent to further investigate groundwater conditions and, if necessary, to design and implement a depressurization for the NW wall.

HYDROGEOLOGICAL INVESTIGATIONS

Comprehensive hydrogeological/geotechnical investigations have been undertaken in the NW wall consisting of core logging, hydrogeologic testing using pneumatic packers and downhole surveys, installation of nested grouted-in transducers, pumping tests, and installation of dewatering boreholes and drainholes. Borehole diameter and depth were limited by drilling rig availability and a remote site with access only by air or by an ice road for about 2 months in the winter.

Hydrogeologic Testing and Downhole SurveysHydrogeologic testing with pneumatic packers and downhole surveys were conducted in eight of the boreholes drilled in the NW wall

(Figure 4). Two types of hydrogeologic tests were conducted: scan tests using overlapping double packers at approximately 30 m packer intervals and detailed tests performed over longer time intervals (6 to 16 hours versus 1 hour for scan tests) to investigate a larger radius of influence and hydraulic connectivity. The downhole surveys included fluid temperature/resistivity, caliper and optical and acoustic televiewers. Collectively, this suite of four logs, together with hydrogeologic test results, help identify features or zones associated with relatively high hydraulic conductivity.

Figure 4 - Plan view showing locations of packer-tested boreholes.


Additionally, a dynamic fluid logging program was carried out to improve identification and characterization of flowing features intersecting the holes. Relatively low-flow features were enhanced by perturbing the groundwater flow system and logging fluid temperature/resistivity and relative flow rates using a dynamic impellor flowmeter. Figure 5 presents an example of a composite log for one of the boreholes drilled in the NW wall.

Figure 5 - Example composite borehole log.

Core LoggingCore logging was undertaken on four boreholes drilled in the NW wall. The boreholes chosen to be logged were based on the following

criteria: Distance from existing core log data and preliminary results of the downhole surveys and hydrogeological testing. Point load tests were performed on core at approximately 10 m intervals. The optical and acoustic televiewer images were used to orient and assign depths to structures within the core. Geotechnical parameters were collected to allow the calculation of rock mass quality using the RMR classification system.

Nested TransducersA total of 26 transducers have been installed in the NW wall. The locations of the transducers are shown in Figure 6 and a cross

section through one of the piezometer sections is presented in Figure 7.

Figure 6 - Plan view showing locations of grouted-in vibrating wire transducers.

Also shown on Figure 7 are the pressure heads measured in each of the transducers for January 2007, shortly after they were installed. The bottom of the pit at that time was at 9250 m elevation. There are four similar sections (with similar piezometer port locations) through the NW wall that displayed similar elevated pressure heads.


Figure 7 - Pressure heads in Cross–Section through Piezometer A154-MS-09.

Dewatering WellsFour vertical dewatering boreholes were drilled from the 9280 m bench. These boreholes were abandoned due to low yields and small

radius of influence. A total of ten (10) inclined (-70 degrees) dewatering boreholes were drilled to a maximum depth of 180 m from the 9280 m bench. All of these boreholes encountered artesian heads of 40 m or greater above the drill collar at depths between 90 m and 170 m. Driller’s logs indicated that the inflows generally occurred at 100 m depth (~9190 m elev.), 140 m depth (~9150 m elev.) and 170 m (~9120 m elev.).

Small diameter pumps were installed in nine of these wells; currently the total pumping rate from all these wells is approximately 400 m3/day. Analyses of a pumping test conducted in one of these boreholes has indicated a bulk transmissivity of approximately 1 x 10-4 m2/sec; a fracture system that is not uniform with some nearby transducers showing less drawdown than ones at a greater distance and a system that is highly influenced by the strong constant source of water from the nearby lake.

DrainholesA large number of drainholes with inclinations of generally less than 50 degrees were drilled in the NW wall using DDMI’s L8 drilling rig.

These boreholes were approximately 150 mm (6-inch) in diameter but were generally limited to depths of 100 m or less. Of the 40 plus drainholes installed only two exhibited groundwater flow rates greater than 7.2 m3/day (5 litres per minute). In addition, only one of these drainholes produced a response in the transducer ports in the NW wall.

On-going Monitoring of Pressure HeadsThe piezometer ports have been monitored continuously on an approximate 30 minute basis since their installation. Figure 8 presents

the inferred equipotentials for November 2007, before dewatering wells were installed (the pit bottom was at 9250 m elevation) and for June 2009 when all dewatering wells were installed and operating.

Although installation of the dewatering wells has had an effect on lowering the water pressures in the NW wall, a large portion of this drawdown is attributed to natural drainage into the pit.


Figure 8 - Inferred Equipotentials for 2007 and 2009.

One observation made during the monitoring of the pressures heads is the effect of blasting on these heads with the relative effects depending on the location and type of the blast. Figure 9 presents the transducers response to blasting in piezometer A154MS08D. Blasting generally results in a rapid rise in water levels following by a much slower recovery of the water levels.

Figure 9 -Piezometer response to blasting.


CHARACTERIzATION Of HYDROGEOLOGIC REGImE

The understanding of the hydrogeological regime in the NW wall has been developed through several lines of evidence.

The downhole surveys and core logging have indicated that the rock mass in the NW wall is dominated by two main conductive fracture sets, one set dipping in a north direction between 10 to 20 degrees and one set dipping northwest at 10 to 20 degrees; although a few steep structures (dip angle > 75 degrees) exit. The orientation of the conductive and possible conductive fracture sets matches the orientations of the non-conductive features. Observed locations along the borehole of increases in artesian flows during drilling provide evidence that the major, shallow oriented permeable features are located on average approximately 30 metres apart vertically.

The hydrogeologic testing and downhole surveys suggest a heterogeneous fractured rock mass with generally low hydraulic conductivity and connectivity (K of < 1 x 10-8 m/s), but with preferential flow along highly permeable structures (K of >1 x 10-6 m/s) that are widely spaced.

The pattern of equipotentials lines in the NW wall indicate the presence of high heads below the pit bottom that likely results from the presence of relatively highly permeable sub-horizontal features (with similar orientations to the sub-horizontal fracture sets identified during the core logging and downhole surveys) of regional significance that are likely hydraulically well connected to Lac de Gras through as series of permeable vertical structure. The response during the pumping test confirmed a substantial influence of the constant head formed by the lake. The high hydraulic gradient near the pit walls and pit bottom, as well as evidence from the drilling of the 54 metre long drainholes, indicates a low hydraulic conductivity near the pit wall, it appears that the highly permeable features terminate at 50 m or greater from the pit walls. The cause of this apparent reduction in hydraulic conductivity is unknown at this time but may be attributed to pit wall freezing, clamping of structures near the pit walls, etc.

DISCRETE fRACTURE NETwORk mODELLING

Basic DFN Model DevelopmentAs the previous sections have described, the Diavik rock mass comprises a complex fracture system that imposes considerable

heterogeneity on the groundwater system. In particular, the observation that permeable fracture connectivity is highly variable means that continuum modelling techniques will satisfactorily addresses regional and mine scale groundwater issues but will have difficulty explaining more local scale properties. In order to help understand some of these more local scale problems, conceptual DFN models of the Diavik rock mass were developed with the aim of helping to understand the distribution of conductive structures and also unusual observations of head in slope monitoring boreholes.

A number of properties need to be defined in order to build a DFN model, namely conductive fracture intensity, fracture orientation, fracture size. In order to simulate flow through the fracture network additionally fracture transmissivity, storativity and aperture also need also to be defined. A brief description of their definition follows in this section.

Typically fracture frequency (known as P10, units m-1) as measured in boreholes is not a preferred modeling input as it is a directionally biased sample. Instead P10 values are converted to a volumetric measure of intensity, known as P32 (see Rogers et al. 2009). This a directionally independent measure of fracture intensity defined as the fracture surface area per unit volume also with units m-1. Derivation of this property can be achieved in a number of ways but for this study it was achieved by building models with a range of known P32 values and sampling the model to derive the resultant P10 measure. Through this simulated sampling method, a relationship can be developed between P10 and P32 that allows the volume of the model to be populated with the appropriate fracture intensity, derived from borehole logs.

Fracture orientation as with fracture intensity is defined from borehole imaging data or trace mapping. Where fracture orientation data are highly systematic and organized into distinctive fracture sets, the statistical properties of these sets can be defined and used as a key stochastic input into the DFN model. However, in the case of Diavik where the orientation data have a more dispersed nature, an alternative method of “bootstrapping” can be used. This is a statistical method based upon multiple random sampling with replacement from an original sample to create a pseudo-replicate sample of fracture orientations.

Definition of fracture size typically comes from data acquired from bench and outcrop. Fracture trace length or persistence represents a chord measurement of a fracture equivalent radius and these data need to be analysed to derive the underlying radius distribution from the observed trace length distribution. Analysis of fracture trace length data indicated that the underlying fracture size distribution could be usefully described with a Power Law distribution of X=1m and D=2.

Fracture transmissivity is not generally measured; rather it needs to be derived from hydrogeologic testing results over intervals containing several fractures. Using a technique derived from Osnes et al (1988), the distribution of hydrogeologic test transmissivity results and interval lengths are analysed in order to find the underlying distribution of fracture transmissivity and conductive fracture frequency that will generate the observed distribution, see Figure 10.


To reflect the broad hydrostructural division of the site, hydrogeologic testing data from within the highly conductive Dewey Fault zone was analysed separately to data within the less permeable country rock. These derived fracture transmissivity distributions and conductive fracture frequencies are the values that best produce the observed distribution of hydrogeologic test transmissivities. The conductive fracture frequency represents the required intensity of conductive features that given the derived fracture transmissivity distribution will yield the observed hydrogeologic test transmissivity distribution. This number will always be less than the observed fracture frequency values but higher than the frequency of conductive features identified from the fluid logs as these represent only the most transmissive features.

By combining all of these properties described above, a DFN model of part of the Diavik rock mass can be stochastically generated using the FracMan code (Dershowitz et al 1998), see Figure 11. This model represents a section of rock mass across the Dewey Fault zone which can be seen as the more transmissive section through the middle of the model. The DFN model properties and their justifications are summarized in the table below.

Figure 10 -The OXfilet fracture transmissivity approach (from Dershowitz 1992).

Figure 11 -Section of a DFN model of the Diavik rock mass. Left, fractures colored by transmissivity showing the 85 m wide Dewey Fault Zone in the centre as warmer colors and right, fractures colored by radius.


Property Values Zone Justification

Fracture Frequency P10=0.35, p32 = 0.49 All zones P32 value from simulated sampling calibration

Fracture Transmissivity

Log Normal (4.2e-5, 2.3e-4)

Log Normal (3.1e-6, 7.1e-6)

DFZ

CRValues derived from OXFILET analysis

Fracture Orientation

Bootstrapping from flowing feature data All zones Constrained by Televiewer data

Fracture Size Power Law Distribution (X=1m, D=2) All zones Based on field observations

Fracture Aperture Log Normal (0.01m, 0.01m) All zones From data fitting to 04DF16 borehole data

Table I -DFN Model Properties

Flow Simulation Through the DFN Slope ModelWhilst the continuum groundwater model of the Diavik mine provides the main operation and design tool for assessing groundwater inflow

and groundwater head distribution, having a DFN description of the conductive fracture system allows some more localised investigation of a number of important issues relevant to the safe design and operation of the pit slopes that are hard to achieve in conventional modelling. These include understanding the local variation in observed head, investigation of the possibility of fractures having heads significantly above their elevation just behind the slope face and testing how the DFN model performs against observed data as a means to verify or refine the conceptual basis for the DFN model.

In order to address some of these issues, a small conceptual study was undertaken using a DFN model through the Diavik pit slope with dimensions 700 m long, 400 m high and 200 m wide, see Figure 12. The model volume was populated with fractures with properties as previously defined for the Country Rock domain. Simple boundary conditions were assigned to the model to replicate broadly that observed at the mine. The northerly edge of the model was assigned a constant head of 400 m and the pit bottom assigned a constant head of 100 m. All other model surfaces were assigned no flow boundaries. Assigning no flow boundaries on the slope face is not a strictly true description of the system. However it creates a very similar flow regime as that seen at Diavik, likely associated with pit wall freezeback, and is fit for the purpose laid out above.

Figure 12 -Left, DFN model used for steady state flow simulation and right, boundary conditions applied to the model to simulate the constant head of the nearby Lac de Gras.

Additionally 2 observation boreholes were simulated in the pit floor (collar elevation = 100 m) with each borehole being composed of 10 x 10 m intervals such that the simulated head in each interval intersected by a fracture could be reported. Simulation of groundwater flow was undertaken by converting the FracMan DFN geometry into a finite element grid and using the code Mafic (Miller et al 2001) to simulate the steady state flow conditions imposed by the near by Lac de Gras.

Model ObservationsModelling groundwater head using an equivalent porous media approach generally results in a relatively smooth distribution of pressure

as a result of the degree of homogenisation implicit in model construction. However, when the true heterogeneity of the fracture network

�0 Santiago Chile, November 2009 Slope Stability

is preserved through a DFN representation, a far more variable pressure field is revealed that is more consistent with the variable distribution of pressure observations. Figure 13 shows the distribution of head in a section through the slope model with the hotter colours representing high heads and the cooler colours, low head.

Figure 13 - Left, Section through the DFN model showing steady state head distribution revealing highly irregular pressure distribution through the DFN model. Right, a view of the groundwater head in the slope face showing the highly variable and non-smooth distribution of pressure within the fracture network. The black circles indicate fracture elements in the model that are connected through the slope to considerably high pressure zones such that they appear to locally have in excess of 50 m overpressure.

Whilst it can be seen that the generalised pressure field is transitioning in a predictable way, if the pressure variability were to be contoured as isobars, they would be represented with extremely jagged lines to effectively capture the variation in pressure through the fracture network. What controls this distribution of head within the model is how well the fracture network connects together. In the scenario tested here, the connectivity of the fracture network remains relatively high. Even with this situation the variation of groundwater head is high. With increasing disconnection, the chance of encountering a feature with an elevated head increases considerably. The issue of connectivity is further illustrated in Figure 14 where a view of the slope face is shown with fracture elements coloured by head. A number of fractures have been circled to illustrate those that appear to have a head approximately 35-50 m higher than fractures immediately surrounding them. These fractures are clearly connected out of plane of the slope through the rock mass to higher groundwater pressures resulting in this observed potential overpressure. The implications from a stability point of view resulting from over pressure are obviously poor. However the potential for a large fracture surface to maintain limited connection to the broader rock mass, thus ensuring over pressure is relatively small. Rather, as in the example shown, discrete over pressured features are more likely to be smaller features. A scenario not tested is to ensure the transmissivity of fractures and their size within the model is correlated such that larger fractures are more transmissive. Under these circumstances there is an increased risk that should a fracture have over-pressure it has an increased risk of representing a failure surface.

Figure 12 -Observations of groundwater head on the simulated boreholes in the pit bottom do show considerably less variation than those observed in both the section and slope face views. Simulated head in two vertical boreholes drilled from the 100 m level showing the variation of head away from a normal hydrostatic gradient.

��Slope Stability Santiago Chile, November 2009

The vertical distribution of head is generally smooth although there are a number of anomalous values observed, with the maximum variation from a hydrostatic gradient of approximately 8 m. This is much less than observed in reality where a number of boreholes have been drilled in the pit bottom with 30 to 50 metres of excess head. It is thought that for this simple conceptual study this is the result of a sampling issue. If more boreholes were placed within the model, the chances of intersecting one of the previously identified over-pressured fractures would increase and consequently the magnitude of any borehole measured over-pressure. However, what is observed is that a number of the simulated borehole intervals record no pressure as no conductive fractures are intersected. This is a common occurrence in the field, with many of the drilled observation wells being dry.

It is important to reiterate that these observations from the slope model are qualitative and strong conclusions cannot be drawn directly from them. However, they do illustrate how a fracture network behaves hydraulically in a very different way to a continuum and allows variations and apparently anomalous measurements and responses to be tested and conceptualised more powerfully.

CONCLUSIONS

Comprehensive drilling, testing, instrumentation and monitoring of the NW wall has revealed a complex fractured rock system that is strongly influenced by the presence of a nearby lake, the heterogeneous nature of the fracture sets, sparsely distributed and widely spaced enhanced permeability fractures and an apparent low permeable veneer of rock on the slope face. It has shown that detailed instrumentation and testing that provides a wide range of data on various scales (hydraulic heads, hydraulic conductivity, fracture orientation, spacing, and connectivity) is necessary to develop a good understanding of hydrogeologic conditions in support of pit wall stability assessments, particularly for slopes adjacent to large bodies of water. Natural drainage of the slopes in these fracture-controlled hydrogeologic conditions is difficult unless the source of the water is removed.

The DFN modelling approach, in conjunction with proper site characterization, provides an invaluable insight into detailed distribution of hydraulic heads in the pit slopes. The fractured-controlled flow field in the presence of strong hydraulic gradients, induced by the constant head formed by the body of water (e.g., between the lake and the pit slope) and the apparent low permeable rock veneer on the pit slopes (likely caused by freezeback), results in large changes in hydraulic heads over relatively short distances. Spatial variability in hydraulic heads is a function of fracture connectivity with hydraulic heads becoming more variable as the connectivity decreases.

REfERENCES

1. Osnes, J.D., Winberg, A. & Andersson, J. 1988. Analysis of well test data, applications of probabilistic models to infer hydraulic properties of fractures. Topical report RSI 0338, RE/SPEC Inc., Rapid City, South Dakota.

2. Dershowitz, W.S. 1992. Interpretation and synthesis of discrete fracture oriention, size, shape, spatial structure and hydrologic data by forward modelling. In Fractured and jointed rock masses. Myer, Cook, Goodman & tasang (eds), pp 579-586.

3. Dershowitz, W.S., Lee, G., Geier, J. and Lapointe, P.R. 1998. FracMan: interactive Discrete feature Data Analysis. Geometric Modelling and Exploration Simulation. User Documentation. Golder Associates Inc. Seattle - Washington.

4. Miller, I, G. Lee & W. Dershowitz. 2001. Mafic User Documentation V 2.0. Golder Associates Inc. Seattle - Washington.

5. Rogers, S., D. Elmo and R.Beddoes. 2009 Mine scale DFN modelling and rapid upscaling in geomechanical simulations of large open pits. This volume

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Cho Rley Paper