USING REALISTIC FRACTURE NETWORK MODELS FOR MODELLING

BLOCK STABILITY AND GROUNDWATER FLOW IN ROCK SLOPES Steve Rogers, Golder Associates, Burnaby, BC, Canada Karen Moffitt, Golder Associates, Burnaby, BC, Canada Al Chance, Golder Associates, Burnaby, BC, Canada ABSTRACT Much of the analysis and modelling of fracture related phenomena, utilize descriptions of the rock mass fracture system that are generally unrealistic. For instance the common assumptions of infinite ubiquitous joints applied to kinematic analysis of wedge failure do not provide a clear and transparent transfer of rock fabric from field data to simulation or modelling. The same applies for representing the fracture network within groundwater models where the conventional approach oversimplifies the role that fractures play. In contrast, Discrete Fracture Network (DFN) modeling methods allow the fracture network to be more realistically defined with reference to observed fracture orientations, size, intensity, spatial model and hydraulic properties, incorporating both observed large discontinuities (deterministic features) and smaller stochastically generated features. When conducting wedge or key block analysis with a DFN model, the same kinematic equations as conventional wedge stability analyses are used, including the ability to model the impact of ground support, water pressure and earthquake movement. However, by using a more realistic model of the geometry and properties of the structural features, DFN wedge analysis provides a more reliable prediction of the factor of safety than conventional tools. The same goes for modelling groundwater flow in fractured rocks. By discretely modelling the key flow pathways using a DFN approach, many of the important controls on groundwater pressure distribution such as fracture connectivity can be captured and modelled. The result of this is a far better understanding of the impact of fractures on rock mass behaviour and therefore the ability to more intelligently design and manage structures built within fractured rock masses. RÉSUMÉ Plusieurs analyses et modélisations portant sur les phénomènes reliés à un système de fracture utilisent une description du système de fracture des massifs rocheux souvent irréaliste. Par exemple, l’hypothèse commune des joints omniprésents infinis appliqués à des analyses cinématiques de coins de rupture ne fourni pas un transfère clair et transparent pour la structure des roches à partir des données de terrain jusqu’à la simulation ou modélisation. Le même phénomène s’applique pour la représentation des réseaux de fracture à l’intérieur des modèles d’eaux souterraines où l’approche conventionnelle sur-simplifie le rôle que joue les fractures. Par contre, les modèles de réseaux de fractures discrètes (RFD) permet un réseau de fracture plus réalistement définie afin d’observer l’orientation, la grandeur, l’intensité et le modèle spatial des fractures, incorporant les larges discontinuités observées (déterministique) ainsi que les petites stochastiques caractéristiques générées. Lorsqu’une analyse de RFD sur un dièdre conducteur ou un groupe de blocs est fait, les mêmes équations cinématiques sont utilisées que lors d’analyse de stabilité conventionnelle de dièdre, incluant l’habilité de modéliser l’impact du soutènement, de la pression d’eau et du mouvement sismique. Pourtant, en utilisant un modèle plus réaliste de géométrie et de propriété pour les caractéristiques structurales, l’analyse sur les dièdres à l’aide de l’approche de RFD fourni une prédiction plus fiable en ce qui a trait au facteur de sécurité contrairement aux outils conventionnels. La même chose s’applique pour la modélisation d’eau souterraine dans les roches fracturées. En modélisant séparément le tracé du courant clé en utilisant l’approche RFD, plusieurs des importants contrôles sur la distribution de la pression créée par les eaux souterraines, comme la connectivité des fractures, peut être capté et modélisé. Le résultat donne de loin une meilleur compréhension de l’impact du comportement des fractures de massifs rocheux et donne donc l’habilité de designer et de gérer intelligemment les structures construites à l’intérieur des fractures de massifs rocheux..

1. INTRODUCTION Recent developments in the ability to better characterize rock mass fabric include high resolution geophysics, borehole televiewers, optical borehole cameras, high resolution photography, laser scanning, and satellite imagery coupled with modern image processing techniques. These new tools have dramatically improved our ability to accurately describe rock fabric. With relative ease, data can be acquired and processed that will provide a detailed description of key fracture properties such as fracture length,

orientation, intensity, aperture and transmissivity. However despite the improvements in our ability to characterize the rock mass, the standard of practice for slope and tunnel kinematic analyses remains dependent on the assumption of rock wedges being defined by ubiquitous, infinitely continuous fracture planes. Some available analytical tools allow the influence of intact rock bridges to be considered by distributing the rock bridge strength over the entire fracture area; however the size distribution and frequency of intact rock bridges must be assumed by the user. In reality, the presence of fractures in the

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rock mass is spatially variable, with their geometric, mechanical and hydraulic properties being more accurately described by statistical distributions. In response to the recent advances in the ability to characterize the spatial variability of rock mass structure, a Discrete Fracture Network (DFN) method of wedge analysis has been developed to provide a more robust, probabilistic approach to surface and underground wedge stability and ground support design. 2. REAL FRACTURES AND FRACTURE

NETWORKS Despite the improvements in our ability to characterise the rock structure, most analysis and modelling techniques still fail to adequately describe the rock mass fracture system. In terms of conventional “Wedge” analysis, there are a number of fundamental implicit assumptions about the nature of the fracture system: • Fractures are ubiquitous • Fractures have infinite length • Fractures are considered independent • Fracture sets generally have constant

orientation These assumptions simplify the fracture system, but at what cost? If it is assumed that fractures are ubiquitous, this fails to capture the fact that fractures distribute themselves according to some form of spatial model, generally associated with a geological process that caused their formation. Whilst ubiquitous fracturing (or anti clustering) is one possible spatial model, many others exist and in fact are far more common. For instance often the intensity of fractures is observed to cluster around key structures such as faults, with a diminishing intensity with distance from the fault. An alternative is the observation that fractures often tend to clump together (the Fractal Model) rather than being considered as independent Poisson events. In strongly layered systems, fracturing may be constant, but it is often layer bounded with spacing controlled by bed thickness or other geomechanical properties of the rock. All other properties of the fractures being equal, the way that fractures spatially organize themselves imparts a fundamental control upon the ability of the rock mass to form wedges and ought to be considered as part of a stability analysis. Similarly, the assumption that fractures are infinite is simply not true. Even if we consider “infinite” to represent the scale of our problem, the fractures of interest (i.e. those thought to be geomechanically important) represent a range of lengths from the decimetre scale up to potentially kilometre scale features. There is ample research to demonstrate that fracture (trace) length generally conforms to some statistical distribution such as the exponential or log normal distributions. As with the spatial distribution of

fractures, clearly the length scale of fractures is going to have an important control upon the likelihood and size of rock wedge formation. Whilst fractures may initiate independently, during their propagation they do interact such that they may terminate against each other. In rock masses where the fracture network shows a high percentage of termination, the probability of block formation is significantly increased above an equivalent network where significant rock bridges exist. The general process by which fracture orientation data are analysed is to plot them stereographically and then to group them into sets of common orientation. It is then the usual practice to take the average (or range) of orientation of the various sets and identify unstable wedges based upon this simplified fracture geometry. Whilst this might not be quite so critical in highly aligned systems with fracture members of a particular set having almost constant orientation, this does not account for rock masses where fracture orientations are highly dispersed and the concentration of a particular stereographic pole centre is very weak. In this case, representing a set as a single orientation fails to honour the underlying fracture data, which has been obtained usually at no insignificant cost. So given these fundamentally unrealistic assumptions about the fracture system, can we find a better way? 3. DISCRETE FRACTURE NETWORK

TECHNOLOGY 3.1 Introduction Whilst discrete modelling methods have been around for some time now, it is only in the last few years with the wider availability of computation power that they have gained in prominence. Much of the early interest in the DFN approach was associated with modelling of groundwater through natural fracture systems (largely as part of nuclear waste isolation programmes) and for modelling fractured hydrocarbon reservoirs. However they are increasingly being identified as useful tools in modelling some of the geomechanical problems that are encountered when designing structures in fractured rock masses. DFN methods have a number of key advantages in that they are better at describing local scale problems because of their ability to capture the discrete fracture properties more accurately than larger scale continuum approaches and can capture the heterogeneity of the fracture system by explicitly describing key elements of the system. Most importantly they provide a more clear and reproducible route from site investigation data to modelling because real fracture properties are being preserved through the modelling process. 3.2 Derivation of DFN Parameters A number of properties need to be defined in order to build a DFN model, namely fracture intensity, fracture orientation and fracture size. If the simulation of flow is

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to be undertaken through the model, fracture transmissivity, storativity and aperture need also to be defined,, (Dershowitz 1995). Fracture intensity is typically defined either from borehole data (fracture logging or borehole imaging tools) or from trace mapping upon surfaces such as benches or tunnel walls. Care needs to be taken in the use of this data as it is highly directionally biased. The preferred measure of fracture intensity for a DFN model is known as P32 (fracture area/unit volume) which is an intrinsic rock mass property. Whilst it cannot be directly measured it can be inferred from the 1D and 2D data above using a simulated sampling methodology. Through this simulated sampling method, a relationship can be developed between observed fracture intensity and its associated P32 that allows the volume of the model to be populated with the appropriate fracture intensity. 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. Often however the data have a more dispersed orientation that does not support this approach. In this case, 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. A degree of “noise” is introduced to each sample to ensure that the pseudo sample is slightly different in order that multiple realizations will result in a similar but not unique orientation model. Definition of fracture length or persistence typically comes from bench and outcrop mapping although there are methods for deriving these data from borehole image logs. Fracture lengths need to be converted to an equivalent fracture radius for inclusion within the DFN model with the radius being described by a number of different statistical distributions such as the log-normal or exponential distribution. It is often a critical input into the DFN model and a key parameter for sensitivity studies. Figure 1 below illustrates the difference between a DFN model built using the conceptualization of the rock mass being comprised of infinite ubiquitous joints and one using more realistic fracture properties.

Figure 1: Comparison between a DFN model based upon assumptions of ubiquitous infinite fractures of constant orientation (top) and one based upon the more realistic assumptions of distributed, length variable and dispersed fracture orientations (bottom) 4. DFN BLOCK ANALYSIS To evaluate key slope blocks using a DFN approach, the analysis is carried out using the modelled fracture network with its fully defined spatial and geometrical properties. This is in contrast to the previously outlined implicit assumptions of most conventional methods. Rather than simply considering the possibility of blocks forming, the DFN approach considers the probability of adverse block formation. The algorithm used for the analysis of block stability in slopes is the same as those used for block or wedge stability in underground excavations. Therefore the DFN approach provides a versatile approach to probabilistic analysis of wedge/block stability in both surface and underground applications. The initial step is the construction of an appropriate DFN model. This should be generated based on the appropriate site geological and geometrical model and where possible conditioned to borehole and trace map data. The fracture geometry may include major structural features such as faults and fracture zones, stratigraphic contacts, and spatial variations in fracturing patterns. After construction of the DFN model, all 3D rock blocks in the DFN models which have at least one free face on the slope are identified. To achieve this, a trace map for the fracture intersections with the slope is generated. Once the slope trace map has been created, rock blocks can be constructed by identifying those fractures which form closed two-dimensional blocks in the trace map, forming trace maps on those

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fractures and repeating the process until all fractures participating in trace maps have trace maps of their own. This results in a collection of faces and connection information. All of the faces are processed using an “unfolding” algorithm to generate the minimum volume polyhedron which connects to the slope face. The rock block volume is computed by a process of three-dimensional tessellation with the associated block mass being calculated using this computed volume and the assigned rock density. The stability analysis for rock blocks defined by realistic fracture geometries from a DFN model is functionally identical to other key block analysis tools. The fundamental difference is that the analysis is carried out on actual defined 3D blocks for a specific realization of the fracture geometry, rather than on a combinatorial approach of infinite fractures. The stability analysis is carried out by checking whether each identified block satisfies the criteria for unconditional stability, whether the block may slide (along one or two sides), or whether it is a free falling block. The factor of safety is then calculated based on limit equilibrium assumptions. The factor of safety (FS) of a block is calculated in different ways depending on the failure mode of a given block. Stable blocks have an effectively infinite factor of safety. Free falling blocks have a factor of safety of zero. Between these two extremes lie the cases of one-plane sliding and two-plane sliding. The factor of safety for these two failure modes can be calculated using either a Mohr-Coulomb or Barton-Bandis model and shown below for the Mohr-Coulomb model. For single plane sliding and the Mohr Coulomb failure criterion;

FSA c N

S=

⋅ + ′ ⋅ tan( )φ

[1]

Where A is the area of the face C is the cohesion parameter N’ is the normal force to the face φ is the friction angle S is the magnitude of the shear force For two plane sliding: Using the Mohr-Coulomb model:

S

AA NcNcFS

12

22221111)tan()tan( φφ ⋅′+⋅+⋅′+⋅

=

[2]

Where

N1’, N2’ are the normal forces to faces 1 and 2, respectively A1, A2 are the areas of faces1 and 2, respectively S12 is the shear force along the edge created by faces 1 and 2 ci is the cohesion parameter of face i φι is the friction angle of face i 5. DFN SLOPE STABILITY EXAMPLE In order to help understand the DFN approach to slope stability assessment, an example is presented that represents a simulated rock mass where a DFN wedge analysis has been carried out on a 30m high slope. Figure 2 shows a stereonet of the synthetic fracture data generated, with the three fracture sets present being defined in Table 1. Fracture length data were described using a log normal distribution with a mean of 15m and a standard deviation of 15m. The average volumetric fracture intensity (P32) was 0.5m-1. The slope is dipping to the north with a dip of 65o.

Figure 2: Lower hemispherical stereographic projection of fracture orientation data showing the 3 simulated fracture sets Table 1: Input joint set distribution for simulated fracture model

Fisher Distribution Log Normal Distribution

Joint Set

Dip Azi

Dip Dispersion Mean SD Min

1 45 70 30 15 15 7.5 2 315 70 35 15 15 7.5 3 360 45 30 15 15 7.5

In contrast to the common block analysis approach, the DFN model has explicitly modelled this fracture network using a stochastic approach.

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Figure 3: Slope face trace maps for 6 iterations showing blocks that are formed by the slope face. The darkest colour represents kinematically unstable blocks, the intermediate colour represents stable blocks, the lighter grey colour represents areas of the slope face that have formed 2D blocks and the light areas represents zones that don’t form blocks at all.

Unstable Blocks Stable Blocks 2D Blocks only Unformed Blocks

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Each realisation that is generated is statistically equivalent to one other but has different block geometries as a consequence of varying fracture orientations and fracture lengths derived from the stochastic process. The DFN method searches through each realisation of the model to identify blocks and to determine their factor of safety. Figure 3 shows the slope trace map for six of the simulations, coloured by whether a block is formed or not and distinguishing unstable from stable blocks. For each block identified by the DFN analysis, its size, mass, and FOS are reported allowing a comprehensive analysis of the likelihood of block formation to be undertaken. For each iteration, the total number of blocks formed is recorded along with the number of stable, unstable and free falling blocks. Interestingly in the example shown in Figure 3, despite the relatively high fracture frequency, the number and size of complete blocks formed by the fracture network is relatively low due to the variable nature of the fracture geometry. However when they do form, the geometry of the fracture network is such that they tend to be unstable. 6. DFN MODELS AND GROUNDWATER FLOW

It is not only the mechanical influence of fractures on slope behaviour that is significant, but also their hydrogeological significance that requires consideration. Conductive fracture networks impose a number of important controls upon groundwater flow and the distribution of groundwater pressures within rock slopes that are somewhat different from porous rocks.. Having developed a DFN model to examine block kinematics, that same model can be used to simulate flow through a rock slope in order to evaluate the likely distribution of groundwater pressures. The model can also be used to optimise depressurization measures where required to mitigate potentially elevated slope heads. Figure 4 shows an example of a DFN model converted to a finite element (FE) flow grid that can be used simulating groundwater flow. One of the striking observations seen when conducting discrete rather than continuum flow modelling, is the danger of considering average pressures. The distribution of pressure in the fracture network is primarily controlled by both the distribution of hydraulic fracture properties (principally permeability and storage) as well as the degree of connectivity between the various fracture elements. This means that the notion of pressure contours representing lines of equal head becomes less relevant in a fractured system where head distribution is often actually mapping fracture connectivity (see Figure 5).

Figure 4: DFN model of a slope converted to a FE flow grid for groundwater flow modelling

Figure 5: Distribution of steady state head through a slope DFN slope model. The hand-sketched contour lines show the highly irregular nature of the pressure distribution. It is not too uncommon to measure anomalously high groundwater heads in piezometers installed behind a slope face. It is difficult, however, to rationalize whether these high heads are indicative of a large area of elevated pressures or if they are very local in nature. These generally represent borehole connections to fracture elements connecting over different length scales. Fractures connecting over greater length scales may for instance carry elevated heads into localised areas behind the slope, possibly creating potentially localised areas of instability. Figure 6 shows an example of a fracture connected back through the slope having a head approximately 40m higher than the surrounding the rock mass. Similarly lack of connectivity is often observed with boreholes taking considerable time to equilibrate either after installation of transient events (such as blasting) as a result of poor connectivity to the main conductive fracture network.

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Figure 6: Distribution of groundwater head in the slope face of model seen in Figure 5 showing localised areas where significant elevated heads are observed (see circle) 7. WELL TESTS AND FRACTURE FLOW

As mentioned above, one of the key issues in understanding and modelling groundwater flow in fracture systems is accurately capturing the degree of fracture connectivity. The main source of data that can help us address this uncertainty are hydraulic tests. Arguably these are the only piece of data that tell us something about how the conductive fracture network extends away from the borehole. Conventionally, well tests are used to derive basic hydraulic properties of the rock mass such as hydraulic conductivity and storativity. However there is much more that can be extracted from a test, in particular the pressure derivative curve. The derivative curve (Bourdet et al 1989) is the semi-log derivative of pressure with time and effectively maps out changes in transmissivity with distance from the well. This means that the gradient of the derivative curve provides information on the connectivity of the fracture pore volume away from the well. For instance where flow is principally moving through a poorly connected and anisotropic fracture network, the derivative curve would generally show what is known as a positive half slope. If the degree of connectivity of the fractures were to be greater (and all other things remained the same), the derivative curve would be flatter. For years people have understood how these derivative curves related to the basic geometry of flow in porous media. However it was only as a result of discrete modelling methods that allowed them to be related to fracture network geometries. Figure 7 below shows an example of a poorly connected fracture network with highly conductive faults away from the well. When a well test is simulated through this fracture network, strong linear flow (half slope) is seen up until approximately 50 hours when the derivative curve roles

over as the simulation senses the conductive faults at distance.

Figure 7: DFN model of a poorly connected fracture network with conductive faults (top) and the resultant simulated pressure derivative (solid line) and actual derivative curve (dots) below. By using a DFN model to help test and explore the controls on well test responses, the underlying conceptual fracture model can be strengthened, the underlying network geometry constrained and the hydraulic properties calibrated.

8. DISCUSSION A real benefit of the DFN approach is that it more accurately represents the three-dimensional fracture network geometry. In the past one of the barriers to using discrete modelling methods was the lack of accurate data describing the fracture geometry and its

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physical properties. However increasingly over the last few years there have been significant improvements in our ability to image and measure properties of the fracture network. This means that increasingly many of the site investigation and data analysis steps routinely undertaken are now providing DFN ready data inputs to the fracture modelling process. Certainly the use of borehole televiewer systems, laser scanning systems, and advanced photogrammetry techniques are increasingly being used within both the civil and mining industry, providing a rapid and reproducible way for obtaining data on the distribution of fracture orientation, fracture length and fracture intensity. A key advantage of the DFN approach is the ability to handle the analysis and results in a probabilistic way for use in design studies. As illustrated in the stability example, multiple realisations of the same model can be generated, with the stability analysis carried out on each iteration. In this way, the DFN model could be generated a large number of times (e.g. 100) and the stability analysis undertaken on each model, resulting in the generation of probability density functions (PDFs) of wedge mass, wedge volume, factor of safety and so forth. This probabilistic approach using measured fracture size and orientation data effectively allows the engineer to apply an appropriate level of conservatism to the design application. In effect, the method can predict the likelihood of an event occurring for a given slope length or area. For instance the results might predict the probability of a wedge greater than 10 tonnes being present for every 50m of slope length. This could also be expressed in terms of frequency of occurrence such than on average, a wedge greater than 10 tonnes should occur every 200m on average. The analysis of the fracture network in a discrete way allows the engineer to optimize appropriate measures for the specific fracture network. For instance support systems such as bolt length, spacing and orientation can be optimised to provide the most effective support system to meet a given design criteria. Alternatively dewatering wells can be designed to maximise their connection to the conductive fracture system, maximizing their efficiency for slope depressurization. 9. CONCLUSIONS DFN methods clearly have an increasing role to play in the design of structures in fractured rock masses, where they can take advantage of the significant developments in fracture imaging and measurement. The DFN approach offers a number of advantages over many conventional modelling methods, namely:

• Fractures are modelled more realistically with assigned size, orientation, dependence and spatial model;

• Multiple realizations of the DFN provide a probability distribution function for unstable wedge development for a given excavation geometry.

• The simulation of groundwater flow (both steady state and transient) through DFN networks allows a greater understanding of the fracture controls on flow to be developed

By more realistically modeling the geometry and the hydraulic properties of the fracture network, more intelligent input to design and control of rock slopes can be made that ultimately has the potential to produce safer and more economical rock slope designs. 10. ACKNOWLEDGEMENTS We would like to acknowledge the immense contribution of Bill Dershowitz and Joe Carvalho, both of Golder Associates for their leading role in the development of a DFN approach to block stability 11. REFERENCES Bourdet, D, Ayoub, J.A, and Pirard, Y.M. 1989. Use of

the pressure derivative in well test interpretation. Society of Petroleum Engineers, Formation Evaluation, June 1989.

Carvalho, J., E. Hoek, and B. Lee, 1991. UnWedge:

Underground Wedge Analysis. Department of Civil Engineering, University of Toronto, Toronto.

Dershowitz, W.S. 1995. Interpretation and synthesis of

discrete fracture orientation, size, shape, spatial structure and hydrologic modelling. In Fractured and Jointed Rock Masses, Eds Myer, Cook, Goodman and Tsang.

Einstein, H. and E. Glynn, 1979. Probability of

Kinematic Instability in Rock Slopes: A Numerical Approach. Proceedings, 20th US Symposium on Rock Mechanics, Austin, Texas. ASCE, NY. p 317-325

Goodman, R. E., and G.-h. Shi, 1985. Block Theory

and Its Application to Rock Engineering. Prentice Hall, New York.

Hatzor, Y and R. Goodman, 1992. Application of Block

Theory and the Critical Key Block Concept to Tunneling: Two Case Histories. Proceedings, ISRM Conference on Fractured and Jointed Rock Masses, Lake Tahoe, CA.

Warburton, P.M., 1987. Implications of Keystone

Action for Rock Bolt Support and Block Theory. International Journal of Rock Mechanics, Mining Science, and Geomechanics Abstracts., Vol 24, pp 283-290.

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Pages1345-1459.pdfPaper 274.pdfINTRODUCTIONLIMESTONE HYDROGEOLOGY AND QUARRYING IN THE EAST MENDIPSGIS DEVELOPMENTConceptMethodology

RESULTSGeographical Information SystemLocal Planning ToolSystem Publication

CONCLUSIONSACKNOWLEDGEMENTS

Paper 313.pdfINTRODUCTIONPROJECT OVERVIEWAFLRP PROJECT COMPONENTSAvailable Reclaimed WaterReclaimed Water Storage Credits and Recovery

GROUNDWATER MODELINGPrevious Infiltration Impact AssessmentsGroundwater Model DevelopmentNo-Action ScenarioInitial AFLRP ProjectionsRevised AFLRP Projections

AREA OF HYDROLOGIC IMPACTCONCLUSIONS

Paper 214.pdfINTRODUCTIONDESCRIPTION OF THE STUDY AREAGEOLOGYBedrock geologyWolfville FormationBlomidon FormationNorth Mountain Formation

Surficial deposits

FIELDWORKWater level surveyDrilling and installation of piezometersWater and soil samplingHydraulic testsSeepage and flowmeter measurementsBorehole geophysicsFuture work

ANALYSES AND INTERPRETATIONBedrock resultsSurficial deposits and soil resultsConceptual modelSurface water results

COUPLED MODELINGCONCLUSIONACKNOWLEDGEMENTS

Paper 250.pdfINTRODUCTIONMATERIALS AND METHODSDescription of the studied watershedThe AGRIFLUX modelThe PHYSITEL/HYDROTEL modelThe MODFLOW model

RESULTS AND DISCUSSIONInfiltration and recharge using AGRIFLUXSurface flow using PHYSITEL/HYDROTELGroundwater flow using MODFLOW

CONCLUSIONACKNOWLEDGEMENTS

Paper 258.pdfINTRODUCTIONDATAClimate DataHistoric Climate DataGlobal Climate Model Data

Spatial Data

METHODSClimate GenerationFuture Predicted Climate ChangeDownscaling Using SDSMRaw GCM Data

Recharge Model

RESULTSCONCLUSIONS

Paper 213.pdfINTRODUCTIONSITE DESCRIPTIONMETHODOLOGYPiezometersRunoff and Pond Water LevelsMeteorological Stations and Soil TemperatureChloride extraction

RESULTSSpring Melt 2003Snowmelt and runoffSoil thawing and pond infiltrationGroundwater

Spring Melt 2004Snowmelt and runoffGroundwater

Post-snowmelt 2004Recharge by rainfall eventControlled flooding of C24

Chloride balance

DISCUSSIONPrecipitationRunoffSnowmeltInfiltrationDepression-focused recharge

CONCLUSIONSACKNOWLEDGEMENTS

Paper 544.pdf1. BACKGROUNDThere was an extraordinary coincidence of events in 1973 with B.C. Hydro’s initiation of the Revelstoke Dam Project. These eve1.3 B.C. Hydro’s Project ManagementThe project management of B. C. Hydro (Hydro) was critical to the successful outcome of the slide investigation. Hydro ensure

The ideal modular monitoring array probably needs to be made up of two or more compatible modular subsystems:

Firstly, a modular borehole completion casing system that can be used to hydraulically isolate monitoring zones by means of seSecondly, a modular data acquisition and control system that can be used inside the casing system for data collection. This sy3.2 Calibration and Maintenance RequirementsThe ideal borehole monitoring system should have the capability for essential QA tests to be conducted on all components immed3.3 Requirements for Multilevel Borehole Seals3.5 Other Useful CapabilitiesAlthough fluid pressure is the key parameter in any slide study, it would be helpful in other geotechnical and hydrogeologic s4.2. Adverse Drilling ConditionsDrilling conditions were extremely adverse in comparison with typical geotechnical environments. Boreholes were lost or unabl5. INSTRUMENTATION SOLUTIONS ADOPTEDIt was recognized that no geotechnical instruments were available to handle the combination of depth, fluid pressure, placemen5.1. First Solution – Multiple StandpipesHydro had previous experience placing small-diameter standpipes inside HQ and NQ sized boreholes. They continued to develop thThe fluid levels in the standpipes were monitored for piezometric level fluctuations. Frequently, in any one borehole there wThe multilevel piezometer system is a borehole completion casing that has a number of components including external casing pacBecause slide movements can deform the borehole, the instrument system had to be flexible. As a result, telescopic casing seg

7.2 Dutchman’s Ridge Slope Stability StudyIn the period 1986 to 1988, B. C. Hydro undertook the first full deployment of the multilevel piezometer system for the slope 8. CONCLUSIONSThe serious engineering problem posed by the existence of the Downie Slide along the side of the proposed Revelstoke Dam reserWhile the fluid–pressure data utilized for the Downie Slide studies were largely provided by the use of multiple standpipe pieAn early version of the resulting system was installed late in the Downie Slide investigations. But, it was not until after thWithout the stimulus provided by the Downie Slide investigations and the creative environment associated with the project, the

9. ACKNOWLEDGEMENTSHubbert, M. K. (1940) The theory of ground-water motion, Jour. Geology, 48: 8: 785-944Imrie, A. and Bourne, D. R. (1981) Engineering geology of the Mica and Revelstoke Dams, Field Guides to Geology and Mineral DePatton, F.D. and Deere, D.U. (1971a) Significant geological factors in rock slope stability Proc. Int. Conf. on Planning Open Patton, F. D. (1983) The role of instrumentation in the analysis of the stability of rock slopes, Int. Sym. on Field MeasuremePatton, F. D. (1990) The concept of quality in geologic and hydrogeologic investigations, Proc. 5th Int. Sym. on Landslides, L

Patton, F. D., Black, W. H. and Larssen (1991) D. A modular subsurface data acquisition system (MOSDAX) for real-time multi-leTatchell, G. E. (1991) Automated data acquisition systems for monitoring dams and landslides, Proc 3rd Int. Sym. on Field Meas

Paper 147.pdfINTRODUCTIONSTRATIGRAPHYINSTUMENTATIONGROUNDWATER SYSTEM MODELINGSTABLITY MODELINGCONCLUSIONSACKNOWLEDGEMENTS

Paper 391.pdfDevelopment of flushable adaptorDetermination of grout propertiesPore pressure response testPiezometer selectionInstallation Procedure

Paper 364.pdfINTRODUCTIONINSTRUMENTATION SYSTEMSETTLEMENT OF THE DAMUpstream ShellDownstream ShellClay Core

NUMERICAL MODELLINGBack Analysis

CONCLUSIONSACKNOWLEDGEMENTS

Paper 566.pdfINTRODUCTION2.HAZARD3.EARTHQUAKE MAGNITUDE FOR USE IN LIQUEFACTION ASSESSMENTSELECTION OF EARTHQUAKE RECORDSCHARACTERISTIC EARTHQUAKE DISTANCE

Paper 558.pdfINTRODUCTIONREAL FRACTURES AND FRACTURE NETWORKSDISCRETE FRACTURE NETWORK TECHNOLOGYIntroductionDerivation of DFN Parameters

DFN BLOCK ANALYSISDFN SLOPE STABILITY EXAMPLEDFN MODELS AND GROUNDWATER FLOWWELL TESTS AND FRACTURE FLOWDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES

Pages1460-1583.pdfPaper 267.pdfPaper 267.pdfINTRODUCTIONOBJECTIVES AND SCOPEPHASE 3 ANALYTICAL STUDYDatabase Development and InterpretationConceptual Hydrogeologic ModelGroundwater ModelSlope Stability Analyses

RESULTS AND CONCLUSIONSCritical Moraine SlopesMoraine Hydrogeology Modeling ResultsStability of Critical SlopesConsequences of FailuresStability Risk Assessment

RECOMMENDATIONSRemedial MeasuresReservoir Operations

ACKNOWLEDGMENTS

Paper 416.pdf3. PHYSICAL CHARACTERISTICS OF THE AQUIFERAGRICULTURE PROGRAMS AND RESULTS5.1 Stewardship Programs by the Raspberry Industry

6. TRENDS IN GROUNDWATER QUALITY7. SOURCE AND MECHANISM OF CONTAMINATION7.1 Effectiveness of Nutrient Management Planning as Source Control Tool7.2 Additional Sources of N7.3 Mechanism of Contamination

8. FURTHERING OUR UNDERSTANDING8.1 Advanced Hydrogeological Research8.1.1 Modeling8.1.2 Direct Testing of Recommended BMP’s8.1.2 In-Situ Remediation8.2 Improvement of Monitoring

Paper 265.pdfINTRODUCTIONPHASE 1 - DATA COMPILATIONPHASE 2 – CONCEPTUAL MODELPHASE 3 – NUMERICAL MODELModel DevelopmentWell Capture ZonesAquifer Water Balance

CONCLUSIONSACKNOWLEDGEMENTS

Paper 152.pdfINTRODUCTIONGEOPHYSICAL SURVEYINGSite DescriptionBorehole LoggingGround Penetrating RadarImplications for Nitrate Transport

VADOSE ZONE MODELLINGBackgroundResidual Nitrate Concentration

The ApproachModelling Results

SATURATED ZONE TRANSPORTSensitivity AnalysisParticle TrackingComparison of Model Ages to Isotopic Ages

CONCLUSIONSACKNOWLEDGEMENTS

Paper 359.pdf1. INTRODUCTION 2. HYDROGEOLOGY 3. METHODS 6. ACKNOWLEDGEMENTS

Paper 377.pdfINTRODUCTIONSTUDY AREALocation and Geologic SettingHydrogeology

SALINITY DISTRIBUTIONTRANSPORT MODELLINGDISCUSSIONEstuarine AreasInland and Delta Front Areas

CONCLUSIONS

Paper 401.pdfINTRODUCTIONPHYSIOGRAPHIC AND GENERAL GEOLOGICAL SETTINGGeneral Stratigraphy

EXISTING GROUNDWATER USAGESTRATIGRAPHIC INTERPRETATION FOR GROUNDWATER DEVELOPMENTSemiahmoo Outwash SandWestlynn Outwash

APPLICATION OF THE SURREY STRATIGRAPHIC MODEL TO GROUNDWATER EXPLORATION AND DEVELOPMENTSurrey Test Well Drilling Program

CONCLUSIONS

Paper 121.pdfINTRODUCTIONPHYSIOGRAPHY AND HYDROGEOLOGYFRACTURE COLLECTION AND ANALYSISField Data CollectionStatistical AnalysisStochastic ModelingDiscrete Fracture NetworksParameter Estimation

VERTICAL PERMEABILITY RESULTSCLIMATE AND RECHARGE MODELLINGClimateRechargeRecharge Modelling

RECHARGE DISTRIBUTIONCONCLUSIONSACKNOWLEDGEMENTS

Paper 316.pdfINTRODUCTIONHYDROGEOLOGYVancouver IslandGulf Islands

AQUIFER CLASSIFICATIONSOBSERVATION WELL NETWORKLONG-TERM TRENDS IN WATER LEVELSSite 1 – North-Central Saanich AquiferSite 2 – Gabriola Island

6.SUMMARY7. ACKNOWLEDGEMENTS

Paper 417.pdfINTRODUCTIONSTUDY REGION AND GEOLOGIC SETTINGFRACTURED BEDROCK AQUIFERS AND WELL YIELDSdrilling. In some instances, involving low-producing wells, some well drillers would also drill a few extra metres below the INVESTIGATORY APPROACHRESULTSArea “A” (Aquifer 608 at Ardmore)

Figure 4. Extension fractures (tension joints) occurring between shear zones striking east-west and dipping towards the north,Area “B” (Aquifer 681 at Willis Point)Area “C” (Aquifer 680, Highlands-Lone Tree Hill)

Figure 10. Curvilinear low angle shear fracture and intersecting open tension fractures in rocks of West Coast Crystalline ComCONCLUSIONSREFERENCES

Paper 350.pdfINTRODUCTIONStudy Area

METHODSEstimated Well YieldWell Head Location, Elevation and SlopeLineaments

RESULTS AND DISCUSSIONEstimated YieldWell DepthElevation at the Well HeadSlopeBedrock TypeDistance Between Well and Closest LineamentDistance between Well and Closest Lineament IntersectionSources of Error

CONCLUSIONS & RECOMMENDATIONSACKNOWLEDGEMENTS

Paper 188.pdfINTRODUCTIONSTUDY AREAMETHODOLOGYFIELD DATANUMERICAL MODELMODEL DESIGN

RESULTS AND DISCUSSIONSteady state calibrationTransient state simulation

CONCLUSIONACKNOWLEDGEMENTS

Pages1584-1680.pdfPaper 234.pdfELEVATED FLUORIDE AND BORON LEVELS IN GROUNDWATER FROM THE NANAIMO GROUP, VANCOUVER ISLAND, CANADASteven Earle, Geology Dept., Malaspina University-College, Nanaimo, British Columbia, CanadaErik Krogh, Applied Environmental Research Laboratories, Chemistry Dept., Malaspina University-College, Nanaimo, British Colum1. INTRODUCTION1.1 Study area

FormationLithologyGabriolaMedium- to coarse-grained submarine fan feldspathic sandstone (average 15% matrix), with mudstone interbedsSpraySubmarine fan mudstone and siltstone with turbidites, and with sandstone interbedsGeoffreyMedium- to coarse-grained submarine fan feldspathic sandstone (average 15% matrix) interbedded with conglomerateNorthum-berlandSubmarine fan mudstone and siltstone with sandstone interbedsDe CourcyMedium- to coarse-grained submarine fan feldspathic sandstone (average 15% matrix), with mudstone interbedsCedar DistrictSubmarine fan mudstone and siltstone with turbidites, and with sandstone interbeds2. METHODS3.1 Major element water geochemistryAs shown on Figure 4a, the majority of the groundwaters that we sampled are dominated by bicarbonate, although a few have chloThe major-element characteristics of the Yellow Point and Gabriola groundwaters, as described above, are generally very simila3.2 Trace element water geochemistry3.3 Rock geochemistry

Paper 286.pdfHydrogeological Study of the Cold Lake Air Weapon Range, Alberta

Paper 327.pdf3.2.Second exemple, |a| < 2, éqs. [20-21]Chapuis, R.P. 2002. Solution analytique de l’écoulement en régime permanent dans un aquifère incliné à nappe libre, et compara

Paper 237.pdfHYDROGEOLOGIC INVESTIGATIONS IN THE CANADIAN NORTH1.INTRODUCTION2.ACCESS, CLIMATE, AND DRILLING3.PERMAFROST4.POST-GLACIAL GEOLOGY

Paper 240.pdf1.INTRODUCTION

Paper 118.pdfINTRODUCTIONTHE INTERFACIAL FLOW METER DESIGNCalibrationInstallation of piezometer and stream gauge clusters.Measuring water flow across the sediment-water boundary

RESULTS AND DISCUSSIONTemporal and spatial variation of hydraulic headWater flow across the sediment-water boundaryVertical hydraulic conductivity

ACKNOWLEDGEMENTS

Paper 146.pdfINTRODUCTIONDATA ACQIUISITION STRATEGYCompound-Specific Isotope Analysis (CSIA)Signature Metabolite Analysis (SMA)Redox-Sensitive Tapes (RST)

CASE STUDIESCase Study 1 – Creosote-contaminated SiteSite DescriptionSite-specific Data Acquisition StrategyResults

Case Study 2 – Gas Station SiteSite DescriptionSite-specific Data Acquisition Strategy

Results

CONCLUSIONS

Paper 386.pdfINTRODUCTIONSIMULATION OF ADVECTIVE HEAT TRANSPORT IN HETEROGENEOUS ENVIRONMENTSGeneration of Permeability FieldsNumerical Models

RESULTSDISCUSSION AND CONCLUSIONSACKNOWLEDGEMENTS

Paper 144.pdfFINDING BURIED TREASURE – ASSESSING AQUIFER SUITABILITY FOR LARGE OPEN LOOP GEOEXCHANGE APPLICATIONSINTRODUCTIONGENERAL APPROACHRequired Site InformationAcceptance CriteriaIn practical use, many of the above parameters can be estimated or approximated using established methods and the minimum requSuitability Assessment

FLOW CHART FOR SITE SUITABILITY ASSESSMENTCASE EXAMPLECONCLUSIONSACKNOWLEDGEMENTSWater QualityMineral scaling or corrosion of well screens or exchanger platesThermalSpace for required geoexchange well separationSpace for dissipation of thermal plumeInduced temperature change in nearby wells

Paper 550.pdfINTRODUCTIONPRE-MINING CONCEPTUAL MODELS1996 Field Data1996 Conceptual Model 1997 Field Data1998 Conceptual ModelGeochemical Data1998 Numerical Model1999 Field Data1999 Conceptual Model

OBSERVATIONS DURING MININGCONCEPTUAL MODEL 2004DEWEY'S FAULTDISCUSSIONACKNOWLEDGEMENTSREFERENCES

Paper 138.pdfINTRODUCTIONDISCRETE VS MIXED PLUMESSTATISTICAL MEASURES OF SIMILARITY4.APPLICATION TO THE LLAGAS SUBBASINCONCLUSIONSACKNOWLEDGEMENTS7.REFERENCES

Pages1681-1773.pdfPaper 235.pdfPaper 235.pdfINTRODUCTIONGEOLOGICAL SETTINGFigure 2. MERA I and II springs overlayed on the lithology of the SNRB and NNP (based on Okulitch, 2005).LINKING MAJOR IONS AND ISOTOPES TO THE LOCAL GEOLOGYPREDICTING MINERALIZATION TYPES USING TRACE ELEMENT CONCENTRATIONSStatisticsLocal Pluton and Spring Trace Element ComparisonFicklin DiagramLinking Trace Element and Major Ion Geochemistry

CONCLUSIONSACKNOWLEDGEMENTSREFERENCES

Paper 238.pdf1.INTRODUCTION2.HYDROSTRATIGRAPHY2.1Ordovician Sedimentary Rocks2.2Ekwan River and Severn River Formations2.3Attawapiskat Limestone2.4Quaternary Deposits

3.GROUNDWATER FLOW

Paper 251.pdfINTRODUCTIONMETHODSRESULTS AND DISCUSSIONPhase I Data ReviewEarly Historical DataHistorical Data from 1990 to 2001

2002 to 2005 Monitoring ResultsWater Levels2002 Chemistry2004 and 2005 Chemistry

SUMMARY AND CONCLUSIONSACKNOWLEDGEMENTSREFERENCES

Paper 260.pdfINTRODUCTIONSITE HYDROGEOLOGYNUMERICAL HYDROGEOLOGIC MODELEVALUATION OF REMEDIAL ALTERNATIVESOption 1 – Pumping of Mixed Freshwater and SeawaterOption 2 – Pumping of Freshwater with Seawater BarrierOption 3 – Pumping of Freshwater with Barrier WallOption 4 – Pumping of Freshwater Only

IMPLEMENTATION OF GROUNDWATER MANAGEMENT SYSTEM

Paper 279.pdfINTRODUCTIONEXPERIMENTAL OVERVIEW –TRACE-METAL MOBILITY EXPERIMENTSSampling: Aqueous PhaseSampling: Solid Phase

RESULTS AND DISCUSSIONAqueous GeochemistryTrace-Metal Mobility MCCTrace-Metal Mobility MFCC

CONCLUSIONS

Paper 319.pdfINTRODUCTIONWELL INSTALLATIONSDesign of Monitoring Wells

SAMPLING AND TESTINGWater Quality

PRESSURE PROFILESDISCUSSIONACKNOWLEDGEMENTSREFERENCES

Paper 326.pdfINTRODUCTIONMODELLING APPROACHESCONCEPTUAL MODEL 1OverviewSimulation ApproachSimulation Results

CONCEPTUAL MODEL 2OverviewSimulation ApproachSimulation Results

CONCLUSIONSACKNOWLEDGEMENTSREFERENCES

Paper 353.pdfINTRODUCTIONCONCEPTUAL MODELGROUNDWATER FLOW MODELSOLUTION MINING IMPACT ASSESSMENTOperation PeriodPost Operation Period

SUMMARY AND CONCLUSIONSACKNOWLEDGEMENTS

Paper 430.pdfINTRODUCTIONDIVERSION CAPACITY OF INCLINED COVERSBACKGROUND STUDIESSIMULATIONS OF THE YEARLY RESPONSE UNDER HUMID CONDITIONSDISCUSSION AND CONCLUSIONACKNOWLEDGEMENTS

Paper 475.pdfINTRODUCTIONASSESSMENT OF THE TUNNEL PLUGProving that the Plug Test would be safePreparation for the Plug Test and Mine FillingFilling of the Mine and the Plug TestResults of the Mine Filling Experiment

ACKNOWLEDGEMENTS

Paper 525.pdfINTRODUCTIONClimate

POTENTIAL REMEDIAL OPTIONSCONCLUSIONS

Pages1774-1775.pdfEXTENDED ABSTRACT