This document discusses analytics for evaluating early stage diamond projects. It describes how an integrated value chain model (IVCM), or "digital twin", can be used to explore different project configurations and identify material risks. The probabilistic, dynamic analytic approach provides insights to support investment decisions. By exploring a range of potential outcomes under different scenarios, it helps assess projects earlier to identify opportunities and risks, compared to traditional averaged or deterministic approaches. The methodology aims to improve evaluation and decision making for senior stakeholders regarding early stage diamond exploration projects.

1. Diamonds – Source to Use – 2021 Online Conference
Johannesburg, 9 – 10 June 2021
The Southern African Institute of Mining and Metallurgy
17
Analytics for effective investment in early stage diamond
projects
S. Coward1 and J.A.H. Campbell2
1Interlaced, Australia
2Botswana Diamonds Plc, Ireland
Early stage (i.e., pre resource definition) diamond projects need to be evaluated with an
appropriate level of sophistication to successfully compete for scarce funding. The
approach needs to balance speed, cost and accuracy to prepare and deliver information
that can used to support commercial judgments made by potential investors.
Characterised by high levels of uncertainty, these projects require a seemingly
counterintuitive, dynamic analytical approach to value proposition assessment. The
methods used must not only produce plausible possible commercial value ranges in a
timeframe that matches the window of investment opportunity, but their outputs must
also provide clarity that supports the trust and judgment of investors. Traditional
approaches (that range from simple rules of thumb to far more quantitative approaches
that require classified spatial ore body models, validated process designs with detailed
financial projections) have often failed to deliver results that meet with the requisite mix
of speed and clarity to support effective risk-based investment decisions.
This paper describes a methodology that has been successfully applied to several
diamond projects and investment opportunities. The method demonstrates how, in very
early-stage evaluation, an analytics framework incorporating geological, process and
financial data can be used to build an integrated value chain model (IVCM). This dynamic
connected model, or so called 'digital twin', can be used to explore, refine and select the
most attractive configuration of the project and, importantly, highlight the material risks.
The transparent quantitative approach provides richer project insights for project
owners, potential investors and a variety of stakeholders. This results in confident
judgement and improved investment decision making processes.
Adopting a dynamic probabilistic analytic approach to project evaluation presents senior
stakeholders (companies, company executives, project owners, potential investors and
interested stakeholders) with a methodology to:
• Create an ongoing review paradigm to identify and invest in projects with
potential upside;
• Explore value liberating project configurations that can be achieved by both
technical and financial engineering options;
• Create insights that will allow for improved judgment and timing of decisions to
robustly build a well-structured portfolio of exploration options.
The approach can lead a transformation in the way diamond projects and their
required investments can be conceived, evaluated and executed by using a range of
dynamic analytics paradigms.

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INTRODUCTION
Successful early stage exploration and resulting kimberlite discoveries rely on a sequence of good
strategic decisions (both commercial and technical), and some measure of luck. These decisions include
the selection of the right terrains, applications of a variety of craton to camp scale exploration
methodologies, rapid following up of indicators to identify kimberlites and critically appropriate levels
of funding.
The stage post-discovery pre-resource definition requires substantial sampling, sample processing and
analysis to demonstrate that discovery is an attractive investment. This is a difficult phase to complete -
called the ‘orphan phase’- as there is often a requirement for large investment (of the order of millions
of US$) to evaluate a diamond-containing deposit with sufficient rigour to support the definition of a
classified resource. The tantalising challenge in early stage projects is to frame and evaluate an
appropriate number of project configurations with sufficient realism to inform the judgment of
investors, project owners and stakeholders in a way that allows them to take a view on a project's value.
This view will support the selection of a chosen investment pathway that might include any combination
of the following; invest fully, fund in stepwise fashion to acquire more information or move on to other
investment opportunities.
Due to the low and variable mineralisation content in diamond deposits, substantial weight must be
placed on conceptual models. The reliance on these models requires diligent application of the scientific
method which seeks to invalidate these models; this suggests that useful evaluation frameworks will
embrace and support multiple competing working hypotheses (Tarantola , 2005). These models include
aspects of geology that are used to make inferences about geometry and total size of the deposit, and the
number, shape and relationships between internal lithologies. Models of diamond mineralisation
(concentration, size distribution and expected diamond value) often based on relatively few stones, will
change quite markedly as additional samples are acquired and treated. In some cases, it is possible to
make inferences from other similar operations to provide a variety of standard techno-economic
indicators (capital cost per tonne, operating cost, labour intensity etc.,) and to modify these within
plausible limits to suit the new deposit through correct scaling. This requisite simplification of early-
stage evaluation models can, in some cases, conceal details of the project that may present material risks
or conceal substantial upside. This essential simplification limits insights required to inform judgment
and constrains effective investment decision making.
The conceptual models of the deposit and attendant operation can be used to derive a number of metrics
to inform investor judgment (Samis, 2005), including:
Project metrics: The primary drivers of value of diamond projects include the
size of the deposit, diamond concentration, diamond size distribution and
diamond quality. The nature of diamond deposits makes each of these variables
difficult to determine with certainty, and hence the range of project value is a
product not only of the range of value for each of these underlying variables, but
also a function of the inter-dependant relationships with each other (Covariance).
Project structure: The location of kimberlites and associated mining projects often
requires collaboration from several investor parties. These structures change over
the life of the project and each set of stakeholders may have very different risk
tolerance levels and hence require different returns and guarantees. This impacts
markedly on the way in which projects can be funded, levels and forms of
funding.
Project value - The value of the project to various stakeholders (investor, owner,
country) may require very different valuation techniques to help inform
judgment and support subsequent investment decisions. Value metrics range
from discounted cashflow metrics so a variety of valuation techniques that can be
applied to value mining projects

3. 19
These project metrics are a necessary, but perhaps not sufficient, condition to convince potential
investors of a project’s potential future value. The broader perspective of value will take on different
nuances for each stakeholder group and communicating these (and their ranges) effectively remains a
pertinent challenge for early stage diamond project owners (Figure 1). In the post-resource definition
phase, valuation methods and reporting criteria are well codified (Valmin 2015, SamVal 2018, CimVal
2019). Whilst these codes are not strictly speaking applicable at this phase, their underlying concepts
are useful for guiding project owner actions and reporting with the aim of eventual resource declaration.
Figure 1. Summary of stakeholder expectations.
The emergence of a range of readily accessible data science techniques (predictive analytics, big data,
artificial intelligence, machine learning) that include a number of 'implicit' models that can be used to
explore and model relationships that exist in complex data sets, present several opportunities for early
stage evaluation. Their effective use however requires an organising framework that integrates with
more traditional and accepted approaches. The proposed framework balances the need for explicit
demonstration of value with an understanding (and communication) of the uncertainty (or range) that
is ubiquitously inherent in the evaluation of early stage mineral resource projects.
The successful use of this framework requires a clear link between the metrics produced and the way
these are used to inform a coherent aligned investment strategy. The strategy needs to inform decisions
that can be considered as 'options in projects' and 'options on projects'. The former refers to actions that
drive upside by sufficient understanding of technical aspects of projects and the latter to the insights of
the economics of a project and how these relate to the company’s tolerance for risk, and its ability to
raise and structure funding, develop a unique 'play' and its ability to create and deliver value.
Evaluation Practice in Diamond Projects
Project 'evaluation' differs from project 'valuation' in that the former aims to explore many more
dimensions of 'value'. It is common to derive a 'best' project, or 'base case' from a number of inputs and
calculations that are averaged. These may include:
• Average grade and average diamond values that represent large aggregates of the targetted
orebody,
• A mine plan that runs at an average rate for the life of the operation,
• Recovery and loss estimates that are also averaged out over the life of the operations, and
• Calculation of expected project value based on annualised cashflows that are then used in a
financial model.

4. 20
This approach can deliver simple, clear project metrics within a single operating configuration and life
of mine strategy. The downside of this approach is that it overly smooths the cashflows and reduces
the ability for decision makers to explore the value of risk mitigating strategies, the benefits of design
flexibility, agile market responses and the value that adroit management of these projects can deliver
(Samis, 2001). This shortcoming may result in incorrect value assessment and erroneous investment
judgement.
Several alternative approaches exist that allow a more granular approach to modelling, allowing a more
meaningful exploration of the range of value (upside and downside) that these projects may present.
The more useful of these are able to incorporate the material sources of uncertainty and allow these to
interact at the correct scale (both spatial and temporal) with the project’s flexibility and constraints.
Three sources of project uncertainty can be distinguished, which need to be incorporated in different
ways:
1- Spatial uncertainty – i.e., the deposit and resource model. Additional sampling can reduce
this uncertainty but will not change inherent variability. This aspect is incorporated via a suite
of spatial simulations of the mineral resource. These need not be restricted to grades and can
include, inter alia, mineralogy and geometallurgical parameters.
2- Operational uncertainty – this uncertainty arises from the interaction of the mineralisation
and a variety of aspects of execution of mining and processing. Flexibility and constraints are
set by the process configuration and these can be adapted by design and operational strategy.
The impacts of this type of uncertainty can be replicated and explored by stochastic process
simulation.
3- Future uncertainty – the context in which the project will run. Although this cannot be
predicted with any reliability, one functional method is to use the scenarios approach
developed by Wack, Sunter and van der Heijden (Van der Heijden, 2005). In this approach it is
possible to set up several plausible internally coherent futures in which the project could
operate. Operational outcomes (e.g., Capex, Op-cost and Cashflows) generated in each of these
can be compared and contrasted to select sets of decisions (operational policies and strategies)
that yield robust outcomes in the range of future scenarios.
This linked approach to evaluation allows numerous very different alternative project configurations to
be effectively analysed and stress tested in a scenario-based framework (Vann et. al. 2012,
Dimitrakopoulos, 2012, Dowd, Xu et.al., 2016). Adoption of this approach in early stage projects may
appear counterintuitive, but with deft use of a combination of relevant historic data, heuristic models
(e.g. geological mineralisation models) and limited sample information it is possible to develop a useful
analytical framework. These frameworks create a basis for ongoing and systematic approach to value
exploration to support judgment and investment decisions in early-stage projects.
Dimensions of Diamond Projects
Geological frameworks and mineralisation models
The geology of diamond deposits can broadly be split into primary (Kimberlites, Lamprophyres,
Orangeites) and secondary (alluvial, fluvial and marine). The conceptual framework models of these
deposits have to be relied on to underpin the regional, camp and deposit scale approaches to
exploration, discovery and evaluation.
Economically viable secondary deposits (alluvial, fluvial, marine) are typically characterised by very
low diamond concentrations. Morphology of the deposit, gravel size distributions and inferences of trap
site size and distribution are essential elements in effective exploration and evaluation of this type of
deposit (Prins, 2013). Kimberlites, the primary diamond host deposit type, are a relatively scarce
geological phenomena and are also mostly sub-economic (Kjaarsgaard, 2007). There are a number of
diagnostic tools that can be applied at early stages to rule out diamond content (e.g. thermo-bathymetry
etc.) but for those that contain diamonds, no such simple techniques exist. To quantify the grade
requires several large spatially selected samples to be acquired and treated. Effective design of

5. 21
sampling can be effective, provided that representative samples of each lithotype in a deposit have been
acquired and appropriately tested. This is however costly and requires funding that may not be readily
available to convert discovered deposits into declared mineral resources. It is in the "stepchild phase"
(Campbell, 2019) that this methodology becomes particularly pertinent.
Data derived from samples taken from the deposit (and its environs) can be used to generate estimates
of values at unsampled locations. Linear estimation techniques, such as kriging (Journel and Huibrechts,
1978) generate models that are smoother than reality but are useful and appropriate for developing
ultimate pit designs and long-term mining sequences. However, smoothed estimates are likely to
underestimate variability, especially in the shorter term. Kriged models (including non-linear kriging)
can also enable quantification of mineralisation uncertainty (Coward et.al., 2012).
An alternative approach to estimating spatial models of mineralisation characteristics is to use
geostatistical simulation, which generates unsmoothed spatial models. There are several algorithms that
can be used to generate geostatistical simulations of spatial mineralisation characteristics, including
sequential Gaussian simulation (Deutsch, 1992), the turning bands method (Journel, 1974) and pluri-
gaussian methods (Dowd et al, 2003). Within a given domain and at a specified scale, geostatistical
simulation reproduces the spatial variability (via the variogram) of the data and the distribution
(histogram) of the data. Conditional geostatistical simulation also honours actual data values at sampled
locations. It is possible to generate multiple realisations of the mineralisation, each with the variability
that will be encountered when mining. A sufficiently large set of realisations constitutes a model of
spatial mineralisation uncertainty, both globally and locally. It is also possible to simulate the geometry
of the envelopes into which the mineralisation and other pertinent properties are simulated (Deraisme,
J. & Field, M., 2006).
In many cases the estimation of diamond content requires that the estimated variable (e.g., grade in
cts/hundred tonnes) be a so-called composite variable that results from the combination of sample
variables and models (Stiefenhofer, 2018). For example, a sampled stone concentration (stnspm3) can be
converted to a diamond carat concentration through application of the size distribution and a cut off
size parameter (Ferreira, 2013). Even in early-stage projects with the correct approach to sampling, some
of these variables may exhibit sufficient spatial structure to facilitate estimation and with some
assumptions, spatial simulation (Armstrong, 1998). In this way it is possible to generate spatial models
of the deposit that have used both in value chain modelling of the project’s potential and also for
enhancing further sampling strategies as the models generated will clearly highlight where there is most
uncertainty.
Mining and Processing Models
Development of an ultimate pit shell, the mining sequence and the mining schedule require the
consideration of spatial mineralisation properties, process configuration and economic parameters. The
‘optimal’ mine plan yields the highest net present value (or the maximum of some other financial
indicator). This requires the aggregation of the mined material into larger parcels to make the
optimisation tractable. Thus, a smoothing step is required (if the inputs are kriged blocks, this is an
additional smoothing; Vann et al, 2011). Conventional mining optimisation assumes that the input
parameters are all deterministic, i.e., exactly known.
Furthermore, the solution of the long-term mine plan usually is defined of a number of ore parcels
(commonly shapes representing the geometry of annual excavations termed annual ‘push backs’ or
‘shells’) that are to be mined in a sequence over an extended period. Once the mine is operational, this
long-term optimal mine plan usually forms the basis for shorter term mine planning within each sub-
parcel to achieve a tonnes and grade profile in a shorter time horizon (e.g. months). This short-term
mine plan is necessary for operational planning and control.
In early exploration the prototype mine plan will usually be based on annual or at most quarterly
increments that are extracted in a logical manner. Although there are approaches to optimisation of the

6. 22
mine plan under conditions of uncertainty (Dimitrakopoulos et al., 2014, Dowd and Dare-Bryan, 2004),
currently block-scale optimisation is not practically applicable for real time optimisation of the sequence
of processing of block models. This is due to the fact that there can be in excess of several million blocks
(in large deposits hundreds of millions) each containing multiple (50 - 300) realisations for several grade
and physical attributes. Thus, in some cases, even in early exploration projects, it is possible to apply a
selection algorithm to find the ‘best’ blocks in a way that approximates a realistic mining path or
sequence through the application of relatively simplistic constraints.
Primary rock properties (such as mineralogy and bulk density) are generally additive and,
consequently, can be readily estimated and spatially simulated. This is not true for metallurgical
response properties (crushed rock particle density distribution), which are often inherently non-linear
and non-additive. Coward et al (2009) give a framework for classification of geometallurgical
parameters. Geometallurgical modelling should proceed by building spatial models of primary rock
properties which ‘drive’ key processing responses, and thus allow correct handling of non-linearity and
non-additivity. Spatially informed prediction of processing responses necessitates modelling the
linkage of primary properties of the in-situ mineralisation (e.g., mineralogy and grades) with
metallurgical performance measures at various scales (e.g., recovery and throughput). The concept of
scale in this context must encapsulate the physical scale of primary samples extracted (the sample
support), the scale of the selective mining unit (SMU) used in geostatistical simulations (the SMU
support) and the scale of the metallurgical tests from which the responses are inferred (what
metallurgists call ‘scale’).
Process simulation of an operating plant requires the use of calibrated unit process models. A popular
approach includes the use of population balance models where the balance of mass flows of material in
the circuit are used as a criterion for defining optimal solutions (King, 2001, Napier-Munn, 1992). The
model iterates until the differences in mass flows entering and leaving units in the flowsheet converge
to a specified minimum. This process is computationally intensive and it is currently unrealistic to run
these models to convergence for every block when block models contain in the order of, say, 250 000
blocks. The approach suggested here is to evaluate the process response to a number different
combinations of rock property inputs (e.g., rock strength parameters, different feed blends) until
sufficient data are acquired to model the relationship between input primary variables and the process
responses of interest (Coward et al., 2009). Machine learning methods are now more commonplace in
this process (PLS, SVM etc.,) but pragmatically, these can often be reasonably modelled by linear or
second-order quadratic functions (Brownlee, 2018).
Even with relatively few sampled points, and a few ore characterisation tests combined with history
from other similar operation, it is possible to analyse and predict the range of process responses that
may be encountered when treating a new orebody (Figure 2).

7. 23
Figure 2. Plot of derived relationship between proportion kimberlite and fractional diamond recovery (LHS)
and simulated recovery (RHS) (Adapted from Coward 2013).
As more data is gathered these models can be superseded either with explicit unit process models or
implicit calibrated machine learning models. In this way, it is possible to generate many such curves for
each of the required processes that can be sampled randomly for each iteration of the model. In this
way, operational uncertainty is introduced into the recovery model, in a way that is related to the
variability and number of observations that have been obtained. Using these models, it is possible to
simulate the process operation and recovery efficiency on a block-by-block basis. Figure 3 depicts the
daily production statistics, including the tonnes mined, carats recovered, end of day stockpile level, the
blocks mined and the diamond recovery factor.
Figure 3. Outputs from integrated process simulation.
Incorporation of future uncertainty
The resource inputs and physical outputs of the intended operation need to be assigned a value in some
realistic way. Most often the selling price of the diamonds recovered is one of the key drivers of project
value, but equally other factors such as grade mining cost and exchange rate may also have a material
impact. This suggests that the conventional practice of quoting a single net present value (NPV) output

8. 24
is deemed idealistic and, often, misleading. Conversely, running hundreds (or thousands) of stochastic
realisations to quantify uncertainty in each component may be excessively time-consuming and
expensive and could result in superfluous data that have little material impact on the NPV (Nicholas,
Coward et. al., 2005).
A useful approach for dealing with the complex uncertainty of future operating contexts is to use a
scenario planning approach (Van Der Heijden, Bradfield 2005). In this approach the scenarios are used
to constrain the number of permutations of variables used in the study. The scenarios are thus defined
as a limited set of future contexts in which the project will operate. The values of external parameters
over which the project team has no control are required to be consistent with the scenario logic.
Parameters include diamond price trajectories, exchange rates, tax regimes, royalties, input costs and
cost of capital. In early-stage diamond projects, the creation of relevant sets of plausible scenarios in
which project configurations are tested allows for competing viewpoints to be incorporated in models
used to inform judgement, and hence improve the quality of the processes used for investment decision
making.
Evaluation, Sequencing and Synchronicity of Investment Decisions
The investment required to convert discoveries to classified reported resources is substantial. The
process is not simple and requires several steps. The scope of work required, duration and cost of each
step can to some degree be defined by projects owners. This managerial flexibility has value, provided
that project owners are able to quantify the financial implication of selecting different development
strategies.
Early stage diamond projects progress through a cycle of data acquisition rapid processing of the data,
and then information release to the market. This process needs to synchronise information release with
funding raising, and results in the 'standard curve' for junior exploration companies where value is
created if the project development increments continue to yield information that is valuable.
This phase of the project development is typically funded by the founders, management and possibly
high-net-worth individuals (HNWIs), and the investment is relatively small compared to resource
definition. Incubators, such as the Hunter Dickinson Group, Lundin Group and 162 Group are also
particularly important at this stage. Typically, at this stage, investors look for a significant short-term
uptick in the share price on the back of a discovery (Campbell, 2019). Following discovery, the ‘allure’
of a project reduces, and the early stage investors tend to exit, leaving the company the challenging task
of raising funds for resource definition programmes, which in today’s market is extremely challenging.
If the project does not have the potential for large diamonds, Type II’s or coloured stones, then funding
would be almost impossible unless there are other significant commercial mitigating factors.
The approach suggested here produces a range of production and financial outputs that are constrained
by the use of plausible models. This ranged approach suggests that the increasing levels information on
the deposit can be rapidly used to explore changes to configuration of the intended operation that are
value adding. These can be confidently reported knowing that they are underpinned by a structured
evaluation platform.
The adoption of an integrated value chain modelling approach in these projects also allows owners to
explore and potentially harness the value of the options that they have to balance unsystematic risk
(project specific) and systematic (economic) risks e.g., exchange rate risk through a variety of
mechanisms such as foreign exchange hedging (Nicholas, 2014).
Underground Application
Kimberlites occur in several morphologies including dykes, sills and pipes. The underground mining
of dykes and fissures has in some cases been carried out for over 70 years (Gurney, 1996). The dykes
usually occur as dyke systems or groups of en-echelon lenses which pinch and swell along strike and
down dip. The associated mining operations are usually constrained by the thickness and geometry of
the kimberlite. These physical aspects constrain the mining rate and often drive variations in ore loss

9. 25
and dilution. Wide spaced sampling in these environments can lead to assumptions that oversimplify
their geometry and result in overly optimistic mine plans and treatment rates.
Project configuration
In this specific case three approaches were used to evaluate the project: 1 - a sensitivity analysis where
the variables dilution, tonnage throughput and recovery were changed by ±5%, 10% and 15% from their
expected values; 2 - Monte Carlo simulations were run on the same variables using expert opinion to
parameterise the distributions of the input variables; 3 - an IVCM was developed. The IVCM was used
to compare the benefits and limitations of the evaluation methodologies (Nicholas, et. al., 2006).
The sensitivity analysis study used the base case financial model and flexed the inputs individually by
the percentage range selected by industry experts. The outputs from each trial were recorded and hence
a sensitivity table and plot could be generated.
The Monte Carlo analysis targetted the same variables, by instead of flexing each of the variables in
sequence, the distributions for each of the input variables were calibrated in consultation with domain
experts that were familiar with the project. To generate the required output, the financial model was
run several 1000s of times using values for the input variables that were drawn at random from the
calibrated distributions.
The IVCM approach requires several equally plausible ore body models, a mining treatment model and
a linked financial model that is used to provide inputs to the value chain model and to evaluate its
outputs.
To assess the geological variability of the project, face maps of the dyke from development tunnels were
used to model the variability of the thickness, shape and vertical location of the dyke. This data was
used to generate 25 estimated and 25 simulated ore body models.
The impact of geological variability on mining and treatment was assessed by linking the orebody
models to a dynamic simulation of the mining and metallurgical operation. The planned underground
operation was based on a conventional room and pillar design with an option of slashing or drifting for
ore extraction. The sequence of the metallurgical plant flowsheet followed conventional design
principles. The dynamic simulation responded to a variety of delivered ore characteristics, including
variable, block-by-block, dilution. The diluting rock types, and their proportional content, had an
impact on processing rate, diamond liberation and recovery efficiency.
The scenarios contemplated in this model included three diamond price trajectories and one for the
exchange rate. As the model operates at a highly granular block scale, it is possible to collect and report
information at a high resolution and then cumulate these to a variety of time horizons (days, weeks,
months and years). For this reason, the IVCM is referred to as a bottom-up method, rather than the two
other methodologies which by default operate on an annual basis and represent so called top-down
methods.
Outcomes
The sensitivity analysis model produced NPVs that ranged from -5 $M to 200 $M. An analysis of the
sensitivity curves might suggest that given the slope of the throughput curve is steepest, this factor is
the most important risk. This approach to modelling does not give any indication of which variable is
most likely to vary the most, nor does it allow an investor to evaluate the consequence of correlations
that are likely to exist between the changes of the values of these variables.
The Monte Carlo analysis is perhaps more informative, in this case reporting NPVs that ranged from 50
$M (5% of cases less than this value) to 154 $M (5% of cases above this value). The output is more
granular and the shapes of the NPV distributions can be compared to give a sense of probabilistic
interpretation to the range of output resulting in reasonable assumptions of impact of the ore body
variability on the project outcomes. The granularity of approach is however also somewhat limited as
it is not possible to drill down beyond the period of the financial model (annual increments). It is also

10. 26
subject to the decision making biases and challenges that are faced when eliciting expert opinion to
calibrate, and correlate, the input distributions (Aspinall, 2018).
The IVCM approach produced NPVs that ranged from 28 $M to 33 $M - a far smaller range than either
of the above methods. Although it is more time consuming to develop and run, the IVCM provides a
platform to build relationships between the resource, mining, processing, and the financial model. At
each one of the steps in the value chain it is then possible to investigate the impact of constraints and
flexibility, and explore the cost and benefit, in financial terms, of mitigating the risk or releasing the
constraint. The platform also allows the use of a range of complexity of processing models to be used at
each stage in the project’s development. The project model, (or digital twin) which might start out as a
few simple transfer functions can evolve in step with the intended project as the definition of the project
improves.
Open Pit Application
Project Configuration
In this specific project there was sufficient sampling information to constrain the resource grade;
however, as is often the case in diamond projects, the upside of diamond revenue, treatment rate and
recovery efficiency was relatively undefined.
An ore body model was developed using a combination of geostatistical estimates and several
geostatistical simulations of the grade, bulk density, diamond value and concentrate yield. The
estimated (kriged) ore body model was used to develop the ultimate pit, pushbacks and sequence the
pit.
The impact that bulk density, ore hardness and the density distribution of the crushed rock would have
on the process was a material geometallurgical consideration. The mining model and process models
were designed to respond to changing ore characteristics (e.g., changes in ore hardness results in change
in blast design, changes in ore feed size distribution changes liberation and concentrate yield).
The financial model was directly linked to the production model to cost inputs and generate a cashflow
stream for each of the ore body realisations that were processed. To account for diamond selling price
uncertainty, the revenue achieved for each production parcel of diamonds was based on a stepwise
simulation of the value of each diamond produced. Given the modelled size distribution of each block
processed, it is possible to generate the number of stones in each block. Each stone is then allocated to a
size category. The value of the stone is then derived by sampling from the value distribution in the size
class. In this way the full potential range of the value of diamonds produced in each period is reflected
in the model.
Outcomes
Processing of the kriged ore body model through the IVCM forecast an NPV of ~100 M$ with an internal
rate of return of ~11%. Running multiple orebodies through the model identified that there was indeed
potential upside from the larger stones, but that mineral processing would remain a risk that would
increase through the life of the project (Figure 4).

11. 27
Figure 4. Cumulative discounted cashflow for the configured project depicting cash flows for three scenarios.
The benefit of running the evaluation model at block scale is that it overcomes the perceptions of
‘smooth’ production that is created from the use of larger increments of physical scale and time. The
impact of daily variability on process performance may cumulate to have a material impact on annual
outputs. This will not be detected if characteristics of annual increments of ore are used in modelling.
The higher granularity of this approach also uncovers upside that may lie in better selection and
sequencing of blocks, or design and implementation of technology to deal appropriately with the
variability that will be encountered.
In this case study the IVCM approach identified that the uncertainty in cashflow from the two and three
years of the project had most impact on a variety of financial risk indicators (e.g. cash flow available for
debt servicing) and overall uncertainty in the NPV. This highlighted the need for accurate planning to
ensure delivery of ‘right tonnes and the right time during this period. This resulted in design and
implementation of additional storage capacity in the process to facilitate blending and remixing of ore
types that were indicated as having low throughput, liberation and high yields. This investment in
flexibility lowers the perceived NPV if the project is evaluated in a traditional annualised discounted
cash flow model, but increases the expected NPV when an IVCM approach is adopted.
CONCLUSIONS
Exploring for and discovering kimberlites requires very astute decision making. Once the discoveries
are shown to contain diamonds, then an even greater level of adept thinking, energy and resources are
required to define resources and then transform these discoveries into viable mining projects.
Kimberlites present early stage project owners and investors with a considerable challenge. Early stage
projects are characterised by a lack of data but also have maximum flexibility. This juxtaposition of
upside and downside needs to be systematically translated into a tangible realistic evaluation.
Evaluation needs to be executed in a way, and in a time frame, that allows stakeholders to engage with
the project, take a view and make investment decisions.
In the absence of time and resources, simplistic realistic cashflow models can help experienced project
owners to identify which areas of the project require most attention, but caution must be exercised as
these do not produce realistic granular valuation outputs. Many of the engineering aspects of the project
can be optimised (pit shell, mining costs, process design) to exploit flexibility, and adapted to
accommodate improving information; the inherent grade and variability of the ore body cannot.
DCF
US$
Million
Year
Annual Discounted Cashflow

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Monte Carlo analysis, and to an extent sensitivity analysis, using a detailed financial model will provide
a degree of insight into the relationships that exist within a project, but these are vulnerable to the
impacts of so-called double or triple smoothing, and interrogation of value cause and effect is limited
to the resolution of the financial model. The impacts of averaging may conceal important risks that
require mitigating actions and prevent project owners from exploring and harnessing upsides in their
projects.
The integrated value chain approach when supported with a suitable platform such as SBPE in which
models of appropriate sophistication can be built provides information to support improved investment
judgment. These platforms allow for the inclusion of modern data science techniques presenting project
owners and sponsors with an opportunity to fully explore, characterise and articulate their value
proposition. It is possible to communicate this value potential to a variety of project stakeholders
including owners, investors and interested and affected parties.
The methods presented here are characterised by their:
• Sufficiently granular nature;
• Bottom up integrated, holistic approach to the entire value chain, craton to crown approach;
• Deal appropriately with three main dimensions of uncertainty resource/process/future
uncertainty: and
• Provide a dynamic platform that creates a continuous record of the project that is scalable,
defendable and auditable.
Tools and techniques for implementing data-rich, science-based technologies (AI, machine learning,
deep learning, internet of things, Industry 4.0) are become more prevalent in exploration and continue
to reduce the costs, increase efficiencies and underpin success of a host of mining and processing
techniques. Their use should be actively pursued in evaluation of new and old discoveries and decision-
making theory and practice. Using these techniques should strive to bring greater clarity and trust to
investors through more accurate assessment, rapid and transparent disclosure and realisation of latent
project value.
Exploration for and discovery of new orebodies plays an important role in the success of mining
industries, and societies around the world. Increasing diverse funding models and sources of capital
can be accessed, provided the total value (in a broad sense), and risks to potential investors, and all
stakeholders can be clearly conveyed. A variety of rapidly evolving methods should be embraced and
rigorously validated to ensure that projects in the post-discovery, waning attractiveness ‘stepchild’
phase, continue to be able to arouse sufficient interest and investment from long term beneficiaries.
These groups range from stakeholders such as large corporates selling interest rates, and governments
that may provide tax concessions, to funding through vehicles such as minerals development boards
and the Industrial Development Corporation.
Improved richer, clearer evaluation of the range and variability of return not only opens a variety of
avenues for funding but should translate into a more robust supply of new projects. The success of those
that are able to deliver new and exciting maiden resources will be achieved through optimal addressing
of risks, identification of opportunities and liberation of most value for all stakeholders.
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Stephen Coward
Interlace
A mining industry professional, with experience in identifying and harnessing strategic opportunities
that arise from improved integration of technical, financial and human facets of the mining industry.
Skilled in holistic value chain analysis, modelling and simulation which has repeatedly demonstrated
material increases in operational resilience and underpins robust returns from mining projects.
A broad systemic understanding of the drivers of value in this industry have been developed through
a career that has spanned mining operations, project management, corporate support and broad
technical consulting. His quantitative analytic skills, sound reasoning and management credentials
bring a dynamic flavour to his research and ongoing training and mentoring offerings.
In addition to qualifications in metallurgy, Steve has a B.Comm. from UNISA, an MBA from WITS and
a PhD from The University of Adelaide