1
25-057
Mine Plan Risk Assessment and Grade Uncertainty
Characterization Using Geostatistical Conditional Simulation:
Gold Mine Case Study
Steven Hoerger
Peak View Mine Planning, Englewood, CO
Kadri Dagdelen
Colorado School of Mines, Golden, CO
SUMMARY
For gold mines, orebody uncertainty is one of the leading
causes of differences between planned and actual produc-
tion. Geostatistical conditional simulations create multiple
equally probable orebody block model realizations which
are conditional to the current drilling information and
demonstrate the sampled grade distributions and spatial
statistics. These simulated block models are key inputs for
a stochastic evaluation to show the likely range of grades
and cash flows to be delivered by a mine plan. The sto-
chastic evaluation outputs can be used to compare different
mine plans in terms of expected values and risks to planned
grades, production, costs, Net Present Value (NPV) and
other plan metrics.
For the McLaughlin gold mine, risk profiles are dem-
onstrated to show how different mine, cut-off and stockpile
strategies can reduce risk and improve expected value. The
strategies considered were effective in improving expected
NPV and reducing or eliminating tonnage uncertainty, but
the strategies had little impact on reducing the uncertainty
of NPV’s or mill grades. The stochastic evaluation process
is also used to compare the risk impact of orebody uncer-
tainty against the risk of not achieving planned mining
tonnages or vertical advance rates. Risks for plans created
with and without stochastic mine plan optimization are
also compared.
INTRODUCTION
Mine planners seek to create production schedules to
maximize the value generated from an orebody subject to
mining and processing constraints and defined cost struc-
tures. Additionally, the mine planner wants a plan which
will be reliably achieved. A key component of creating
reliable plans is in choosing mining and processing con-
straints that can be consistently achieved over long periods
of time. Another key component of plan reliability is the
inherent grade uncertainty in the orebody. Understanding
grade uncertainty allows for a better understanding of plan
reliability.
An open pit production schedule defines which loca-
tions are mined when—the mining sequence—and which
mined material is sent to which destination at which
time—the processing sequence [Clark and Dagdelen,
2023]. If there is no grade uncertainty at an operation with
a single processing plant, a process schedule is often defined
in terms of an operating cut-off grade: material above the
cut-off grade is sent to the process plant and material below
the cut-off grade is sent to a stockpile or waste dump. The
operating cut-off grade ensures that material above cut-off
generates sufficient revenue to pay for processing costs plus
the opportunity cost of delaying the processing of future
material [Rendu, 2014]. Over time, this opportunity cost
declines toward zero, so cut-off grades typically decline over
time to a breakeven cut-off where generated revenue equals
processing costs [Lane, 1988].
With grade uncertainty, if a mining sequence is fixed,
and the actual amount of material above the planned cut-
off is more or less than planned, cut-offs can be adjusted
up or down to keep the material above cut-off balanced
with the process capacity. However, a cut-off would not
normally be adjusted below the breakeven cut-off at break-
even cut-off, the process plant would be operated at or
below full capacity. By understanding the orebody’s grade
uncertainty and grade distributions, it is possible to predict
25-057
Mine Plan Risk Assessment and Grade Uncertainty
Characterization Using Geostatistical Conditional Simulation:
Gold Mine Case Study
Steven Hoerger
Peak View Mine Planning, Englewood, CO
Kadri Dagdelen
Colorado School of Mines, Golden, CO
SUMMARY
For gold mines, orebody uncertainty is one of the leading
causes of differences between planned and actual produc-
tion. Geostatistical conditional simulations create multiple
equally probable orebody block model realizations which
are conditional to the current drilling information and
demonstrate the sampled grade distributions and spatial
statistics. These simulated block models are key inputs for
a stochastic evaluation to show the likely range of grades
and cash flows to be delivered by a mine plan. The sto-
chastic evaluation outputs can be used to compare different
mine plans in terms of expected values and risks to planned
grades, production, costs, Net Present Value (NPV) and
other plan metrics.
For the McLaughlin gold mine, risk profiles are dem-
onstrated to show how different mine, cut-off and stockpile
strategies can reduce risk and improve expected value. The
strategies considered were effective in improving expected
NPV and reducing or eliminating tonnage uncertainty, but
the strategies had little impact on reducing the uncertainty
of NPV’s or mill grades. The stochastic evaluation process
is also used to compare the risk impact of orebody uncer-
tainty against the risk of not achieving planned mining
tonnages or vertical advance rates. Risks for plans created
with and without stochastic mine plan optimization are
also compared.
INTRODUCTION
Mine planners seek to create production schedules to
maximize the value generated from an orebody subject to
mining and processing constraints and defined cost struc-
tures. Additionally, the mine planner wants a plan which
will be reliably achieved. A key component of creating
reliable plans is in choosing mining and processing con-
straints that can be consistently achieved over long periods
of time. Another key component of plan reliability is the
inherent grade uncertainty in the orebody. Understanding
grade uncertainty allows for a better understanding of plan
reliability.
An open pit production schedule defines which loca-
tions are mined when—the mining sequence—and which
mined material is sent to which destination at which
time—the processing sequence [Clark and Dagdelen,
2023]. If there is no grade uncertainty at an operation with
a single processing plant, a process schedule is often defined
in terms of an operating cut-off grade: material above the
cut-off grade is sent to the process plant and material below
the cut-off grade is sent to a stockpile or waste dump. The
operating cut-off grade ensures that material above cut-off
generates sufficient revenue to pay for processing costs plus
the opportunity cost of delaying the processing of future
material [Rendu, 2014]. Over time, this opportunity cost
declines toward zero, so cut-off grades typically decline over
time to a breakeven cut-off where generated revenue equals
processing costs [Lane, 1988].
With grade uncertainty, if a mining sequence is fixed,
and the actual amount of material above the planned cut-
off is more or less than planned, cut-offs can be adjusted
up or down to keep the material above cut-off balanced
with the process capacity. However, a cut-off would not
normally be adjusted below the breakeven cut-off at break-
even cut-off, the process plant would be operated at or
below full capacity. By understanding the orebody’s grade
uncertainty and grade distributions, it is possible to predict