CAPITULO 9 Y 10

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Society of Economic Geologists
SP3, 2012, p. 105–112
Chapter IX
Mine Grade Control
Objective
SEVERAL standard texts discuss the role of geology at an operating mine (e.g., McKinstry, 1953; Peters, 1978), and the
role of geology within the organizational structure of a mining company. However defined, and whatever the reporting
responsibilities, most geologic work at an operating mine
usually falls under the general category of “grade control,”
and serves as a central source of predictive information to
other areas of the operation.
At most mines, the grade control program has three
principal goals:
1. To forecast the grade or physical characteristics of the
material to be mined during the next month or year. This is
a cooperative effort involving all of the mine staff, and is
the first step in preparing the financial projection for the
period in question. This function is obviously simply an
extension of the estimates prepared in the feasibility stage
of the project.
2. To ensure that the material mined as “ore” is acceptable to the mill and purchaser, and that it is separated during mining from nonprofitable material to the extent possible. Again, this effort requires close cooperation between
geological, engineering and operations personnel, and
often involves trade-offs from the ideal for any or all of the
parties involved.
3. To supply the operating staff with reasonably accurate
outlines of the orebody, so that development work can be
laid out in such a way as to make available the maximum
amount of ore at the least cost.
Note that the role of the geological staff at an operating
mine, as previously mentioned, is not so much the discovery of new ore (although this may be an important part of
the job), as it is to help insure the profitable utilization of
the given reserve. Assuming that the initial feasibility
reserve is adequate to amortize the capital investment and
to return the anticipated profit, extending the reserve
(again because of the present value factor), is generally less
important than is the effort to wring the maximum profit
from the initial reserve.
In a review of the geological activities at the Grasberg
mine, Arnold and MacDonald (1995) categorize the Mine
Geology Department as a service group whose corporate
“clients” consist of the Operations, Sales, and Financial
departments of the company. At Grasberg, the Geology
Department is responsible for five general areas of investigation:
105
Geochemistry:
8 Base and precious metals
10 Major elements
41 Trace elements
Corporate clients include:
Mill Operations
Environmental
Concentrate Sales
Exploration
Mineralogy:
Sulfide, Oxide, Silicate
Corporate Clients include:
Mill Operations
Environmental
Resource:
Au, Cu, Ag grades
Bulk density
Costs
Tonnage
Value
Corporate clients include:
Mill Operations
Mine Operations
Sales & Finance
Metallurgy:
Recovery
Concentrating Characteristics
Corporate Clients include:
Mill Operations
Concentrate Sales
Rock mechanics:
RQD
Rock Strength
Structure
Lithology
Corporate Clients include:
Mine Operations
Mill Operations
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As noted by the authors, not all of these elements are
important to each of the corporate clients—although Mill
Operations may, for example, be concerned with all of
these areas, the Mine Engineering group normally is less
concerned with Geochemistry and Metallurgy than with
those areas such as slope stability and blasting characteristics that directly influence the mining process. Similarly, the
Exploration Department is less concerned with the
resource or metallurgy of the existing deposit than with
those areas such as Geochemistry and Mineralogy that may
yield clues to the “signature” of possible new ones.
In most instances, both initial resource estimates and
subsequent reserve estimates are presented in some sort of
block value format, in which the overall resource is subdivided into smaller units based on location, grade, mineralogy, anticipated profitability, and the like. Each block will
be assigned some sort of location code, as well as the relevant data, such as tonnage, grade, vein thickness, or metallurgical recovery, from which the economic merit of the
block can be derived. The blocks commonly coincide with
the anticipated benches or mining levels, and, if of uniform
size and shape, may include either the position of the
ore/waste contact, or the proportions of ore and waste
expected within the block.
Resource blocks are estimated initially from exploration
results, and as additional information becomes available,
the blocks will be updated, and eventually move into the
category of the probable or proven reserves upon which
project feasibility will be based. As the mine moves toward
production, proven reserve blocks will in turn become
developed or operational reserves that form the basis for
various production forecasts. In most instances, short-range
forecasts of mine production are based on the results of
detailed sampling (blast holes, development drill sampling,
face or bank samples, etc.), not available at the exploration
or even the feasibility stage, and may be further modified to
reflect the results of reconciliations between production
forecasts and actual operational results.
During the progression from resource blocks to reserves
and ultimately to developed or operational reserves, it is
critical that a record be kept of the changes (and of the reasons therefor) in the status of individual blocks or groups of
blocks. In particular, it is important to document the proportion of resource blocks that actually convert to developed reserves, in order to be able to accurately evaluate
resource (or even undeveloped reserve) blocks outside the
immediate mining area. Furthermore, the operational
parameters of any given mine are rarely static over time,
and changes in operating procedures will almost certainly
have an impact on the economic evaluation of both poorly
known resource areas as well as reserve blocks not yet developed in advance of actual mining.
In recent years, there has been a concerted effort to
improve the ore/waste selectivity through geostatistical
analyses of blast hole data (although itself somewhat suspect), and to identify possible sources of bias in short-range
estimation (Kwa and Mousset-Jones, 1986; Guardiano,
1990; Knudsen, 1992; Sims and Goodwin, 1992; Rossi and
Parker, 1993; Rossi et al., 1993; Douglas et al., 1994; Pan,
1994; etc.), particularly in low-grade, open pit disseminated
gold operations. These techniques, coupled with highly
selective mining, have significantly improved the ore/waste
selectivity at many of these operations, but are, unfortunately, after-the-fact approaches. The fact that even careful
geostatistical analysis of sample data from blast holes at
approximately 15-ft spacings still cannot guarantee a 100
percent separation of ore and waste blocks of blast hole size
does not inspire confidence in block predictions based on
widely spaced exploration sampling.
Consider, for example, the results of blast hole sampling
at the low-grade alkalic porphyry copper deposit at Ingerbelle in British Columbia. Results from blast hole drilling in
an area scheduled for mining indicated that the northern
part of the area to be blasted was ore, and that the southern
part of the area was waste. The holes were loaded, but the
explosive froze before the blast could be shot. The block
was redrilled and resampled, and, mirabile dictu, the northern portion was waste, and the southern was ore!
Although often considered as ground truth in reconciliation studies (especially those undertaken on a block-byblock basis), blast hole samples themselves may well introduce errors into the comparison. In most operations it is
standard practice to extend blast holes below the bottom of
the underlying bench in order to provide uniform fragmentation at the bench elevation. If blast holes are to be
used as the primary source of grade control information,
only that part of the hole actually corresponding to the
bench height should be sampled, especially if there is a vertical trend in grade. Also note that the subgrade portion of
the hole would otherwise be sampled twice—once by the
holes drilled in the block above a particular bench elevation, and again by holes drilled on the bench itself. In addition, operational considerations may preclude collection of
reliable samples from blast holes. Satisfactory sampling is
particularly difficult in wet holes.
Figure IX-1 illustrates the results of a series of multiple
samples from the approximately two tons of cuttings from
a single blast hole drilled in a low-grade copper-gold operation. The value in the solid rectangle on the histogram
(0.004 oz/ton Au) represents the sample collected by a
mechanical sampler on the drill rig; at this operation it
would have been the value assigned to the area represented
by that blast hole.
Eighteen additional samples, represented by the open
rectangles, were taken from opposite halves of the muck
pile using various standard sampling techniques. These samples have grades ranging from 0.004 to 0.008 oz/ton Au.
Those from one half of the pile averaged 0.005 oz/ton Au,
and those from the opposite half averaged 0.006 oz/ton Au.
Finally, all the remaining material from each half of the
pile was collected, and the two roughly 1-ton bulk samples
were processed and analyzed. The two results, 0.004 and
0.008 oz/ton Au, are shown on the figure by the crosshatched pattern. The bulk sample with a grade of 0.004
oz/ton Au came from the half of the pile averaging 0.005
oz/ton Au, and the sample with a grade of 0.008 oz/ton Au
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Number of Samples
ORE RESERVE ESTIMATES IN THE REAL WORLD
8
6
4
2
0.004
0.005
0.006
0.007
0.008
OZ/TON Au
FIG. IX-1. Histogram of multiple samples from a single blast hole.
came from the half of the pile averaging 0.006 oz/ton Au.
From the histogram, it would appear that a reasonable
value for the block might be about 0.005 oz/ton, but that
we have only a roughly one in three (8 in 21) chance of
actually obtaining a single sample with this value from any
given blast hole. The copper grades determined from the
same set of samples showed a similar spread of values, albeit
with a lower percentage variation.
Published studies of grade control procedures often
compare two estimation techniques, for example, two kriging techniques, using blast hole assays as an umpire to
determine the “best” estimator. As discussed previously, neither estimator of the in situ reserve may be appropriate for
estimating the tonnage and grade of the material actually
extracted, and the reliability of blast hole samples themselves may leave much to be desired. The ultimate umpire
remains the mill feed grade (Manns and Ellingham, 1992).
Not only is it unbiased, but it also pays the bills. A quote
from Miller and Giroux (1986, p. 270) summarizes a study
of the type referred to above:
Since it is impossible to prove which estimation procedure is closest to reality without tracing the estimated
material through the mill, the final conclusion for the
best estimation technique remains unresolved.
The reliability of any grade control sampling program
clearly needs to be carefully checked, not only against actual
production results, but by analyzing the repeatability of the
samples themselves. Again, very few reconciliations based on
blast hole data take replacement dilution into account.
Reconciliation of Reserve Estimates
It is obvious, but unfortunate, that the true tonnage and
grade of any deposit is known only after final closure of the
mine. It is equally obvious that such a before-and-after comparison is of no use whatsoever in the evaluation of the
property prior to mining or in mine planning during the
operation. Comparison of the final production figures with
estimates produced before and during the life of the operation can be useful in determining how the estimates and
the evaluation might have been improved. Note, however,
that any reconciliation of final production with premining
reserve figures must take into account any changes in
cutoff grades during the life of the operation, as well as
operational changes, including changes in grade control
procedures, that may have affected the economics and
therefor the ultimate reserve of the operation. For this reason, a detailed reconciliation between the premining
reserve estimate and final production figures may be
extremely difficult. Even if produced, such detailed comparisons are seldom made available to the general public.
The two papers by Crawford (2003, 2004) provide a succinct summary of the problems encountered in the reconciliation of production results with forecasts. Crawford lists
three steps in the reconciliation process, any or all of which
may prove problematic in practice:
1) Preproduction model to actual mine production;
2) Actual mine production to mill production;
3) Actual mill production to sales receipts.
Two types of reconciliations need to be made during the
course of the operation. The first are economic reconciliations over specific time periods such as the performance
statistics and the cost sheet shown in Figure II-1. These
compare estimated budget projections with actual costs of
production and are used to track the profitability of the
mining venture. The second type is a separate volume-byvolume reconciliation of tonnages and grades estimated by
the mine compared to the measured production; these are
used to determine the reliability of the reserve estimates.
In most operations, it is standard practice to reconcile
forecast and actual production on a monthly or yearly basis,
and to adjust future predictions in light of empirical factors
that have been found to reconcile the observed differences.
Table IX-A presents the year-by-year reconciliation between
TABLE IX-A. Groveland Mine:
Comparison of Actual Production and Estimates
1970
1971
1972
1973
1974
Total Fe
Actual
Original est.
After-the-fact est.
Variance*
34.8
35.0
35.0
–0.2
34.7
35.2
35.2
–0.5
35.2
34.9
35.1
+0.1
34.8
34.6
35.2
–0.4
34.5
34.9
35.2
–0.7
% Magnetic Fe
Actual
Original est.
After-the-fact est.
Variance*
55.5
55.2
58.0
–2.5
58.6
53.7
60.2
–1.6
53.1
55.9
53.0
+0.1
60.7
62.7
58.5
+2.2
61.0
66.2
63.3
–2.3
% Weight Recovery
Actual
Original est.
After-the-fact est.
Variance*
42.6
42.0
42.9
–0.3
43.6
43.0
43.2
+0.4
44.3
42.8
42.8
+1.5
44.2
42.8
43.2
+1.0
44.4
42.8
43.1
+1.3
Grinding Rate (LTPH)
Actual
Original est.
After-the-fact est.
Variance*
626
610
627
–1
599
619
614
–15
612
624
626
–14
600
620
625
–25
620
621
632
–12
*Actual - After-the-fact estimate
Note: “Original” estimates made in December of the preceding year.
“After-the-fact” estimates made at the end of the year based on the area
actually mined during the year.
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STONE AND DUNN
pre- and post-mining production estimates compared with
actual figures reported by the Groveland mill. Note that
there is a definite tendency for the mill to report slightly
lower figures for grade than even the “after the fact” mine
estimate. The slight positive difference between estimated
and actual weight recoveries is probably due to the use of a
tonnage factor that was a little too low. The large discrepancy in the predictions of grinding rate suggests a problem
with the procedures used to extrapolate test work on drill
core to plant performance. Such empirical factors are usually referred to as assay adjustment, mine call factor, swell
factor, dilution factor, and the like, but are simply numbers
or procedures designed to force a reserve calculation to
match the observed mill feed. At the Los Bronces operation, for example, blast hole grades are simply discounted
by 3 percent to arrive at predicted mill feed (Godoy, 1992).
Unfortunately, the reconciliation process too often dissolves into acrimony, with mill and mine each accusing the
other for any shortfall in production or profitability, and
both blaming the geological staff for failure to accurately
predict the characteristics of the block of ground actually
extracted. Even worse, this sort of factor can be determined
only after the capital investment in mine and mill has been
made. King et al. (1982) cites the situation at an Arizona
porphyry copper operation in which it took 20 years to
arrive at a satisfactory procedure for ore reserve prediction!
Reconciliation of predicted vs. actual production results
is often surprisingly difficult (Sides, 1992). Ore from the
mine may be stockpiled before treatment, with a lag time of
several days or even weeks between mining and milling.
Estimates of the material to be mined are normally based
on separate estimates of volume and density, while the mill
feed is based on direct weight measurements (which
include variable amounts of moisture). Mine production
may come from several sources, with the tonnage from
each only known as a volume proportion, rather than as a
measured weight. Likewise, a portion of the material mined
may be sent to a lean-ore stockpile, measured only by truck
count and an assumed grade rather than weighed and
assayed. Sampling of coarse material fed to an autogenous
mill or sent to a heap leach pad is notoriously difficult.
Within the mill itself, the tonnage of material reporting to
tailings is usually arrived at by simply subtracting the tonnage of final product from the tonnage of crude feed, and
in a surprising number of instances, the actual feed grade
itself is a calculated number based on an assumed or historical unit recovery. Tailings analyses are seldom reliable
in a heap leach operation, and even in a carbon-in-pulp
gold operation may be influenced by a “nugget effect”
caused by the escape of an occasional large but high grade
fragment of carbon. Short term predictions of process
recovery will be influenced by the fact that the efficiency of
the processing plant itself will vary with time and maintenance schedules.
Table IX-B presents an idealized list of the parameters
that must be reconciled at various points in the process,
together with some of the data required. In this instance,
the flowsheet envisions a relatively simple operation producing a concentrate that is then sold to a custom smelter.
Note that in many operations (particularly heap leach operations) where one or more of the measurements are missing, the only truly reliable figure in the entire exercise is
the final smelter return.
Given this situation, and in light of the difficulties mentioned above, it is highly improbable that short-term forecasts and actual production will always agree, and even
after-the-fact re-estimation of blocks actually mined (vs.
forecast) will usually show discrepancies. Figure IX-2 illustrates a month-by-month reconciliation between plant and
mine for crude grade at the Pilot Knob operation.
Although the discrepancy for any given month may be
rather large, over time, these discrepancies should fluctuate
PILOT KNOB PRODUCTION ESTIMATES
Magnetic Fe in Plant Feed
Plant Estimate – Mine Estimate
1972
1973
1974
+3.0%
+2.0%
+1.0%
0
-1.0%
-2.0%
-3.0%
-4.0%
-5.0%
-6.0%
Flotation Installed
-------------Average error 1972 - mid 1974
FIG. IX-2. Pilot Knob production estimates.
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ORE RESERVE ESTIMATES IN THE REAL WORLD
109
TABLE IX-B. Factors to Consider in Reconciling Predicted vs. Actual Production
RECONCILIATION
PARAMETERS
DATA REQUIRED
Mine/Mill
Grade
Tonnage
Contained metal
Analyses (e xploration and development)
Pit survey of area mined
Tonnage Factor(s)
Ore types
Disposition (ore, waste, etc.) by truck count
Stockpile balance (may require measured
weights into and out of pile)
Measured tonnage of mill feed
Analysis of mill feed (including moisture)
Reser ve Estimate/
Mine & Mill
Grade
Tonnage
Contained metal
Block model of reserve
Pit survey of area mined
Tonnage Factors
Ore types
Analysis of mill feed
Disposition (as above)
Measured mill feed rate
Grinding rate
Mill Feed/
Concentrate
Metallurgical recovery
Contained metal
Reser ve Estimate/
Concentrate
Metallurgical recovery
Projected grind rate
Mill feed grade
Mill feed tonnage
Mill feed moisture
Concentrate(s) tonnage(s),
grade(s) and moisture(s)
Tailings grade
Ore types, tonnages, grades
Projected recoveries
Measured mill feed rate
Product Sales/
Concentrate
Contained metal
Shipping loss
Projected revenue
Measured weight FOB
Smelter weight, grade and moisture
Smelter deducts
Sales price
Freight cost
Revenue received
Reser ve Estimate/
Product Sales
Projected production
Projected revenue
Estimated vs. actual contained metal sold
Estimated vs. actual sales price
Estimated vs. actual production rate
around some mean value. If the average discrepancy is
large enough to be significant, an adjustment factor of the
type discussed above is usually applied. Note that a radical
and persistent departure from the normal pattern is an
indication that something is amiss—again, as at Pilot Knob
early in 1974. In this instance, the mill feed grade calculated by the plant was based on historical iron unit recovery.
Early in 1974, the mill flowsheet was modified to meet
changing customer requirements, and the previous
method of calculating mill feed grade in the plant had to
be revised. The geological estimate is not always wrong! A
somewhat similar situation occurred at the Groveland
mine, where the appearance of a sudden large discrepancy
between mine and mill estimates was ultimately traced to a
hidden mechanical failure in the mill.
Inasmuch as the areas actually mined during a given time
period are likely to be somewhat different from the areas
scheduled, a single reconciliation of actual mine production
to scheduled tonnage and grade is likely to be misleading as
an indicator of reliability of reserve estimation. This second
reconciliation, simply involves re-estimating the blocks actually mined during the period, and comparing the results with
actual production. Figure IX-3 illustrates an actual reconciliation between mine and mill at a porphyry copper operation,
in which the various elements responsible for the discrepancy
between projected and actual results have been quantified.
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(000) Lbs.
Actual
145,344
Budget
145,155
Difference
189
Recovery
-178
Sequence
Difference
8,943
Tons
-2,458
Cutoff
Difference
9,429
Block Model
Difference
5,233
Grade
2,825
Other
-2,894
FIG. IX-3. Reconciliation of budget vs. actual performance.
In block-by-block comparisons it is informative to examine not only the overall tonnage and grade from any particular area, but also the comparisons above and below the
cutoff grade. Most estimation techniques have the effect of
smoothing out the grade distributions, with the result that
the grade of waste blocks is overestimated and the grade of
ore blocks underestimated. An example from an actual
operation is shown in the scatter diagram in Figure IX-4.
The estimated grades were based on kriging exploration
drill hole results and are compared to the block grades
defined by blast hole samples. The grades for the entire set
of blocks appear to correspond reasonably well, with the
regression line (a slope of 0.96) essentially coinciding with
the 45° slope on the diagram.
Note, however, that roughly half of the blocks defined as
waste by the blast hole samples were originally estimated to
be of ore grade. The slope of a regression line of these
waste blocks alone would be only 0.33. This particular estimation indicated appreciably more tonnage above cutoff
than really existed.
It is strongly recommended that similar scatter diagrams
be plotted in the course of any comparisons, not just in
reserve reconciliations. The actual distribution of points is
generally more informative than the simple calculation of
correlation coefficients; note that in this example the
apparently high correlation coefficient is due to two highgrade pairs. Examination of the actual plots is particularly
important when comparing a large number of paired data
points. Figure IX-5 presents a comparison of some 3,400
individual comparisons of estimated and blast hole grades.
The regression line again nearly corresponds with the theoretical 45° slope, but the correlation coefficient is only
0.74. Note the broad spread of points on both sides of the
regression line, and that a significant number of blocks
were misclassified (regardless of whether the estimated or
the blast hole figures are considered as “correct”).
Production Simulation
Clearly, during the feasibility stage of any proposed
mining project, it is of some importance to try to forecast
the type of grade control system that will be built into the
capital and operating cost structure for the proposed
operation. It is not enough to simply budget for the salary
of a mine geologist or geological engineer without considering the specific task that the budgeted geologist or
engineer will be expected to accomplish, and the sorts of
information for which the geological staff will be responsible. In general, the more inflexible the mill or processing plant, the greater the requirement for careful grade
control. In many operations, the mill can physically overcome the effects of misclassification of waste for ore; only
0.60
0.50
Blast Hole Grade
0.40
Blast Hole Grade
0.30
0.20
0.10
0.60
Estimated Grade
0.50
0.40
0.30
FIG. IX-4. Estimated vs. blast hole grades.
0.20
Estimated Grade
0.10
0.00
0.00
FIG. IX-5. Comparison of estimated vs. blast hole grades.
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ORE RESERVE ESTIMATES IN THE REAL WORLD
the profitability suffers. In others, a seemingly minor error
may shut down the entire operation.
The Cerro Matoso operation is a case in point of the latter. The smelting characteristics of the ore depend on a delicate balance between Fe, MgO, and SiO2 in the plant feed,
and the profitability of the operation on the content of Ni.
If the Fe-MgO-SiO2 balance is outside very narrow limits,
either the metal in the electric furnace freezes solid or a
superheated slag burns through the furnace wall. In such a
situation, careful grade control is clearly of some interest to
all concerned, and prior to project approval, it became necessary to design a grade control system that would eliminate
the likelihood of a major disaster. Unfortunately, geostatistical analysis indicated that the uncertainty of prediction of
slag melting temperature for individual mining blocks was
too great to be acceptable.
At Cerro Matoso, Hanna approached the problem
through the use of production simulation, and although the
details will vary, the general approach can be applied to a
wide variety of deposits.
1. The first step in the simulation is the calculation of
the statistics of the population of values assigned to the
individual mining blocks within the overall outlines of the
deposit, including the effects of both internal and replacement dilution. The population of values should include values assigned to isolated blocks of subore grade surrounded
by ore-grade material. Indicator kriging should be used
with extreme caution, and only if it is absolutely certain that
ore and waste can be effectively separated during mining.
2. Using Monte Carlo simulation, enough blocks are
drawn at random from the population to satisfy one day's
mill feed. The process is repeated numerous times, the statistics of a population representing a single day of production calculated, and the percentage of this population
lying outside acceptable limits estimated. Note that this
procedure presupposes no effective effort at grade control
during the operation, and will therefore represent a worst
case situation, where production grade control is either
difficult or impossible (as it often is in a true bulk mining
situation).
3. Assuming that the results of step 2 indicate an unacceptably high proportion of noneconomic or otherwise
inadmissible days of production, the effects of stockpiling
to smooth out daily swings can be evaluated by simply
repeating the evaluation using various combinations of single days of production—three days, a week, or a month
(Table IX-C). If, say, an analysis of stockpiling a week’s production is adequate to smooth out the day-to-day variations,
a blending system and space adequate for at least two such
stockpiles can be built into the feasibility study. Note that
the same amount of offgrade material will still be mined;
the effects will simply be less obvious.
4. In conjunction with steps 2 and 3, an educated guess
may be possible as to the probable effects of a mine grade
control program, especially if a test pattern of close-spaced
drilling of the sort previously considered has been undertaken. If the test pattern (at less than the proposed blast
hole spacing) indicates that the projectability of contacts
and grades is reasonably reliable over distances equal to the
blast hole spacing, it is likely that a program of blast hole
sampling will, in fact, reduce (but not eliminate) the likelihood of misclassification of ore and waste.
In light of the critical nature of the grade control function
at Cerro Matoso, the following scheme was adopted, that
although far more elaborate than necessary at many other
types of operation, may nevertheless be of some interest:
1. Mining blocks of a few thousand tons each are outlined by blast hole sampling, augmented with XRF face
samples during actual extraction.
2. A single day of mine production consists several individual mining blocks, each representing specific chemical
TABLE IX-C. Production Simulation—Cerro Matoso; Effect of Stockpiling
1 day
Stockpile
7 day
Stockpile
30 day
Stockpile
Mean
Std. Deviation
3.18%
0.30%
3.18%
0.10%
3.18%
0.04%
SiO2 in slag
Mean
Std. Deviation
63.16
3.66
63.20
1.31
63.22
0.46
1637˚ C
70˚ C
1652˚ C
40˚ C
1654˚ C
28˚ C
92˚ C
17˚ C
4˚ C
Ni
Slag Melting Temperature
Mean
Std. Deviation
Average Difference in
Slag Temperature on
Successive days
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STONE AND DUNN
compositions required for the desired blend. The individual blocks are mined and stockpiled separately ahead of the
crusher.
3. A few hundred tons are reclaimed from one of the
stockpiles, crushed separately, weighed, sampled, and
spread over the length of a 30-day stockpile of mill feed by
a travelling stacker. The process is repeated with each of the
other precrusher piles required for the day's production.
4. Based on the results of the previous day's sampling,
the estimated analyses of the remaining precrusher piles
may be adjusted, and the tonnages of new ore from individual mining blocks and the proportions of precrusher
piles reclaimed adjusted to bring the blend in the 30-day
pile into compliance with target requirements.
5. As the 30-day pile nears completion, any deviation
from target requirements is adjusted using a small amount
of material from one or more “specialty” stockpiles of ore
with disproportionate amounts of whatever chemical element is needed to bring the final pile into balance.
In this particular operation, the mine is responsible for
the grade of the mill feed stockpile, and in practice, the
stockpile grades very closely match the mill feed analyses.
An important consideration in a predevelopment study
of anticipated grade control is an honest assessment of the
relative benefits of improvements in estimating technique
compared with efforts to improve sampling, chemical
analysis, stockpiling and the like. At Cerro Matoso, it was
more cost effective to improve stockpiling (itself analyzed
by a variety of statistical techniques), and to accept admittedly imperfect short-range predictions, than to spend the
money on elaborate (and still imperfect) estimation techniques. Note that while geostatistical estimating techniques
were essentially useless for grade control purposes at Cerro
Matoso, developing an acceptable grade control system
required the use of innovative statistical analysis, and could
not have been done without this input. Even if geostatistical
procedures are inapplicable, there is still a need for statistical mathematics!
Provided that the fundamental assumptions underlying
geostatistical analysis are met, and given a reasonably reliable short range variogram, Rossi and Parker (1993), propose the following general procedure to estimate the recoverable reserves of the combinations of selective mining
units that constitute mining blocks or panels:
1. Develop a reserve model using ordinary kriging or a
combination of ordinary and indicator kriging to give
grade-tonnage curves for blocks of approximately the size
and shape of possible mining panels.
2. Perform a conditional simulation, hopefully based on
blast hole data, on a small grid.
3. Sample the conditional simulation, extracting a set of
simulated blast holes, using the same grid used by the
mine.
4. Use some form of kriging to interpolate grades on a
very tight grid.
5. Assign grades to panels, defining the location of possible diglines. Note that this step may incorporate isolated
points of internal dilution.
6. Compare panel grades and tonnages to reserves modelled from exploration data and develop suitable correction
factors on a local to global basis. In conjunction with step 5,
the effects of contact or replacement dilution can be taken
into consideration, assuming the true contact has an equal
probability of falling anywhere between the last ore-grade
hole and the first waste hole outside the proposed digline,
as previously discussed. A paper by Douglas et al. (1994)
expands on this technique by including an economic function to help discriminate between material that should go
the mill and material destined for the waste dump.
Note that the local correction factors developed in step 6
will vary from place to place within the deposit, and that
global factors likewise will not necessarily hold for a given
smaller area. In particular, the amount of replacement or
contact dilution will be a function of the proximity of the
proposed mining block to the ore/waste contact. Factors
developed for mining panels surrounded by ore-grade
material will therefore differ from factors developed for
panels straddling the ore/waste contact.
Clearly the amount of sample data required for this sort
of exercise is unlikely to be available prior to the actual
development of the mining project, and the procedures as
outlined are designed to improve the estimation of operational or developed reserves, and if the appropriate conditions are met, geostatistical analysis of the type proposed by
Douglas et al. (1994) holds the potential to significantly
improve the prediction of mill feed grades.
In a situation such as the Porgera deposit previously discussed, where a very few very high-grade samples represent a
disproportionate percentage of the total value of the
deposit, a more reliable predevelopment simulation of a
mining schedule may involve removing these samples
entirely from the block model database, rather than
attempting some sort of cutting procedure. Assuming that
the analyses of the samples in question are valid, and that
they do not represent a restricted, identifiable geologic environment, they probably represent occasional random occurrences of unusually high grade material. As such, it will be
extremely difficult or even impossible to accurately predict
where in the deposit similar spots are likely to occur. Consequently, a mining schedule simulated from a block model
from which these samples have been removed will provide a
better picture of a normal operating situation than will a
model purporting to predict precisely when the proposed
mining schedule will encounter one of these “hot spots.”
Needless to say, the proposed operation will encounter
these areas from time to time, perhaps in proportion to the
frequency of such samples in the overall sample population,
and such events may, in fact, represent much of the profit to
be expected from the operation. However, unless the proposed operation can survive long periods of “normal” grade
ore, economic viability will be in doubt.
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Society of Economic Geologists
SP3, 2012, p. 113–117
Chapter X
Theory vs. Reality
It's all very well in practice, but it will never work in
theory.
--French management saying
THROUGHOUT the foregoing, we have stressed the fact that
very few mines operate exactly as forecast, that in most
instances the actual results are below original expectations,
and that this fact is usually due to a failure to anticipate geologic complexities in the orebody, rather than to the computational techniques employed in the reserve estimation
process. Mason (1993) has suggested that the two main reasons for incorrect reserve estimates are (1) a lack of
detailed mine geology (including a fundamental geologic
understanding of the deposit), and (2) “advances” in computer skills and technology.
It has been pointed out to us that on occasion, a property
has been brought into production simply because the company involved is anxious to become an active producer,
even though the deposit in question is clearly uneconomic—a situation that seems especially prevalent with
small gold occurrences. There is a legal term for this sort of
action (“fraud”), and further discussion of this sordid subject is beyond the scope of this text.
The intent of this chapter is not to present a detailed list
of “horror stories,” but is, rather, to summarize some general observations that can be drawn from the literature.
This literature contains a great many studies comparing
predevelopment ore reserve projections with actual production (e.g., Section B, CIM Special Vol. 9, 1968; Blackwell
and Johnson, 1986; Bryan, 1986; Clow, 1991; Birak et al.,
1992; Manns and Ellingham, 1992).
In many of these studies, the mine staff takes pride in the
fact that the operation has produced more metal units than
forecast. A study by Grenier (1964) concluded that the base
metal mines reviewed produced 3.75 times the originally
estimated tonnage and 1.88 times the originally estimated
metal units relative to the preproduction reserves. The corresponding figures for gold mines were 1.57 and 1.08 for
the less successful ones, and 11 and 8 for successful ones. A
specific example is reported by Birak et al., (1992, p. 373)
at the North Generator Hill gold deposit in the Jerritt
Canyon district of Nevada. In this instance, block model
polygons appear to underestimate mined tonnage by 14.9
percent, and to overestimate grade by 9.4 percent.
Note, however, that in each of these instances, the figures
indicate that the production of the additional metal units
required mining a disproportionate tonnage. If the mine
produced, say, 110 percent of the forecast units from 150
percent of the forecast tonnage, it means that the ore
treated was lower in grade than forecast, and absent other
operational factors, was not as profitable as envisioned. Due
to the present value factor, additional years of operation
usually do not compensate for a shortfall in grade during
the early years.
In addition, as we have indicated throughout this discussion, minor errors in tenor, although economically serious,
may not be as catastrophic as errors in the density, metallurgical characteristics, or size, shape and location of the
orebody. The realization that the metallurgical characteristics of the Groveland orebody were significantly different
than those of the ore tested in the pilot plant required
major modifications in the installed milling circuit. Fortunately, in this instance, the reserve was large enough to justify the conversion, but the original financial projections
were obviously unrealizable. King et al. (1986) discuss a
similar situation at Woodlawn.
Unfortunately, in many instances, the reserve is too small
to overcome the effect of the installation of an inappropriate ore treatment plant or an inappropriate mining
method. By the time the problem is identified and the necessary additional equipment installed, the reserve is largely
exhausted; hence the Rule of Three previously discussed.
Although instances of meaningless geostatistical analysis
leading to meaningless reserve estimates do occur, in our
opinion, the almost universal discrepancy between estimated and realized values is due primarily to the geologic
inability to accurately define the contacts between different
mineral zones within the deposit (especially the contact
between ore and waste) rather than to any failure of the
mathematics employed in the estimation process. This geologic problem is further exacerbated by the inability of the
mining operation to precisely follow the true contact
between ore and waste. Together, these two problems can
lead to a significant amount of replacement dilution and a
corresponding reduction in the grade of the material actually mined.
The problem may be simply the inability to recognize
during mining a gradational (and possibly erratic) contact
between ore and waste, as at the Scadding mine described
by Manns and Ellingham (1992), or may be due to errors in
the basic geology of the sort described by Noble (1992b),
shown in Figure IV-2, and King (1986), shown in Figure X1, where the contact between ore and waste is not only
erratic, but covers a much greater surface area than originally envisioned. Unless the contact is marked by a structural boundary or parting plane, as at the Orostar mine
(Manns and Ellingham, 1992), some mixing of ore and
waste is virtually inevitable when mining to the contact, and
the amount of waste taken with the ore (both overbreak
and replacement dilution) will be proportional to the sur-
113
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STONE AND DUNN
3120 N
3080 N
Although there are occasional, and in our opinion
“lucky” exceptions, most successful mining ventures
are proven by accumulation of representative shortrange data at the feasibility stage by drilling closespaced holes or from bulk sample pits or underground
workings. Conversely, most mines which have been disappointing or have failed because of reserve problems
have skipped this step in their development.
3320 E
3280 E
Open Cut
Ore
Mary Kathleen – R.L. 1016 Feasibility Outline
3120 N
3080 N
3320 E
3280 E
Open Cut
Ore
Mary Kathleen – R.L. 1016 as Mined
FIG. X-1. Ore/waste contact at Mary Kathleen mine in feasibility outline and as mined; after King et al. (1986).
face area of that contact. Unfortunately, errors in defining
or following the ore/waste contact cannot be overcome by
clever mathematical or statistical techniques applied to
widely spaced sampling, a classic example of mathematical
analysis exceeding the quality of the primary database
(Thompson, 1992).
Given this sorry state of affairs, the answer seems obvious—DRILL or SAMPLE. As was pointed out earlier, a
carefully selected drilling pattern over a small area, with a
hole spacing no larger than half of the proposed blast hole
or production sample spacing, would have solved many of
the geologic uncertainties leading either to disappointment or disaster in many of the actual case histories previously discussed. As pointed out by H.M. Parker (1994, pers.
commun.):
In addition to providing data on the continuity of geologic, mineralogic, or economic contacts, such close-spaced
drilling and sampling is indispensable, both for proving
that the deposit in question is homogeneous as assumed,
and for the calculation of variograms that can be used for
reliable data projection in other than the down-hole direction. Without such data, conditional simulation studies that
may shed light on potential problems with both internal
and replacement dilution are clearly impossible.
We are, of course, aware of the time and budget constraints imposed by senior management on most development projects. In the words of the TV ad for automobile
maintenance, “You can pay me now or pay me later.” “Later”
may well cost several orders of magnitude more than “Now.”
An excellent postmining reconciliation of the situation at
the Cactus Gold mine operated by CoCa Mines during
1986–91 was presented by G.A. Hahn as part of the short
course given in Denver in April 1993. His observations
address many of the issues covered heretofore that we consider important in ore reserve estimation; they are summarized below with his permission, and with that of Hecla
Mining Co., subsequent operators of Cactus Gold mine.
The Winkler deposit at Cactus was one of several volcanic-hosted gold and silver deposits in the Middle Buttes
area of southern California. The Winkler resource was originally estimated to contain 100,000 tons of mineralization
grading 0.4 oz/t gold, occurring in a lens-shaped body as
shown on Figure X-2a. As indicated in Table X-A, a feasibility study indicated that this high grade reserve would provide a satisfactory financial return. The operating plan envisioned mining 100,000 tons of ore and 1,300,000 tons of
waste by open pit methods over a nine-month period, and
processing the ore by heap leaching to produce 34,000
ounces of gold.
Unfortunately, the minability of the geologic reserve was
not adequately reviewed, and a mine plan was developed
that envisioned bulk mining on 20-ft benches, with no
attempt at selective mining. As a result, 100,000 tons of
waste material with essentially zero value were taken
together with the ore (Table X-A). In addition to the obvious effect on the grade, the added dilution had two important effects on the physical operation. (1) Since it was necessary to treat twice the tonnage originally envisioned, the
leach pads were too small and had to be repermitted and
enlarged. (2) The dilution came primarily from the clayrich alteration zone surrounding the high grade mineralization. This material seriously affected the permeability of
the heap, necessitating the installation of agglomerating
equipment and the retreating of the initial tonnage.
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ORE RESERVE ESTIMATES IN THE REAL WORLD
X-2a
Geologic Resource
X-2b
Minable Reserve
Waste
100 feet
FIG. X-2. Winkler gold deposit section: a. geologic resource; b. minable
reserve.
W
TABLE X-A. Simplified Financial Analysis of the Winkler Deposit
Mining Data
Tons of Ore (1000)
Waste (1000)
Grade (oz/t)
Mine Life (in months)
Production (85 % Recovery)
Gross Revenue ($M)
Costs - Mining & Stripping ($M)
Processing
G&A
Royalty
Local Taxes
Operating Margin
less Capital
Pre-Tax Profit
Although total production (34,000 ounces) and total tonnage moved (1,400,000 tons) equalled the forecast figures,
the economic results fell far short. At the beginning of this
discussion, we stressed the fact that the purpose of any mining exercise is to make money, not ounces or pounds, and
that it is very difficult to achieve a profit by treating material
with zero value. In this example, however, the discrepancy in
the pretax profit figures is primarily due to two factors. (1)
An additional nine months of operation were required to
acquire the necessary permits, expand the leach pads and
solution ponds, install the necessary agglomerating equipment and reprocess the ore initially mined. As a result, General and Administrative costs increased by $1,375,000, and
local property taxes increased by $150,000. (2) Due to operational changes, processing costs increased by $650,000, and
capital costs increased by $750,000.
In retrospect, it was found that a block model of the
deposit using a cutoff of 0.02 oz/ton and 20′·20′·20′ mine
cell units would have more accurately predicted the tonnage and grade of material actually mined (Figure X-2b).
In part, this situation is a consequence of the fact that a
small tonnage of high grade ore in a given block can support a large amount of zero-grade material before the average grade drops to the cutoff. In addition, the block size of
the reserve model was similar to the size of the blast hole
blocks that were used as mine control, and mining therefor
essentially followed the outlines of the blocks included in
the estimate. In this instance, the operation was evidently
more concerned with 100 percent extraction of the ore
than with the effects of replacement dilution or overbreak.
TOTAL
Evaluation
Figures
Production
Figures
9
18
100
1,300
0.4
200
1,200
0.2
34,000 ounces
34,000
2,100
400
1,400
476
300
2,100
1,050
2,750
476
450
11,900
4,676
7,224
(5,250)
1,974
11,900
6,826
5,074
(6,000)
(926)
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STONE AND DUNN
However, the metallurgical testwork on which the original
design was based quite obviously neglected to consider the
possible impact of this dilution on the physical characteristics of the material that would actually be treated. In addition, there was a failure of communication between the
mine planners and the geologist responsible for the reserve
estimate, with the result that the selective mining necessary
to achieve the grades envisioned by the geologist was not
incorporated into the operating design.
In summarizing a study of North American gold mining
failures (defined as any project unlikely to recover its original capital cost at then-current prices), Harquail (1991)
attributes the vast majority (70 percent) of failures to mistakes in ore reserve estimation—mistakes themselves attributable to the following three factors:
1. Basic mistakes
a. Use of the wrong specific gravity (Ketza mine,
Canamax Resources)
b. Use of the wrong property boundaries (Yuba
American Gold)
2. Improper or insufficient sampling
a. Widely spaced drilling leading to the interpretation
of a continuous orebody that in reality is discontinuous (Cullaton Lake, Campbell Resources)
b. Contaminated drilling results such as reverse circulation drilling below the water table (Cove mine,
Echo Bay)
c. Nonrepresentative channel or muck sampling
(Magnacon mine, Flanagan McAdam)
d. Incorrect cutting factor for treating reserves with a
strong nugget effect (e.g., Mascot, Premier, Puffy
Lake, Tartan Lake, Mt. Skukum, Ketza River,
Kingston)
3. Lack of mining knowledge
a. Not understanding the dilution involved in mining, especially narrow vein and placer deposits
(e.g., Kettle River, Tartan Lake, Johnny Mountain,
McCabe, Los Lilenas)
b. Underestimating mining costs and hence using
too low a cutoff for ore (e.g., Colomac, Montana
Tunnels)
Note that only item 2d is in any way related to the mathematical technique used in the reserve estimation process.
The examples given were taken directly from the Harquail
study, others have been mentioned in this discussion, and
doubtless many others are available for inclusion on the list.
A somewhat similar study of some 22 heap leach operations revealed a failure rate in excess of 50 percent
(Kappes, 1979), prompting the following suggestions, that
although aimed specifically at heap leaching projects, are
nonetheless applicable to most other projects as well:
1. FIND SOME ORE. (At least three of the projects
failed because the heaps were built with material that
was not ore even at the projected operating costs).
2. Evaluate the metallurgical geology.
3. Perform sufficient laboratory tests.
4. Perform a field test (or pilot plant test).
5. Allow adequate time and money to begin production.
A more recent study (Bullock, 2011) focusing primarily
on engineering and financial shortcomings in feasibility
evaluations, nonetheless includes several of the geological
shortcomings discussed previously. His list of the basic
causes for the failure of mineral projects to reach their
projected rates of return is as follows:
1. Insufficient reliable or misstated reserves;
2. Incorrect metallurgical recovery used;
3. Overestimation off mining recovery and under estimation of mining dilution;
4. Using a higher commodity price(s) than the trend
price;
5. Lack of identification of where the project’s costs lie
in the seriatum of other operations of the same
commodity;
6. Understated capital and/or operating cost;
7. Major items of capital cost not even considered;
8. Overoptimistic mine design or productivity
9. Overoptimistic mine development schedule and
start up (learning curve) time
10. Overestimation of marketability of commodity;
11. Unpredicted variation of social/business attitude of
community, state and/or national government’s reaction to the project;
12. Unidentified environmental problems;
13. Lack of experience of company and/or feasibility
study contractor in developing projects, especially
in a country where they have no project experience;
14. The plant does not meet design expectations;
15. Differential price inflation between commodities
and consumables;
16. Differential exchange rates between home country
and developing country.
Of these, items #1 and #3 and, to a lesser extent, #2 have
been the subject of this text. If the basic reserve data are
in error, the degree of accuracy of any engineering study
based on the presumed reserve is largely irrelevant.
As summarized by Ranta (1992, p. 281):
Proper geologic work requires a keen awareness of and
ability to anticipate the technical requirements of geotechnical engineers, hydrologists, mining engineers,
metallurgists, and other specialists.
The same statement applies equally well in reverse: it is
imperative that the other parties involved in the evaluation communicate their concerns to the geological staff
responsible for gathering and recording the basic data.
Far too many exploration geologists have little or no operations experience, and unless made aware of operational
concerns, are almost sure to overlook features that will
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ORE RESERVE ESTIMATES IN THE REAL WORLD
subsequently become critical to the evaluation. Very few
geologists, engineers or metallurgists are clairvoyant.
The best and quickest way to improve accuracy and
reduce testing program costs is for geologists, mining
engineers and metallurgists to talk to each other about
ore types, distribution, and order of exploitation of a
deposit.
(Ashley and Callow, 2000)
As an example of the importance of the foregoing, consider the Exotica Project. During the early 1970s, Codelco's
Chuquicamata Division attempted the development of a
high grade body of transported oxide copper known at that
time as the Exotica deposit, now in production as Mina Sur.
The reasons for the eventual failure of the project have
been documented in an unusually honest assessment by
Bannack (1975). Among others:
1. An incomplete geological study that translated
into test samples that were not representative of the orebody. An adit was driven through the deposit in order
to provide pilot plant samples, but no raises were driven to provide bulk samples representing the vertical
dimension of the orebody. As a result, the ore that was
tested was softer, lower in carbonate, and showed less
variation in gangue mineralogy than the ore subsequently mined.
2. The plan for mine development was divorced
from metallurgical testwork. Whereas the pilot plant
treated an idealized mixture of ore based on the average composition and mineralogy of the deposit, mine
production was planned to follow a sequential pattern
117
designed to maximize only the economics of pit development, so as to yield hard bedrock ore during the first
years, together with altered ore that had been shown by
testwork to be characterized by higher acid consumptions, higher extraction of impurities, and lower copper recoveries than the bedrock ore. As a result, the
planned mining sequence and the testwork on the different types of ore were never combined in a study of
projected cash flows.
3. The water used in the pilot plant did not correspond to the recycled water that would be used in the
commercial plant, which subsequently proved to
inhibit the permeability of the ore.
4. The tertiary crushing was designed open circuit
without the benefit of crushing tests on representative
ore samples. As a result, the new plant delivers a product that is too coarse for good copper recoveries (35%
+3/8 " rather than 15% +3/8 " ).
5. The agglomerating system installed for slime
control was only effective when fed with precisely measured proportions of fresh and altered ore. In practice,
the mine was never able to deliver this mixture of prototypes, and instead delivered mixtures with varying
degrees of alteration.
We close with the quote from King et al. (1982, p. 13):
…it is the geological factor that has impressed itself on
us more and more as being the key deficiency where
serious weaknesses in ore reserve estimation have
appeared…