Geological Modelling

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GEOLOGICAL MODELLING AND EVALUATION OF NICKEL LATERITE DEPOSITS GEOLOGICAL MODELLING AND EVALUATION OF NICKEL LATERITE DEPOSITS

Graeme Lyall Graeme Lyall 11 Abstract

Abstract

The development of geologically realistic resource models for nickel laterite deposits is The development of geologically realistic resource models for nickel laterite deposits is hindered by their extensive geometry and complex grade characteristics. Thickness hindered by their extensive geometry and complex grade characteristics. Thickness modeling with digital terrain surfaces is appropriate for straightforward examples, modeling with digital terrain surfaces is appropriate for straightforward examples, however, more complex deposits will require additional enhancements. Examples are however, more complex deposits will require additional enhancements. Examples are included to illustrate methods that have been employed on deposits evaluated by Anglo included to illustrate methods that have been employed on deposits evaluated by Anglo geologists in South American.

geologists in South American.

Grade interpolation should take cognizance of the characteristic presence of vertical Grade interpolation should take cognizance of the characteristic presence of vertical trend profiles and the multivariate behavior of the variables that are to be estimated. trend profiles and the multivariate behavior of the variables that are to be estimated. This is of particular importance if simulation exercises are to be performed.

This is of particular importance if simulation exercises are to be performed.

The tabular geometry and presence of grade trends may lead to problems when using The tabular geometry and presence of grade trends may lead to problems when using kriging algorithms as the interpolation method. An example showing how this problem kriging algorithms as the interpolation method. An example showing how this problem can be minimized is provided.

can be minimized is provided.

Most of the techniques described were developed using the DATAMINE software Most of the techniques described were developed using the DATAMINE software package. The versatile nature of this software was beneficial in developing the package. The versatile nature of this software was beneficial in developing the innovative tools used in these studies.

innovative tools used in these studies.

Generalised Nickel Laterite Profile Generalised Nickel Laterite Profile

Nickel laterite deposits form by surface weathering and leaching processes in tropical Nickel laterite deposits form by surface weathering and leaching processes in tropical and sub-tropical climates. Typically, these phenomena result in three main mineralized and sub-tropical climates. Typically, these phenomena result in three main mineralized units (laterite, saprolite and hard rock), which can be pictured on the cross section in units (laterite, saprolite and hard rock), which can be pictured on the cross section in the figure below. Characteristic vertical trends in nickel and iron grades are also shown. the figure below. Characteristic vertical trends in nickel and iron grades are also shown. The unweathered fresh rock at the base has a dunitic to peridotitic composition, of The unweathered fresh rock at the base has a dunitic to peridotitic composition, of which the principal constituents are approximately 40% SiO

which the principal constituents are approximately 40% SiO22, 35% MgO and 8% Fe., 35% MgO and 8% Fe.

Nickel grades in this unit are sub-economic. The laterite unit is characterized by Fe Nickel grades in this unit are sub-economic. The laterite unit is characterized by Fe enrichment (>30%) and SiO

enrichment (>30%) and SiO22 and MgO depletion (generally both < 10%). The highestand MgO depletion (generally both < 10%). The highest

Ni grades are encountered within the saprolite zone, which shows compositions Ni grades are encountered within the saprolite zone, which shows compositions between fresh rock and laterite.

between fresh rock and laterite.

Laterite

Saprolite

Fresh Rock

Vertical Grade profiles Vertical Grade profiles

Fe

Fe NiNi

100 m

Typical cross section

1 1

Anglo American Chile – Assistant Manager of Mineral Resource Division Anglo American Chile – Assistant Manager of Mineral Resource Division

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Geological Modeling in DATAMINE Geological Modeling in DATAMINE

Technique for generating “optimal” drillhole coding Technique for generating “optimal” drillhole coding

In most laterite deposits, fairly abrupt grade boundaries are observed between the In most laterite deposits, fairly abrupt grade boundaries are observed between the Fe-rich laterite unit, the underlying Ni-Fe-rich saprolite and the low Ni grade hard rock at the rich laterite unit, the underlying Ni-rich saprolite and the low Ni grade hard rock at the base, as can be observed from the grade profiles in the figure above. The identification base, as can be observed from the grade profiles in the figure above. The identification of these zone boundaries is commonly a manual process done on the inspection of the of these zone boundaries is commonly a manual process done on the inspection of the drill hole grades. Moreover, considering that some of these deposits may cover drill hole grades. Moreover, considering that some of these deposits may cover extensive areas and the number of drill holes are often of the order of several hundred extensive areas and the number of drill holes are often of the order of several hundred and sometimes in the thousands, this manual process can be a significant task. To and sometimes in the thousands, this manual process can be a significant task. To alleviate this, an automatic method was developed using DATAMINE processes to alleviate this, an automatic method was developed using DATAMINE processes to identify the “optimal” intercept for each mineralised unit. In summary, this involves an identify the “optimal” intercept for each mineralised unit. In summary, this involves an iterative compositing procedure based on previously established cut-off grades for iterative compositing procedure based on previously established cut-off grades for each unit. The process defines the top and bottom of continuous “mineralised” each unit. The process defines the top and bottom of continuous “mineralised” intercepts and identifies the optimal interval for each unit. The optimal composite intercepts and identifies the optimal interval for each unit. The optimal composite interval will include samples falling below the established cut-off grades only if the interval will include samples falling below the established cut-off grades only if the contained metal (above cut-off) in sample extensions to the interval exceeds the loss of contained metal (above cut-off) in sample extensions to the interval exceeds the loss of contained metal (below cut-off) in the waste samples. Both Fe and Ni cut-offs can be contained metal (below cut-off) in the waste samples. Both Fe and Ni cut-offs can be considered. The figure below illustrates the results of this process.

considered. The figure below illustrates the results of this process.

Geological zonation using optimal composite algorithm Geological zonation using optimal composite algorithm

3355 % % F F ee 00.. 99% % N Nii Waste Waste Not Not Included Included High Fe Laterite High Fe Laterite Ni-rich saprolite Ni-rich saprolite Hard rock Hard rock base base 3355 % % F F ee 00.. 99% % N N ii Waste Waste Included Included Ni-rich saprolite Ni-rich saprolite Hard rock Hard rock base base 3355 % % F F ee 00.. 99% % N Nii Waste Waste Not Not Included Included High Fe Laterite High Fe Laterite Ni-rich saprolite Ni-rich saprolite Hard rock Hard rock base base 3355 % % F F ee 00.. 99% % N Nii Waste Waste Not Not Included Included High Fe Laterite High Fe Laterite Ni-rich saprolite Ni-rich saprolite Hard rock Hard rock base base 3355 % % F F ee 00.. 99% % N N ii Waste Waste Included Included Ni-rich saprolite Ni-rich saprolite Hard rock Hard rock base base 3355 % % F F ee 00.. 99% % N N ii Waste Waste Included Included Ni-rich saprolite Ni-rich saprolite Hard rock Hard rock base base

The above procedure proved very useful during evaluation studies for carried out on The above procedure proved very useful during evaluation studies for carried out on extensive drill hole data that required a relatively rapid evaluation. On reviewing the extensive drill hole data that required a relatively rapid evaluation. On reviewing the results, the “optimal compositing” procedure provided intercepts almost identical to the results, the “optimal compositing” procedure provided intercepts almost identical to the manual determinations.

manual determinations. Loma de Niquel

Loma de Niquel

The modeling of the laterite-saprolite and saprolite-hard rock interfaces is best done by The modeling of the laterite-saprolite and saprolite-hard rock interfaces is best done by generating thickness models for the above units, by this way avoiding cross-overs with generating thickness models for the above units, by this way avoiding cross-overs with the surface topography. For the Loma de Niquel deposit, cross sections are interpreted the surface topography. For the Loma de Niquel deposit, cross sections are interpreted in DATAMINE at 50 metre intervals. These are then used to generate 2D thickness in DATAMINE at 50 metre intervals. These are then used to generate 2D thickness data spaced at 10 metre intervals along each section. Additional thickness data are data spaced at 10 metre intervals along each section. Additional thickness data are provided from the drill hole intercepts and from horizontal delineations indicating the provided from the drill hole intercepts and from horizontal delineations indicating the areal extents of the mineralized unit (thickness=0). All three data types are used to areal extents of the mineralized unit (thickness=0). All three data types are used to interpolate 2D thickness models on a 5x5 metre grid. A surface elevation model is also interpolate 2D thickness models on a 5x5 metre grid. A surface elevation model is also generated on the same 5x5 metre grid. These are illustrated in the figure below.

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Gridded Surface Elevation

Thickness Data

Gri

Gridd

dded

ed La

Later

terit

ite

e th

thic

ickn

knes

ess

s

Gri

Gridd

dded

ed Sa

Sapro

prolilite

te Th

Thic

ickne

kness

ss

Drill hole intercept Drill hole intercept

Cross section interpretation Cross section interpretation

Limits of unit – thick=0 Limits of unit – thick=0

Gridded Surface Elevation

Thickness Data

Gri

Gridd

dded

ed La

Later

terit

ite

e th

thic

ickn

knes

ess

s

Gri

Gridd

dded

ed Sa

Sapro

prolilite

te Th

Thic

ickne

kness

ss

Drill hole intercept Drill hole intercept

Cross section interpretation Cross section interpretation

Limits of unit – thick=0 Limits of unit – thick=0

From the 2D grid model containing surface elevation, laterite and saprolite thickness, From the 2D grid model containing surface elevation, laterite and saprolite thickness, the elevation of the laterite-saprolite and saprolite-hard rock interfaces can be the elevation of the laterite-saprolite and saprolite-hard rock interfaces can be calculated. Digital terrain models are then generated using the gridded models together calculated. Digital terrain models are then generated using the gridded models together with the data used for the interpolation (cross sections, drill hole intercepts and with the data used for the interpolation (cross sections, drill hole intercepts and horizontal limits). These are used to develop block models followed by grade horizontal limits). These are used to develop block models followed by grade estimates. The figure below illustrates these procedures.

estimates. The figure below illustrates these procedures.

Cross section Cross section interpretation interpretation DTM’s generated using DTM’s generated using thickness models thickness models

Block Modelling and grade Block Modelling and grade

estimation estimation Cross section Cross section interpretation interpretation DTM’s generated using DTM’s generated using thickness models thickness models

Block Modelling and grade Block Modelling and grade

estimation estimation

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A more complex example A more complex example

An example of a more complex deposit where a total of five units have been An example of a more complex deposit where a total of five units have been recognized in the vertical profile is shown in the figure below. In addition, these units recognized in the vertical profile is shown in the figure below. In addition, these units are often discontinuous meaning that in many cases not all of the units will be present. are often discontinuous meaning that in many cases not all of the units will be present.

Outcropping waste Outcropping waste

Acid Ore

Ba

Basic Orsic Ore e

Hard Rock Base Hard Rock Base

I

I nternternanal Wl Wasastte e

Vertical Profile

Outcropping waste Outcropping waste Acid Ore Acid Ore Ba

Basic Orsic Ore e

Hard Rock Base Hard Rock Base

I

I nternternanal Wl Wasastte e

Vertical Profile

Thickness modelling techniques were also used on this deposit to generate the surface Thickness modelling techniques were also used on this deposit to generate the surface wireframes for the base of each unit (see figure below) and the geological block model. wireframes for the base of each unit (see figure below) and the geological block model.

Aci

Acid Ord Ore e

Ba

Basic Orsic Ore e I

I nternternnal Wastal Waste e

Aci

Acid Ord Ore e

Ba

Basic Orsic Ore e I

Wireframe Surfaces

Aci

Acid Ord Ore e

Ba

Basic Orsic Ore e I

Aci

Acid Ord Ore e

Ba

Basic Orsic Ore e I

Wireframe Surfaces

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Barro Alto Barro Alto

At Barro Alto in Brazil the laterite development can be classified into two main types. At Barro Alto in Brazil the laterite development can be classified into two main types. Flat lying areas (ETO and PTO areas) show typical laterite profiles similar to the Loma Flat lying areas (ETO and PTO areas) show typical laterite profiles similar to the Loma de Niquel deposit, however, approximately half the mineralisation is characterized by de Niquel deposit, however, approximately half the mineralisation is characterized by much thicker and

much thicker and complex profiles (WTO complex profiles (WTO areas) cross-cut by sub-veareas) cross-cut by sub-vertical chalcertical chalcedonicdonic and internal waste bodies as is shown in the cross sections below.

and internal waste bodies as is shown in the cross sections below.

OVERBURDEN OVERBURDEN ACID ORE ACID ORE BASIC BASIC OROREE CHALCEDONY CHALCEDONY WASTE WASTE WTO

WTO ETOETO

Acid ore

-Acid ore - SiOSiO2/MgO2/MgO>2.5>2.5 Basic ore

Basic ore -- SiO2/MgO<2.5SiO2/MgO<2.5

NW NW SESE OVERBURDEN OVERBURDEN ACID ORE ACID ORE BASIC BASIC OROREE CHALCEDONY CHALCEDONY WASTE WASTE OVERBURDEN OVERBURDEN ACID ORE ACID ORE BASIC BASIC OROREE CHALCEDONY CHALCEDONY WASTE WASTE WTO

WTO ETOETO

Acid ore

-Acid ore - SiOSiO2/MgO2/MgO>2.5>2.5 Basic ore

Basic ore -- SiO2/MgO<2.5SiO2/MgO<2.5

NW

NW SESE

For WTO areas, geological block models were developed by combining 3D surface For WTO areas, geological block models were developed by combining 3D surface geology with detailed wireframe modelling of the sub-vertical units together with geology with detailed wireframe modelling of the sub-vertical units together with surface based techniques as is illustrated in the following figures.

surface based techniques as is illustrated in the following figures. Barro Alto – Area 3A

Barro Alto – Area 3A

OVERBURDEN OVERBURDEN ACID ORE ACID ORE BASIC ORE BASIC ORE CHALCEDONY CHALCEDONY WASTE WASTE SURFACE GEOLOGY

SURFACE GEOLOGY CCHHAALLCCEEDDOONNY Y BBLLOOCCK K MMOODDEEL L SSLLIICCEESS

OVERBURDEN OVERBURDEN ACID ORE ACID ORE BASIC ORE BASIC ORE CHALCEDONY CHALCEDONY WASTE WASTE OVERBURDEN OVERBURDEN ACID ORE ACID ORE BASIC ORE BASIC ORE CHALCEDONY CHALCEDONY WASTE WASTE SURFACE GEOLOGY

SURFACE GEOLOGY CCHHAALLCCEEDDOONNY Y BBLLOOCCK K MMOODDEEL L SSLLIICCEESS

OVERBURDEN OVERBURDEN ACID ORE ACID ORE BASIC ORE BASIC ORE CHALCEDONY CHALCEDONY WASTE WASTE OVERBURDEN OVERBURDEN ACID ORE ACID ORE BASIC ORE BASIC ORE CHALCEDONY CHALCEDONY WASTE WASTE

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Grade Estimation Grade Estimation Flattening

Flattening

Flattening procedures can be used as a simpler alternative to “unfolding” in tabular Flattening procedures can be used as a simpler alternative to “unfolding” in tabular deposits that are controlled by gently undulating surfaces (e.g surface controlled deposits that are controlled by gently undulating surfaces (e.g surface controlled weathering deposits or veins). The basic procedure involves projecting the drill holes weathering deposits or veins). The basic procedure involves projecting the drill holes and block model elevations (or co-ordinates) to a geological “datum” surface. Other and block model elevations (or co-ordinates) to a geological “datum” surface. Other thickness correction and straightening functions may also be employed. Co-ordinate thickness correction and straightening functions may also be employed. Co-ordinate transforms of this type are common practice in petroleum reservoir modeling (Deutch, transforms of this type are common practice in petroleum reservoir modeling (Deutch, 2002).

2002).

An easily identifiable surface that can be used for a straightforward projection in laterite An easily identifiable surface that can be used for a straightforward projection in laterite deposits is the laterite-saprolite contact boundary. Some people may prefer to use the deposits is the laterite-saprolite contact boundary. Some people may prefer to use the surface topography, however, recent erosional processes are likely have distorted the surface topography, however, recent erosional processes are likely have distorted the original geological continuity. When considering veins it may be more appropriate to original geological continuity. When considering veins it may be more appropriate to consider its centre as a reference datum. For Ni-laterites, the co-ordinate transform can consider its centre as a reference datum. For Ni-laterites, the co-ordinate transform can be performed by subtracting the datum elevation (laterite-saprolite inteface) from the be performed by subtracting the datum elevation (laterite-saprolite inteface) from the original block or drillhole elevation. These procedures are relatively straightforward to original block or drillhole elevation. These procedures are relatively straightforward to program in DATAMINE and are illustrated in the following figure.

program in DATAMINE and are illustrated in the following figure.

PROJECTION PROJECTION

Datum elevation surface Datum elevation surface

PROJECTION PROJECTION

Datum elevation surface Datum elevation surface

The flattening process is expected to provide more realistic geological continuity for the The flattening process is expected to provide more realistic geological continuity for the grade interpolation study, especially considering the presence of strong vertical grade grade interpolation study, especially considering the presence of strong vertical grade trends that are characteristic of these laterite deposits.

trends that are characteristic of these laterite deposits. Multiple variables

Multiple variables

The evaluation of Ni-laterite deposits that are to be processed through the The evaluation of Ni-laterite deposits that are to be processed through the pyrometallurgical route require grade estimates for multiple components that are of pyrometallurgical route require grade estimates for multiple components that are of importance to the metallurgy. The principal variables include Ni, Fe, SiO

importance to the metallurgy. The principal variables include Ni, Fe, SiO22 and MgO. Inand MgO. In

the laterite environment, these variables exhibit strong correlations as a result of the laterite environment, these variables exhibit strong correlations as a result of mineralogical transformations and, given that their interrelationships directly affect the mineralogical transformations and, given that their interrelationships directly affect the metallurgical process, it is important that these correlations be reproduced in the block metallurgical process, it is important that these correlations be reproduced in the block grade estimates. Independent interpolation of these by kriging will not guarantee that grade estimates. Independent interpolation of these by kriging will not guarantee that the correlations are reproduced appropriately and so some measures are the correlations are reproduced appropriately and so some measures are recommended to ensure that these are honored. This may be especially important recommended to ensure that these are honored. This may be especially important where drilling is sparse or more data is available for one variable than another. A where drilling is sparse or more data is available for one variable than another. A simple solution is to use identical variograms for variables showing strong correlation simple solution is to use identical variograms for variables showing strong correlation characteristics. Consideration could also be given to estimating secondary variables characteristics. Consideration could also be given to estimating secondary variables based on a ratios that directly associate them to the primary variable, however, an based on a ratios that directly associate them to the primary variable, however, an

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additional weighting mechanism would be required during interpolation given that ratios additional weighting mechanism would be required during interpolation given that ratios do not average in a

do not average in a linear fashion. A more complex linear fashion. A more complex alternative would be to alternative would be to use couse co--kriging methods (Journel, A.G., and Huijbregts, C.J.,1978) that require painstaking kriging methods (Journel, A.G., and Huijbregts, C.J.,1978) that require painstaking variogram modelling processes and an algorithm that is not available in standard variogram modelling processes and an algorithm that is not available in standard commercial software packages. The co-kriging option may be pushing geostatistics to commercial software packages. The co-kriging option may be pushing geostatistics to the limit, especially considering the uncertainty that usually exists in the variogram and the limit, especially considering the uncertainty that usually exists in the variogram and cross-variogram models. A co-located co-kriging approach available in GSLIB cross-variogram models. A co-located co-kriging approach available in GSLIB (Deutsch, C.V., and A. G. Journel, 1997) simulation programs could show promise, as (Deutsch, C.V., and A. G. Journel, 1997) simulation programs could show promise, as this does not require the full LMC variogram models required for kriging. The this does not require the full LMC variogram models required for kriging. The co-located option only requires the correlation coefficient between the variables to be located option only requires the correlation coefficient between the variables to be estimated where the secondary variable block kriging makes consideration of the estimated where the secondary variable block kriging makes consideration of the previously estimated primary variable. However, this alternative is only available in the previously estimated primary variable. However, this alternative is only available in the GSLIB simulation algorithms and not for kriging since, theoretically, it would require the GSLIB simulation algorithms and not for kriging since, theoretically, it would require the use of a slightly different correlation coefficient; that of an estimated block with the use of a slightly different correlation coefficient; that of an estimated block with the sample.

sample.

Problems with kriging flat or thin deposits Problems with kriging flat or thin deposits

During the Barro Alto Evaluation study, it was noted from the initial kriging runs that the During the Barro Alto Evaluation study, it was noted from the initial kriging runs that the block grade estimates for Ni were in almost all cases lower than those of the sample block grade estimates for Ni were in almost all cases lower than those of the sample grades. The regular nature of the drilling grids meant that sample clustering was not to grades. The regular nature of the drilling grids meant that sample clustering was not to be blamed. Further investigations showed that the apparent bias in the kriging process be blamed. Further investigations showed that the apparent bias in the kriging process was due to the over-weighting of lower grade samples at contact boundaries given their was due to the over-weighting of lower grade samples at contact boundaries given their apparent redundancy. The figure below, showing ordinary kriging weights along a drill apparent redundancy. The figure below, showing ordinary kriging weights along a drill hole using the acid ore Ni variogram model, illustrates this problem. Note that although hole using the acid ore Ni variogram model, illustrates this problem. Note that although logic dictates that the centre sample should receive the largest weight or at least a logic dictates that the centre sample should receive the largest weight or at least a similar weight as to all the others, this sample receives the lowest weight, whilst the similar weight as to all the others, this sample receives the lowest weight, whilst the highest weight is given to the samples at the end of each line of data. The highest weight is given to the samples at the end of each line of data. The over-weighting occurs because the end sample is seen as less redundant (it only has 1 weighting occurs because the end sample is seen as less redundant (it only has 1 sample beside it) than the other samples which have a sample on either side and this sample beside it) than the other samples which have a sample on either side and this leads the kriging process to assign higher weights to these samples. The end result is leads the kriging process to assign higher weights to these samples. The end result is that more weight is given to samples lying on contact boundaries, since when these that more weight is given to samples lying on contact boundaries, since when these samples are used they will always be located on the end of the data line. If these samples are used they will always be located on the end of the data line. If these contact samples are generally lower grade, which is often the case, this will result in a contact samples are generally lower grade, which is often the case, this will result in a global underestimation of the grades.

global underestimation of the grades.

Ordinary kriging sample weights along drill hole Ordinary kriging sample weights along drill hole

5x5x5

5x5x5 m m blockblock Sample weights along drill hole

Sample weights along drill hole

25 metres - Y direction 25 metres - Y direction 0.16 0.16 0.07 0.07 0.05 0.05 0.07 0.07 0.16 0.16 0.16 0.16 0.07 0.07 0.05 0.05 0.07 0.07 0.16 0.16

Ni variogram, Acid Ore: Ni variogram, Acid Ore:

0.15+0.40sph(10,4,4)+0.35sph(32,10,10)+0.15sph(150,30,30) 0.15+0.40sph(10,4,4)+0.35sph(32,10,10)+0.15sph(150,30,30)

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The bias was almost completely eliminated by adopting a small modification in the The bias was almost completely eliminated by adopting a small modification in the kriging weighting process. This involved firstly adding an imaginary sample onto the kriging weighting process. This involved firstly adding an imaginary sample onto the end of each string of samples (drillhole), calculating the sample weights by ordinary end of each string of samples (drillhole), calculating the sample weights by ordinary kriging and then eliminating the weights of the imaginary samples. The remaining kriging and then eliminating the weights of the imaginary samples. The remaining sample weights were then re-scaled to sum to 1. This procedure can be carried out in sample weights were then re-scaled to sum to 1. This procedure can be carried out in DATAMINE by reprocessing the kriging sample output file.

DATAMINE by reprocessing the kriging sample output file.

The tables below compare average Ni grades of samples and estimated blocks for the The tables below compare average Ni grades of samples and estimated blocks for the original ordinary kriging Ni estimate and for the modified kriging using imaginary original ordinary kriging Ni estimate and for the modified kriging using imaginary samples in six different resource areas. The bias in the original estimate is clearly samples in six different resource areas. The bias in the original estimate is clearly noted. On the other hand, it can be seen that the bias is almost completely eliminated noted. On the other hand, it can be seen that the bias is almost completely eliminated when using the alternative kriging option. Checks were also carried out using Inverse when using the alternative kriging option. Checks were also carried out using Inverse Distance weighting, which compared closely to the sample and the modified kriging Distance weighting, which compared closely to the sample and the modified kriging average grades.

average grades.

More documentation for this unexpected behavior of the kriging algorithm can be found More documentation for this unexpected behavior of the kriging algorithm can be found found in two papers by C. V. Deutsch (1993 and 1994). In his 1993 paper, Deutsch found in two papers by C. V. Deutsch (1993 and 1994). In his 1993 paper, Deutsch suggests a solution that is identical to the one used here.

suggests a solution that is identical to the one used here.

Average Ni

Average Ni Grades - Ordinary and Modified Kriging OptionsGrades - Ordinary and Modified Kriging Options BASIC ORE

BASIC ORE

Modified Kriging Comparison Modified Kriging Comparison Average %Ni by Area for BASIC ORE Average %Ni by Area for BASIC ORE

1.4 1.4 1.5 1.5 1.6 1.6 1.7 1.7 1.8 1.8 1.9 1.9 2.0 2.0 1 1A A 11BBC C 11D D 2A 2A 33A A 33B B 33CC Area Area A A vvee rraa ggee % % N N ii Samples Samples Original Kriging Original Kriging Modified Kriging Modified Kriging ACID ORE ACID ORE

Modified Kriging Comparison Modified Kriging Comparison Average %Ni by Area for ACID ORE Average %Ni by Area for ACID ORE

1.4 1.4 1.5 1.5 1.6 1.6 1.7 1.7 1.8 1.8 1.9 1.9 2.0 2.0 1 1A A 11BBC C 11D D 2A 2A 33A A 33B B 33CC Area Area A A vvee rraa ggee % % N N ii Samples Samples Original Kriging Original Kriging Modified Kriging Modified Kriging

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Considerations for Simulating Laterite Deposits Considerations for Simulating Laterite Deposits

Notwithstanding recent advances in simulation methodology, experience gained in Notwithstanding recent advances in simulation methodology, experience gained in application of kriging methods is not directly transferable to simulation. This is application of kriging methods is not directly transferable to simulation. This is particularly true when considering trends that are characteristic of Ni-laterite deposits. particularly true when considering trends that are characteristic of Ni-laterite deposits. Ordinary Kriging is remarkably robust at capturing trends and other local variations in Ordinary Kriging is remarkably robust at capturing trends and other local variations in the mineral grades; however, the use of Ordinary Kriging in simulation is not as robust the mineral grades; however, the use of Ordinary Kriging in simulation is not as robust because of a greater reliance on the kriging variance and, implicitly, on the decision of because of a greater reliance on the kriging variance and, implicitly, on the decision of stationarity.

stationarity.

For simulation purposes, trends in average grades can be dealt with by deterministic For simulation purposes, trends in average grades can be dealt with by deterministic modeling of locally varying trends followed by stochastic simulation of residuals of the modeling of locally varying trends followed by stochastic simulation of residuals of the trend. Real simulated values are obtained by adding trend back to the simulated trend. Real simulated values are obtained by adding trend back to the simulated residuals.

residuals.

In addition, the handling of multivariate relationships in simulation is much more In addition, the handling of multivariate relationships in simulation is much more complex than in kriging due to the random component of the simulation. In this regard, complex than in kriging due to the random component of the simulation. In this regard, a co-simulation approach is essential to reproduce the correlation characteristics.

a co-simulation approach is essential to reproduce the correlation characteristics.

For those interested in more detail on way to handle multivariate relationships and For those interested in more detail on way to handle multivariate relationships and trends in

trends in simulating Ni-laterite deposits, refer to the following publications: simulating Ni-laterite deposits, refer to the following publications: Lyall Lyall G.D.G.D. and Deutsch C.V., 2000; Leuangthong O., Lyall G.D. and Deutsch C.V., 2002

and Deutsch C.V., 2000; Leuangthong O., Lyall G.D. and Deutsch C.V., 2002

Conclusions Conclusions

This paper shows some of the tradecraft necessary to obtain realistic models for Ni This paper shows some of the tradecraft necessary to obtain realistic models for Ni laterite deposits. Thickness surface-based modeling is suited for thin tabular deposits laterite deposits. Thickness surface-based modeling is suited for thin tabular deposits that parallel surface topography. Flattening is also recommended to better represent that parallel surface topography. Flattening is also recommended to better represent the geological directions of continuity of these deposits. A number of other useful tips the geological directions of continuity of these deposits. A number of other useful tips for the evaluation of these deposits have also been mentioned.

for the evaluation of these deposits have also been mentioned.

The DATAMINE geological and mining software offers a number of functionalities that The DATAMINE geological and mining software offers a number of functionalities that permit flexible data manipulation and programming of these atypical procedures into permit flexible data manipulation and programming of these atypical procedures into automated processes.

automated processes.

Acknowledgements Acknowledgements

Finally, its important to acknowledge the participation in these studies of a number of Finally, its important to acknowledge the participation in these studies of a number of able geologically minded Anglo professionals in South America.

able geologically minded Anglo professionals in South America.

Hopefully, Leonardo de Souza, who is currently on secondment to S. Africa, will be Hopefully, Leonardo de Souza, who is currently on secondment to S. Africa, will be returning soon to the continent to give us a supporting hand with Anglo’s growing returning soon to the continent to give us a supporting hand with Anglo’s growing assets in South America. Leonardo was responsible for developing the resource assets in South America. Leonardo was responsible for developing the resource models for several laterite deposits in Brazil and for important other deposits further models for several laterite deposits in Brazil and for important other deposits further afield. Leonardo’s practical geological mind and experience continues to be of extreme afield. Leonardo’s practical geological mind and experience continues to be of extreme value to the group.

value to the group.

Luis Carlos de Assis, currently based in Anglo’s Goiania office, continues to provide Luis Carlos de Assis, currently based in Anglo’s Goiania office, continues to provide support to all the Brazilian projects and operations, principally in resource evaluation. support to all the Brazilian projects and operations, principally in resource evaluation. Luis Carlos was the principal resource geologist at Barro Alto, and has also been Luis Carlos was the principal resource geologist at Barro Alto, and has also been involved with a nearly all of Anglo’s operations and projects in Brazil. Additionally, Luis involved with a nearly all of Anglo’s operations and projects in Brazil. Additionally, Luis

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Carlos has been implementing similar resource evaluation techniques at Anglo’s Carlos has been implementing similar resource evaluation techniques at Anglo’s Codemin Ni operation in Goias.

Codemin Ni operation in Goias.

Jose Andre Alvez, up until recently fulfilled a position in charge of mine planning at Jose Andre Alvez, up until recently fulfilled a position in charge of mine planning at Anglo’s Loma de Niquel operation in Venezuela. The Loma de Niquel thickness-based Anglo’s Loma de Niquel operation in Venezuela. The Loma de Niquel thickness-based modelling techniques were originally developed in conjunction with Jose Andre at modelling techniques were originally developed in conjunction with Jose Andre at Anglo’s Santiago offices and since then he continued to improve the procedures on the Anglo’s Santiago offices and since then he continued to improve the procedures on the operation in Venezuela. Jose Andre is another geologist with operational expertise and operation in Venezuela. Jose Andre is another geologist with operational expertise and knowledge that has been of merit.

knowledge that has been of merit.

Manuel Machuca, a mining engineer working with Anglo’s Resource Evaluation Group Manuel Machuca, a mining engineer working with Anglo’s Resource Evaluation Group in Chile has provided innovative support in many of these projects and continues to do in Chile has provided innovative support in many of these projects and continues to do so.

so.

A final acknowledgement is necessary for Professor C.V. Deutch’s tuitition and A final acknowledgement is necessary for Professor C.V. Deutch’s tuitition and contribution in the multivariate simulation aspects of these deposits.

contribution in the multivariate simulation aspects of these deposits.

References References

Deutsch, C.V.,1993.

Deutsch, C.V.,1993. Kriging in a Finite Domain Kriging in a Finite Domain , Mathematical Geology, Vol. 25, No. 1,, Mathematical Geology, Vol. 25, No. 1, January 93, pp. 41-52

January 93, pp. 41-52 Deutsch, C.V., 1994.

Deutsch, C.V., 1994. Kriging with Strings of Data Kriging with Strings of Data , Mathematical Geology, Vol. 26, No., Mathematical Geology, Vol. 26, No. 5, November 94, pp. 623-638

5, November 94, pp. 623-638

Deutsch, C.V., and A. G. Journel, 1997,

Deutsch, C.V., and A. G. Journel, 1997, GSLIB: Geostatistical Software Library GSLIB: Geostatistical Software Library ,, Second Edition, Oxford University Press, New York, NY, 369 pp.

Second Edition, Oxford University Press, New York, NY, 369 pp. Deutsch C.V., 2002.

Deutsch C.V., 2002. GeostatistGeostatistical Reservoir ical Reservoir Modeling Modeling . Oxford University Press, New. Oxford University Press, New York, NY, 376 pp.

York, NY, 376 pp.

Journel, A.G., and Huijbregts, C.J.,1978,

Journel, A.G., and Huijbregts, C.J.,1978, Mining Geostatistics Mining Geostatistics , Academic Press,, Academic Press, London, 600 pp.

London, 600 pp.

Isaaks, E.I., and Srivastava, R.M., 1987,

Isaaks, E.I., and Srivastava, R.M., 1987, An Introduction to Applied Geostatistics An Introduction to Applied Geostatistics ,, Oxford University Press, New York, NY, 561 pp.

Oxford University Press, New York, NY, 561 pp. Lyall G.D. and Deutsch C.V., 2000.

Lyall G.D. and Deutsch C.V., 2000. Geostatistical Modeling of Multiple Variables in the Geostatistical Modeling of Multiple Variables in the presence of Compex Trends and Mineralogical Constraints.

presence of Compex Trends and Mineralogical Constraints. Geostatistics 2000 CapeGeostatistics 2000 Cape Town.

Town.

Leuangthong O., Lyall G.D. and Deutsch C.V., 2002.

Leuangthong O., Lyall G.D. and Deutsch C.V., 2002. Multivariate Geostatistical Multivariate Geostatistical Simulation of a Nickel Laterite Deposit.