geological modelling

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GEOLOGICAL MODELLING AND EVALUATION OF NICKEL LATERITE DEPOSITS

Graeme Lyall1Abstract

The development of geologically realistic resource models for nickel laterite deposits ishindered by their extensive geometry and complex grade characteristics. Thicknessmodeling with digital terrain surfaces is appropriate for straightforward examples,however, more complex deposits will require additional enhancements. Examples areincluded to illustrate methods that have been employed on deposits evaluated by Anglogeologists in South American.Grade interpolation should take cognizance of the characteristic presence of verticaltrend 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.

The tabular geometry and presence of grade trends may lead to problems when usingkriging algorithms as the interpolation method. An example showing how this problemcan be minimized is provided.Most of the techniques described were developed using the DATAMINE softwarepackage. The versatile nature of this software was beneficial in developing theinnovative tools used in these studies.Generalised Nickel Laterite Profile

Nickel laterite deposits form by surface weathering and leaching processes in tropical

and sub-tropical climates. Typically, these phenomena result in three main mineralizedunits (laterite, saprolite and hard rock), which can be pictured on the cross section inthe 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, ofwhich the principal constituents are approximately 40% SiO 2, 35% MgO and 8% Fe.Nickel grades in this unit are sub-economic. The laterite unit is characterized by Feenrichment (>30%) and SiO2 and MgO depletion (generally both < 10%). The highestNi grades are encountered within the saprolite zone, which shows compositionsbetween fresh rock and laterite.

Laterite

Saprolite

Fresh Rock

Vertical Grade profiles

Fe Ni

100 mTypical cross section

1 Anglo American Chile Assistant Manager of Mineral Resource Division


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Geological Modeling in DATAMINETechnique for generating optimal drillhole codingIn most laterite deposits, fairly abrupt grade boundaries are observed between the Fe-rich laterite unit, the underlying Ni-rich saprolite and the low Ni grade hard rock at thebase, as can be observed from the grade profiles in the figure above. The identificationof these zone boundaries is commonly a manual process done on the inspection of thedrill hole grades. Moreover, considering that some of these deposits may coverextensive areas and the number of drill holes are often of the order of several hundredand sometimes in the thousands, this manual process can be a significant task. Toalleviate this, an automatic method was developed using DATAMINE processes toidentify the optimal intercept for each mineralised unit. In summary, this involves aniterative compositing procedure based on previously established cut-off grades foreach unit. The process defines the top and bottom of continuous mineralisedintercepts and identifies the optimal interval for each unit. The optimal compositeinterval 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 ofcontained metal (below cut-off) in the waste samples. Both Fe and Ni cut-offs can beconsidered. The figure below illustrates the results of this process.

Geological zonation using optimal composite algorithm

35%Fe

0.9%Ni

Waste

NotIncluded

High Fe Laterite

Ni-rich saprolite

Hard rockbase

35%Fe

0.9%Ni

WasteIncluded

Ni-rich saprolite

Hard rockbase

35%Fe

0.9%Ni

Waste

NotIncluded

High Fe Laterite

Ni-rich saprolite

Hard rockbase

35%Fe

0.9%Ni

Waste

NotIncluded

High Fe Laterite

Ni-rich saprolite

Hard rockbase

35%Fe

0.9%Ni

WasteIncluded

Ni-rich saprolite

Hard rockbase

35%Fe

0.9%Ni

WasteIncluded

Ni-rich saprolite

Hard rockbase

The above procedure proved very useful during evaluation studies for carried out onextensive drill hole data that required a relatively rapid evaluation. On reviewing theresults, the optimal compositing procedure provided intercepts almost identical to the

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


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Gridded Surface Elevation Thickness Data

Gridded Laterite thickness Gridded Saprolite Thickness

Drill hole intercept

Cross section interpretation

Limits of unit thick=0

Gridded Surface Elevation Thickness Data

Gridded Laterite thickness Gridded Saprolite Thickness

Drill hole intercept

Cross section interpretation

Limits of unit thick=0

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

Cross section

interpretation

DTMs generated using

thickness models

Block Modelling and grade

estimation

Cross section

interpretation

DTMs generated using

thickness models

Block Modelling and grade

estimation


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

Outcropping waste

Acid Ore

Basic Ore

Hard Rock Base

I nter nal Wast e

Vertical ProfileOutcropping waste

Acid Ore

Basic Ore

Hard Rock Base

I nter nal Wast e

Vertical Profile

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

Outcropping waste

Acid Or e

Basic Ore

I nter nal Wast e

Outcropping waste

Acid Or e

Basic Ore

I nter nal Wast e

Wireframe Surfaces

Outcropping waste

Acid Or e

Basic Ore

I nter nal Wast e

Outcropping waste

Acid Or e

Basic Ore

I nter nal Wast e

Wireframe Surfaces


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Barro AltoAt 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 Lomade Niquel deposit, however, approximately half the mineralisation is characterized bymuch thicker and complex profiles (WTO areas) cross-cut by sub-vertical chalcedonicand internal waste bodies as is shown in the cross sections below.

OVERBURDEN

ACID OREBASIC ORE

CHALCEDONY

WASTE

WTO ETO

Acid ore - SiO2/MgO>2.5

Basic ore - SiO2/MgO2.5

Basic ore - SiO2/MgO


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Grade EstimationFlatteningFlattening procedures can be used as a simpler alternative to unfolding in tabulardeposits that are controlled by gently undulating surfaces (e.g surface controlledweathering deposits or veins). The basic procedure involves projecting the drill holesand block model elevations (or co-ordinates) to a geological datum surface. Otherthickness correction and straightening functions may also be employed. Co-ordinatetransforms of this type are common practice in petroleum reservoir modeling (Deutch,2002).An easily identifiable surface that can be used for a straightforward projection in lateritedeposits is the laterite-saprolite contact boundary. Some people may prefer to use thesurface topography, however, recent erosional processes are likely have distorted theoriginal geological continuity. When considering veins it may be more appropriate toconsider its centre as a reference datum. For Ni-laterites, the co-ordinate transform canbe performed by subtracting the datum elevation (laterite-saprolite inteface) from theoriginal block or drillhole elevation. These procedures are relatively straightforward toprogram in DATAMINE and are illustrated in the following figure.

PROJECTION

Datum elevation surface

PROJECTION

Datum elevation surface

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

The evaluation of Ni-laterite deposits that are to be processed through thepyrometallurgical route require grade estimates for multiple components that are ofimportance to the metallurgy. The principal variables include Ni, Fe, SiO2 and MgO. Inthe laterite environment, these variables exhibit strong correlations as a result ofmineralogical transformations and, given that their interrelationships directly affect themetallurgical process, it is important that these correlations be reproduced in the blockgrade estimates. Independent interpolation of these by kriging will not guarantee thatthe correlations are reproduced appropriately and so some measures arerecommended to ensure that these are honored. This may be especially importantwhere drilling is sparse or more data is available for one variable than another. Asimple solution is to use identical variograms for variables showing strong correlationcharacteristics. Consideration could also be given to estimating secondary variablesbased 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 ratiosdo not average in a linear fashion. A more complex alternative would be to use co-kriging methods (Journel, A.G., and Huijbregts, C.J.,1978) that require painstakingvariogram modelling processes and an algorithm that is not available in standardcommercial software packages. The co-kriging option may be pushing geostatistics tothe limit, especially considering the uncertainty that usually exists in the variogram andcross-variogram models. A co-located co-kriging approach available in GSLIB(Deutsch, C.V., and A. G. Journel, 1997) simulation programs could show promise, asthis does not require the full LMC variogram models required for co-kriging. The co-located option only requires the correlation coefficient between the variables to beestimated where the secondary variable block kriging makes consideration of thepreviously estimated primary variable. However, this alternative is only available in theGSLIB simulation algorithms and not for kriging since, theoretically, it would require theuse of a slightly different correlation coefficient; that of an estimated block with thesample.Problems with kriging flat or thin depositsDuring the Barro Alto Evaluation study, it was noted from the initial kriging runs that theblock grade estimates for Ni were in almost all cases lower than those of the samplegrades. The regular nature of the drilling grids meant that sample clustering was not tobe blamed. Further investigations showed that the apparent bias in the kriging processwas due to the over-weighting of lower grade samples at contact boundaries given theirapparent redundancy. The figure below, showing ordinary kriging weights along a drillhole using the acid ore Ni variogram model, illustrates this problem. Note that althoughlogic dictates that the centre sample should receive the largest weight or at least asimilar weight as to all the others, this sample receives the lowest weight, whilst thehighest 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

sample beside it) than the other samples which have a sample on either side and thisleads the kriging process to assign higher weights to these samples. The end result isthat more weight is given to samples lying on contact boundaries, since when thesesamples are used they will always be located on the end of the data line. If thesecontact samples are generally lower grade, which is often the case, this will result in aglobal underestimation of the grades.

Ordinary kriging sample weights along drill hole

5x5x5 m block

Sample weights along drill hole

25 metres - Y direction

0.16

0.07

0.05

0.07

0.16

0.16

0.07

0.05

0.07

0.16

Ni variogram, Acid Ore: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 thekriging weighting process. This involved firstly adding an imaginary sample onto theend of each string of samples (drillhole), calculating the sample weights by ordinarykriging and then eliminating the weights of the imaginary samples. The remainingsample weights were then re-scaled to sum to 1. This procedure can be carried out inDATAMINE by reprocessing the kriging sample output file.The tables below compare average Ni grades of samples and estimated blocks for theoriginal ordinary kriging Ni estimate and for the modified kriging using imaginarysamples in six different resource areas. The bias in the original estimate is clearlynoted. On the other hand, it can be seen that the bias is almost completely eliminatedwhen using the alternative kriging option. Checks were also carried out using InverseDistance weighting, which compared closely to the sample and the modified krigingaverage grades.More documentation for this unexpected behavior of the kriging algorithm can be foundfound in two papers by C. V. Deutsch (1993 and 1994). In his 1993 paper, Deutschsuggests a solution that is identical to the one used here.

Average Ni Grades - Ordinary and Modified Kriging Options

BASIC ORE

Modified Kriging Comparison

Average %Ni by Area for BASIC ORE

1.4

1.5

1.6

1.7

1.8

1.9

2.0

1A 1BC 1D 2A 3A 3B 3CArea

Avera

ge%Ni

Samples

Original KrigingModified Kriging

ACID ORE

Modified Kriging Comparison

Average %Ni by Area for ACID ORE

1.4

1.5

1.6

1.7

1.8

1.9

2.0

1A 1BC 1D 2A 3A 3B 3C

Area

Average%Ni

Samples

Original Kriging

Modified Kriging


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Considerations for Simulating Laterite DepositsNotwithstanding recent advances in simulation methodology, experience gained inapplication of kriging methods is not directly transferable to simulation. This isparticularly true when considering trends that are characteristic of Ni-laterite deposits.Ordinary Kriging is remarkably robust at capturing trends and other local variations inthe mineral grades; however, the use of Ordinary Kriging in simulation is not as robustbecause of a greater reliance on the kriging variance and, implicitly, on the decision ofstationarity.For simulation purposes, trends in average grades can be dealt with by deterministicmodeling of locally varying trends followed by stochastic simulation of residuals of thetrend. Real simulated values are obtained by adding trend back to the simulatedresiduals.In addition, the handling of multivariate relationships in simulation is much morecomplex 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.For those interested in more detail on way to handle multivariate relationships andtrends in simulating Ni-laterite deposits, refer to the following publications: Lyall G.D.and Deutsch C.V., 2000; Leuangthong O., Lyall G.D. and Deutsch C.V., 2002ConclusionsThis paper shows some of the tradecraft necessary to obtain realistic models for Nilaterite deposits. Thickness surface-based modeling is suited for thin tabular deposits

that parallel surface topography. Flattening is also recommended to better representthe geological directions of continuity of these deposits. A number of other useful tipsfor the evaluation of these deposits have also been mentioned.The DATAMINE geological and mining software offers a number of functionalities thatpermit flexible data manipulation and programming of these atypical procedures intoautomated processes.

AcknowledgementsFinally, its important to acknowledge the participation in these studies of a number of

able geologically minded Anglo professionals in South America.Hopefully, Leonardo de Souza, who is currently on secondment to S. Africa, will bereturning soon to the continent to give us a supporting hand with Anglos growingassets in South America. Leonardo was responsible for developing the resourcemodels for several laterite deposits in Brazil and for important other deposits furtherafield. Leonardos practical geological mind and experience continues to be of extremevalue to the group.Luis Carlos de Assis, currently based in Anglos Goiania office, continues to providesupport to all the Brazilian projects and operations, principally in resource evaluation.Luis Carlos was the principal resource geologist at Barro Alto, and has also beeninvolved with a nearly all of Anglos operations and projects in Brazil. Additionally, Luis


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Carlos has been implementing similar resource evaluation techniques at AnglosCodemin Ni operation in Goias.Jose Andre Alvez, up until recently fulfilled a position in charge of mine planning atAnglos Loma de Niquel operation in Venezuela. The Loma de Niquel thickness-basedmodelling techniques were originally developed in conjunction with Jose Andre atAnglos Santiago offices and since then he continued to improve the procedures on theoperation in Venezuela. Jose Andre is another geologist with operational expertise andknowledge that has been of merit.Manuel Machuca, a mining engineer working with Anglos Resource Evaluation Groupin Chile has provided innovative support in many of these projects and continues to doso.

A final acknowledgement is necessary for Professor C.V. Deutchs tuitition andcontribution in the multivariate simulation aspects of these deposits.ReferencesDeutsch, C.V.,1993. Kriging in a Finite Domain, Mathematical Geology, Vol. 25, No. 1,January 93, pp. 41-52Deutsch, C.V., 1994. Kriging with Strings of Data, Mathematical Geology, Vol. 26, No.5, November 94, pp. 623-638Deutsch, C.V., and A. G. Journel, 1997, GSLIB: Geostatistical Software Library,Second Edition, Oxford University Press, New York, NY, 369 pp.

Deutsch C.V., 2002. Geostatistical Reservoir Modeling. Oxford University Press, NewYork, NY, 376 pp.Journel, A.G., and Huijbregts, C.J.,1978, Mining Geostatistics, Academic Press,London, 600 pp.Isaaks, E.I., and Srivastava, R.M., 1987, An Introduction to Applied Geostatistics,Oxford University Press, New York, NY, 561 pp.Lyall G.D. and Deutsch C.V., 2000. Geostatistical Modeling of Multiple Variables in thepresence of Compex Trends and Mineralogical Constraints. Geostatistics 2000 CapeTown.

Leuangthong O., Lyall G.D. and Deutsch C.V., 2002. Multivariate GeostatisticalSimulation of a Nickel Laterite Deposit. APCOM 2002 proceedings, Phoenix.

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