Modeling of Lithology and Total Sulfur in Carlin-type Gold Deposits

8/11/2019 Modeling of Lithology and Total Sulfur in Carlin-type Gold Deposits

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Modeling of Lithology and Total Sulfur in Carlin type Gold

Deposits Twin Creeks Case

Agapito OrozcoB.Sc. Mining Engineering

CPAG - University of Alberta

Denver, September 18th, 2012


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OBJECTIVE

Develop a rock type model based on sparse lithological data. The model will serve to subdivide

the current geological domains which were built up based on the folding of the sedimentarypackage of Conolea anticline and several faults. The purpose is constraining the also sparse

exploration data for geochemical variables, which are very determinant to classify the ore.

For this project only the total sulfur has been considered for geochemical modeling. One domain

was considered within the North Mega section of Mega deposit.

PROBLEM SETTING

The sulfide ores provide approximately 85% of total ounces of gold at Mega pit. Sulfide ores are

placed into high, mid and low grade stockpiles, which are further subdivided based ongeochemistry into seven separate stockpiles for optimizing the autoclave feed, based on contentsof Carbonate (CO3), Organic Carbon (OC) and Sulphide Sulfur (SS) models.

The figure below demonstrated the ore blending requirements for the autoclave. The gray box isthe feed blend required to sustain the autoclave pressure oxidation and the stockpiles available

for blending are plotted. The figure also shows the critical function of the high sulfide sulfur

ores. The optimum operating window is given by ores containing between 3.5 and 5 %SS, andfrom 4 to 6 %CO3 (0.8 to 1.2 %CC).

Autoclave Blending Requirements

Stockpiles are currently classified by carbon sulfur contents as follows, regardless the contentof gold. For mine planning the routing parameters classify the final destination using the

stockpiles categories and gold classified by bin cutoffs.

GeoChem by Ore Source

Mule Canyon

TRJV

Mega-D

Deep Post

Mega-F

Mega-B

Mega-C

Mega - A

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0

%

C

a

r

b

o

n

a

t

e

% Sulfide Sulfur

Conc.

Acid

#

S=rang e 3.8% - 4.5%

S=/CO3

=ratio 0.8 -1.2


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Stockpile Codes

CONCEPTUAL MODEL

A deterministic lithological model is developed using indicator kriging of categorical variables,based on the dominant lithology at each location.

The exploration block model for total sulfur is developed inside each lithology identified withinthe domain tcnUCe. Another model without lithology is also built up for comparison. Both

models are compared with a kriged model based on Blastholes.

A smaller SMU of 20 x 20 x 20 ft has been considered in order to establish a better resolution forthe lithological model. The current SMU is 40 x 40 x 20 ft

A fuzzy boundary test is also considered for reference.

METHODOLOGY

Considering only Sulfide Sulfur and only one domain at Mega deposit:

1.

Select lithology and grades corresponding to the chosen domain

2. Examine statistics of each lithology and compare distributions (,, ). Being a

categorical variable it will be an indicator variogram.

3. Examine statistics of sulfide sulfur (,

,

).

4. Built up kriged indicator models by each lithology. Based on the resulting probabilities,

select the dominant lithology to obtain the lithology block model.

5. Build up a kriged sulfide sulfur model based on each lithology (like control grid). Build

another model without considering lithology.

6. Compare with a Production Blastholes model.

7. Obtain the final geochemical models and gold model.

StkCode Sulfide Carbonate OrgCarb S Sulf% CO3% OrgC% Comments

0 Oxide

1 Low High Low < 3.5 >= 4 and < 12 < 0.6 Low Sulfide & High Carbonate2 High High Low >= 3.5 >= 4 and < 12 < 0.6 High Sulfide & High Carbonate

3 High Low Low >= 3.5 < 4 < 0.6 High Sulfide & Low Carbonate

4 Low Low Low < 3.5 < 4 < 0.6 Low Sulfide & Low Carbonate

5 any value any value any value Visible Arsenic

6 any value VHigh Low >= 12 < 0.6 Very High Carbonate

7 any value High High >= 4 >= 0.6 Very High Organic Carbon

8 any value Low High < 4 >= 0.6 Low Carbonate & Moderate OC


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DATA INVENTORY

Exploration Data:

Last drillhole extraction data Sep. 04, 2012. The information came separately by Au + Geochemical assays (carbon-sulfur), and by

Lithology. Raw data corresponds to 5-ft samples.

Digitized shapes in plan view and vertical section of geological domains.

Production Data:

Blastholes data within a pattern of 20 x 20 ft in average, containing grades of gold andcarbon-sulfur variables.


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The TSS (Technical and Scientific Software) Newmont proprietary system was used forvisualization and block modeling.

The upper image in the next figure depicts gold grades (Au opt) and the lower is for total sulfur

(STOT %). The next figure depict vertical section E-W looking north, likewise for gold and totalsulfur. The magenta contour corresponds to domain tcnUCe.

It is evident that the geochemical variable is more sparsely sampled. This fact served asmotivation to investigate other methods to develop a reliable geochemical model, because so far

several attempts were made unsuccessfully.


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GEOSTAT MODELING

1) EDA Exploratory Data Analysis

Exploration Data Preparation

a) Lump the current 64 lithologies in just 4 four main lithologies. This is prepared by the

Project Geologist. A very specific script was developed for this purpose, in order tocreate a transfer_code. It was applied on the raw data.

b) Join the Au+LECO data and Lithology raw data using Microsoft Access to ensure

integrity of the data. All alphanumeric codes are reduced to integer categorical variables.c) Obtain the downhole composites on 20-ft length, which is the same bench height and the

same blasthole length. The Stones (Newmont proprietary software) was used.

d) Data selection by formation and geological domains, using TSS (Newmont proprietarysoftware) which classifies the data within digitized geological shapes.

See global statistics on next tables.

Production Data Preparation

It does not contain any reference to lithology. On Twin Creeks current operations the on-

field mapped geosegments are used, mostly for ore control purposes, but they are not

used for geological modeling.

Final Data Set Domain tcnUCe

A final exploration data set was prepared for domain tcnUCe as depicted in the image below,containing 4373 composites. Two main lithologies were found: Lit1=Igneous and Lit3=

Siliceous. Only one composite indicated Calcareous however after a spatial review (see the 3Dimage) it was decided to ignore it because it may correspond most likely to igneous (in red)

looking benches above and below.

G ROUP CODE GROUPINGCRITERIA

igneous 1 Igneousrocks. Highlyvariable SulfurandCarbonatedependingonalteration. LittleCarbon

cal care ou s 2 Cal care ou srocks. HighCarbonate,variable carbon

siliceous 3 Siliceoussediments. Originallylowincarbonate,variablecarbon. Valueswillbechangedbyalteration.

overburden 4 Rocksassociatedwithoverburden,alluviumetc.

unknown 5 RocksnotexpectedinComus. Orrockswithunpredicable grouping

nosample 99


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2) Categorical Statistics and Variography

Cell declustering was applied to the categorical variables Lit1 and Lit3, resulting with the

following statistics.

The SAGE program was used for Variography. First an omnidirectional was used to determine

the nugget effect, followed by a downhole variography. Then the multidirectional variograms

were built.

The sedimentary feature of the deposit was taken in consideration when determining the

tolerances and bandwidths, according to the SAGE capabilities. In all cases the Correlogram was

calculated with sill 1.

The sedimentary feature of the depositwas taken in consideration when

determining the tolerances andbandwidths, according to the SAGEcapabilities.

In all cases the Correlogram wascalculated with sill 1.

This variography corresponds to

Lithology 1 = Intrusive.


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During the modeling a Spherical modelwas selected. It is evident a high nugget

effect in that specific domain for

intrusive.

Only two main lithologies wereconsidered for domain tcnUCe


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Finally the model and anisotropies are shown below which resemble the current geologicalenvironment. SAGE allows to get also GSLIB rotations, as needed to run the IK3D program.


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3) Indicator Categorical Modeling for Lithology

A deterministic lithology model was obtained with IK3D. The declustered global proportions

were obtained during the EDA. The block size considered is 20 x 20 x 20. A maximum numberof 6 samples per octant were considered to get the most information from the anisotropy.

The next image depicts the probability block model for intrusive. In this particular 0.35 was

chosen as the lower probability which resembles the old formation model for intrusive. The

green is Upper Sill and the blue is the Main Sill. The ellipsoid corresponds to the variogram, inthis particular case the rotations correspond to TSS convention just for visualization. The big

stars in red depict intrusive and the green are for siliceous.

It is evident that the old geological model is not considering new data, which has been capturedby the model.


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4) Fuzzy Boundary Test

A quick test of determining smoothing with fuzzy boundaries showed little gaining for this

particular exercise. The left image below depicts the result of IK3D in several domains. On the

right is the result of applying FUZZLVM.

Finally it was decided to continue with the first deterministic model.

5) Lithology Contour

The TSS program poly_con allows getting a dig shape from the probability block model grid.


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The lithology contour for igneous rock generated by using IK3D appears in blue on next image.


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6) Total Sulfur (STOT) EDA and Variography

Starting with this contour the current data for this particular domain was split in 2 groups, simply

IN (Igneous) and OUT (Siliceous) of the dig shape. Then two data sets were obtained for Total

Sulfur (STOT).

The corresponding EDA was run for these two groups of data.

The boxplot shows that there is very little difference in global statistics. As it is going to see later,

this not the same for the structural analysis through variography.

The cell declustering gave 120 ft and 200 ft for each group in order to determine a declusteredstatistics.


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The contact analysis gives a firm boundary, with no apparent big changes around the contact.

However the siliceous sub-domain shows a concentration of STOT farther than 250 ft apart that

deservers further investigation.

The correlation analysis for AuFA, STOT and Lithology gives some interesting result, even

though the correlation with NLitCod is not meaningful. The Spearman correlation shows morecorrelation between the variables inside the igneous sub-domain.

The variograms for STOT show better continuity on the igneous than the siliceous, which is

more nuggety.


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7) Total Sulfur (STOT) Block Modeling

The corresponding blocks models were built up using TSS Ordinary Kriging program which

gives more options in order to select the samples inside vicinity. Also it allows limiting thenumber of composites from each drillhole, in this case 6 max samples were considered from the

same drillhole, and the other must be search from others. The next image is simply the Excelinterface. As seen here several pair of two different block models, one for igneous and other for

siliceous were built, and after merged together by means of grid operations. Several scenarios of

max number of samples were run.


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8) Total Sulfur (STOT) Model Validation

For this purpose a blasthole model was built, after running EDA and variography in this specific

domain tcnUCe.

Production Blastholes data STOT


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Production Blastholes STOT block model


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Exploration STOT block model -With Lithology


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Exploration STOT block model -Without Lithology

When compared both exploration block models, by including Lithology there is a betteragreement with the anisotropy showed by the old geology model for intrusive rocks.


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The Grade Tonnage curves show better agreement for the STOT model with Lithology. The first

graph shows ratios with respect to Blastholes, so the ideal is to get 100% (or 1.00) in this

summary report. The cut offs are in the X axis.

Exploration STOT block model -With Lithology


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Exploration STOT block model -Without Lithology

CONCLUSION

The implementation of Indicator Categorical Modeling gives an interesting solution for our

problem to develop reliable geochemical models.

PATH FORWARD

The old geological models for lithology might be updated to get and ensemble with thenumerical model.


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REFERENCES

1.

C. V. Deutsch and A. G. Journel. GSLIB: Geostatistical Software Library: and Users

Guide. Second Edition. Oxford University Press, New York , 1998.

2. E. Isaaks and M. Srivastava. An Introduction to Applied Geostatistics. Oxford University

Press. New York, 1989.

3. Isaaks & Co.: Sage2001 Version 1.08, Users Guide. Redwood, California, 2001.

4. Georges Verly. Geostatistical Mineral Resource / Ore Reserve Estimation. Short Course

presented to Newmont. Denver, April 2010.

5. A Review of Metallurgical Modeling for Cut 24 - Isaaks & Co. March 15, 2010

6. Geostatistical Modeling of Geometallurgical Variables for Twin Creeks Mine, Nevada.

Clayton V. Deutsch Consultants Ltd. September 9, 2011.

7. Newmont Mining Corporation. Technical and Scientific Systems. TSS Studio - v6.2.0.0. -

Help System. Newmont USA, Ltd., 2004 2010.

8. Newmont Mining Corporation. Mineral Resource and Ore Reserve Report as of June 30,

2010. Twin Creeks Surface Operations. pages 18-20. Metallurgy.

Modeling of Lithology and Total Sulfur in Carlin-type Gold Deposits

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