Embedding Geometallurgy into Mine Planning Practices … · Statement of Mineral Resources 18 February 2016 Embedding Geometallurgy into Mine Planning Practices 2 Mineral Resources

Embedding Geometallurgy into Mine Planning PracticesKathy Ehrig, Vanessa Liebezeit, Resource Planning and Development

18 February 2016

Statement of Mineral Resources

18 February 2016

Embedding Geometallurgy into Mine Planning Practices

2

Mineral Resources and Ore Reserves

The information in this presentation that relates to the FY2015 Mineral Resources (inclusive of Ore Reserves) and Ore Reserves was first reported by

the Company in compliance with the ‘Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves, 2012’ (‘The

JORC Code 2012 Edition’) in the 2015 BHP Billiton Annual Report on 25 September 2015.

All reports are available to view on http://www.bhpbilliton.com.

• Mineral Resources are reported by S. O’Connell (MAusIMM) – Olympic Dam.

The Company confirms that it is not aware of any new information or data that materially affects the information included in the original market

announcements and, in the case of estimates of Mineral Resources, that all material assumptions and technical parameters underpinning the

estimates in the relevant market announcements continue to apply and have not materially changed. The Company confirms that the form and context

in which the Competent Persons’ findings are presented have not been materially modified from the original market announcements.

The above-mentioned person is a full-time employee of BHP Billiton and has the required qualifications and experience to qualify as Competent

Persons for Mineral Resources under the 2012 edition of the JORC Code. The compilers verify that this presentation is based on and fairly reflects the

Mineral Resources information in the supporting documentation and agree with the form and context of the information presented.

Table 1.

DepositMeasured Resource

(Mt)

Indicated Resource

(Mt)

Inferred Resource

(Mt)

Total

Resource

(Mt)

BHP Billiton interest

(%)

Olympic Dam

Sulphide

1,330 @

0.96% Cu,

0.29kg/t U3O3,

0.40g/t Au,

2g/t Ag

4,610 @

0.79% Cu,

0.24kg/t U3O3,

0.32g/t Au,

1g/t Ag

4,120 @

0.71% Cu,

0.25kg/t U3O3,

0.24g/t Au,

1g/t Ag

10,100 @

0.78% Cu,

0.25kg/t U3O3,

0.30g/t Au,

1g/t Ag

100

http://www.bhpbilliton.com/

Think Minerals, Not Elements

Minerals, minerals, minerals

• Elements occur in mineral deposits as minerals.

− Rock/alteration types are defined based on

mineralogy.

− Geologists can only qualitatively estimate

mineralogy.

• Minerals, not elements, are mined.

• Extractive metallurgy extracts elements from

minerals.

Elements are proxies for minerals

• No longer necessary to rely on assaying and

qualitative mineralogy to characterise an ore

deposit.

• Mineral abundances can be estimated/measured

at the deposit scale.

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“Every mineral has a story.”Nigel Cook

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The Olympic Dam deposit contains > 100 minerals.

Olympic Dam Mineralogy

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Sub-Econ Minerals

(may be deleterious)Gangue MineralsEconomic Minerals

Olympic Dam ‘Ores’

>100 minerals

(but 15 process critical minerals)

• Cu-Sulphides (py-cp-bn-cc)

– concentrate grade

• uranium minerals

– uranium recovery

• Au-Ag

• concentrate and cathode

quality

– As-, Se-, Bi-, Te-, Sb-,

– Pb-, Co-, Zn-, Mo-,

REE-bearing minerals

• Hematite grinding

• Quartz grinding

• Sericite slimes

• K-feldspar

• Chlorite acid, gelling

• Siderite acid

• Fluorite

• Barite• Wide spectrum of mineral mixtures many ‘ore types’

Two simple, fundamental relationships

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Mineral (wt%) = ƒ(sample composition)

‘Met Performance’ = ƒ(mineralogy, ore texture, process conditions)** modified from Bojcevski (2004)

Geological/mineralogical/geomet data

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Data consists of:

• Geological mapping of +450 km of UG development.

• Geological logging of ~2 M metres of diamond drill core.

• All diamond core is photographed.

• All diamond core is assayed for +26 elements.

• Density and magnetic susceptibility on all assayed samples.

• Abundances of 11 minerals predicted on every sample.

• Quantitative mineralogy measured on ~15,000 samples.

• ‘Super’ suite of elements measured on ~15,000 samples.

• Mineral composition database (LA-ICPMS and EPMA).

• Grinding, flotation, leaching on ~1,700 samples.

• Assays and mineralogy of a size-by-size basis for all

metallurgical samples.

• +27 years of mining and processing production data.

Olympic Dam Resource Model

• ~ 20 million blocks

• 5x10x5m up to 30x30x15m

• ~1.6 million assayed samples

• models for 11 minerals and SG

• models for 24 elements

Resource Geomet-enabled block model

Background behind our approach to populating the resource block model with geomet data:

• Mantra during the mid-2000s was to geostatistically estimate the geomet data.

• Nice idea, but based on a naïve understanding of the impact of mineralogical variability within any

specified rock/alteration type on metallurgical performance.

• A handful of enlightened metallurgists understood met performance relationship to mineralogy and

texture, but geologists didn’t have the ability to populate a block model with quantitative mineralogy.

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Our approach:

• Utilise the power of the resource model!

• Describe all geomet predictors in terms of fundamental

controls (i.e. mineralogy and chemical composition).

• Link all geomet predictors back to parameters which

are estimated (e.g. minerals/elements) in block model.

• The effect is to increase your sampling base by two to

three orders of magnitude.

• By-pass the non-linear behaviour of physical

properties during resource estimation.

Met

Testing

Ore Characterisation

Resource Drill Hole Samples

(all assayed with estimated mineralogy)

Resource Block Model

(~10G tonne resource)

~1.5M

samples

~150,000

~15,000

~1,500

~20M

blocks

Olympic Dam Geomet Samples- drill core

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Sample availability increases as Resource

Classification changes from Inferred to Indicated to

Measured. Seems obvious….

• Sampling for an open-pit (OP) operation:

– EASY, mining benches, regular spaced drill holes

• Sampling for an underground (UG) operation:

– COMPLEX, mining stopes, fan drilling

• Sampling for a combined UG-OP operation:

– CHAOS, potential for data with limited use

Olympic Dam Geomet Deliberate Decision:

• Characterise all possible combinations of process

critical minerals.

• Conduct metallurigcal testing on samples which

represent all possible combinations of process critical

minerals.

• Ensure that sampling covers the lateral and vertical

extent of the mineralised deposit.

Stope Sampling: The Dilemma

How do you produce a

‘representative’ sample of the stope

from the drill holes above?

Answer: not possible!

Geometallurgy Model Variables

10

‘Revenue’ Metals

• Cu

• U

• Au

• Ag

• others that affect

revenue

− S (sulphide S)

− density

Minerals

• pyrite

• chalcopyrite

• bornite

• chalcocite

• hematite

• sericite

• K-feldspar

• chlorite

• siderite

• barite

• fluorite

Other Elements

• Mo

• Zn

• Pb

• As

• Co

• Ni

• Fe, K, Al, Si, Na

• Ti, P, Mg, Mn, Sr

• La, Ce, Zr

‘Met’ Variables

• DWi, BWi,

Specific Energy,

MPower, MTPH,

MillHours

• TCuB, FCuRec%,

FCCu%, FConT,

• FCZn, FCPb,

FCAs, FCF

• SFCuT, SFCu%,

TSF,

• TFT, FTCuT,

FTCuRecT

• RecCu, CuRecOv

• TAuB, FCAuT,

AuRec

‘Met’ Variables

• TAgB, AgRec

• AcidB, TAcidTail,

TAcidCon,

TAcidMG,

TAcidBurn,

• TU3O8, Ba, TotS

• FCU3O8,

TFCU3O8,

TFTU3O8,

• FTU3O8

• TU3O8CL,

TU3O8TL,

TU3O8OL,

U3O8Rec

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Block Model Estimates

using geostatistical methods.

Predicted on each block.

Predictors developed from

metallurgical testing and

OD processing experience.

Geomet: Converting Data into Knowledge

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Geomet

Database

Resource

Database

Stope

Database

Geomet Inputs in Mine Planning Cycle

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Embedding Geomet Data and Knowledge into the Mine Planning Cycle:

• To the mining planner, geomet data is just another variable.

• Don’t change mine planning and scheduling practices or workflows. Don’t create extra work.

• Embed the geomet data into workflows so that it can be managed by their existing systems.

• Significant advantage: Geomet and Mine Planning Teams in the same working group.

• Relentless persistence does pay off.

Highlights:

• Geomet parameters are used to establish:

– value-based grade descriptor: influence stope design for long/medium-term planning

– scheduling constraints: influence short-term production planning

• Geomet predictors are also used fundamental inputs into:

– process plant design criteria

– processing production planning and budgeting

Medium and Long Term Planning

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Medium and Long Term Mine Planning interact directly with the Geomet Block Model

• >Two years, monthly or annual.

• Geomet data is just another variable.

• Project scenarios, ‘first look’ at new areas.

• Not constrained by mining method.

• Can be used as a planning constraint depending mine planning software. F

Y12

FY

13

FY

14

FY

15

FY

16

FY

17

FY

18

FY

19

FY

20

FY

21A

s i

n F

C, m

on

thly

co

mp

osit

e,

pp

m

Arsenic

As (measured, FC)

As (predicted, FC)

As (measured, FF)

As (predicted, FF)

Short Term Planning

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Short Term Planning (mine and process plant):

• Only use stopes designed in the Stope Reserves Database (SRDB).

• Use the geometallurgy module of the SRDB.

• Run geomet-specific reports.

• Use the commentary to understand likely behaviour of new stopes.

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Case Study: Emerald186

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Measured data:

• Determine samples local to the stope design.

• Collate measured data from the Geomet database.

• Check performance of the Geomet model at the sample level.

Geomet Database

measured data

GM0789-0792

GM1111

Stope

Reserves

Database

Emerald186

Intersecting

samples

identified from

design

Geomet

Predictive

Models

Measured v Modelled

(samples)

Applicability of

predictive model

locally

Case Study: Emerald186- geomet samples

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diamond drill holediamond drill hole

geomet sample intersecting stope

geomet sample near stope

Case Study: Emerald186 - BWi

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Geometallurgy database – sample results

Sample Measured BWi (kWh/t) Relative error of modelled

result for sample

GM0789 17.4 1%

GM0790 18.3 2%

GM0791 17.4 3%

GM0792 18.5 1%

GM1111 16.4 6%


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Block model data:

• Evaluate the weighted average value (block by block) from the stope design and the block model

• Mine planning software does this evaluation

Stope

Reserves

Database

Emerald186

Block Model &

Med-term

scheduling

package

Emerald186

Result from block

model for design

stope shape

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31860N

31850N

31870N

31880N

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Emerald186: 31850N (looking north)Stope dimensions ~30mx30mx100m

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Predictive model at stope level:

• Determine the average value of input variables for the stope design (from the block model and SRDB)

• Evaluate the result of the geomet predictive equation

Stope

Reserves

Database

Emerald186

Block Model &

Med-term

scheduling

package

Emerald186

Average

result for

variable/s

Design

Geomet

predictive

model

Result from

predictive

equation

Case Study: Emerald186 - BWi

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Geometallurgy database – sample results

Sample Measured BWi (kWh/t) Relative error of modelled

result for sample

GM0789 17.4 1%

GM0790 18.3 2%

GM0791 17.4 3%

GM0792 18.5 1%

GM1111 16.4 6%

Stope Reserve Database – geometallurgy predictive equations

Emerald186 17.6

Mine Scheduling – block model with stope design

Emerald186 17.6


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Determine result and write to Stope Reserves Database with commentary

Geomet: Converting Data into Knowledge

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Geomet

Database

Resource

Database

Stope

Database

Conclusions

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Acknowledgements

All the mining engineers in the Olympic Dam

Resource Planning and Development team!

18 February 2016Embedding Geometallurgy into Mine Planning Practices

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Documents

Embedding Geometallurgy into Mine Planning Practices … · Statement of Mineral Resources 18 February 2016 Embedding Geometallurgy into Mine Planning Practices 2 Mineral Resources