mining industry consultants - managing risk with quantitative 3d … · 2019-11-21 · risk 2...

BY:

Managing Risk With Quantitative 3D Mineral System Characterisation

Warren Potma

Scott Halley, Andrew Scogings, Serik Urbisinov, Carl Brauhart, Nikita Sergeev, Angela Escolme.Thanks also to Hot Chili Ltd.

Seequent Lyceum 25/10/2019

Risk

2

Effective Mineral Exploration & Development (Technical) Risk

Management involves:

Minimising the time, cost and uncertainty while

maximising the expected value

associated with each decision point

throughout the exploration & mining value chain.

Critical decision points must be underpinned by

the right information at the right time

to maximise the value multiplier…..

Perceived Cost vs Real Cost….. in a Technical Risk Context

3

Probability

of progression

to next stage

100%

0%

Expenditure

Histogram

(Log-scale)

High $$$$

Low $

X 2

50

Ta

rge

ts

X 5

00

Pro

ject

s

X 3

X 1

.5

Current

Probability

Desired

ProbabilityRisk Actual expenditure at

each stage to generate

a development project

Perceived expenditure

at each project stage

Technical Risk

Current

Probability of

progression

Desired

Probability of

progression

Integrated GEOmet / 3D Mineral System Characterisation Approach

4

Inputs

Ore

Bo

dy

Kn

ow

led

ge

4 Acid ICP-MS Geochemistry

Alt’n-Pathfinder-Litho-Geochem

Calculated Mineralogy

Mineralogy: Spectral, Petro & XRD

Appropriate Quantitative Mapping & DH logging data capture

Structure, MagSus, Density, pXRF, Acid Fiz, Hardness, quality photography...

High quality data management

Live (implicit) 3D models

Simplified fit-for-purpose 3D models Lithology – Mineralogy –

Payable/Deleterious Elements -Weathering – Rock Mass

Applications

Exploration

Greenfields – brownfields – mine - shoot

3D Modelling

Litho-structural – Alteration - Resources

Metallurgy

Quantitative 3D Mineralogy

Truly Representative Met. Composites

Mine Design

Rock Mass Classification – Geotech –Blasting - Scheduling

Process Design

Optimisation – Scheduling - Blending

Environmental

Acid Mine Drainage – Mine Closure

Ground Selection

Quantify strategic decisions

Impact

Cradle to Grave Unifying link across Mining Value Chain

Philosophy: to provide critical

knowledge EARLY in project

lifecycle for maximum value

Costs savings at every stage from

improved decision making

Early warning of fatal flaws and opportunities

Informed Observational Geology

Open-minded working hypotheses

Geophysics/Petrophysics

Cost vs Value (reduce risk early in the project life cycle)

5

• Terrane/District/Project selection stage: <20% of total project costs

• Process-based mineral system framework provides structured decision matrix

• Quantitatively constrain the highest risk decision point

• Surface sampling & mapping stage: <10% of total project costs

• Avoid missing the prize and/or gamblers ruin

• Decreased risk and discovery cost at highest risk end of mining cycle

• Exploration drilling stage: <2% of total project costs

• Better constrained targeting, early quantitative models

• Significantly reduced drilling costs

• Earlier go / no-go decisions

Cost vs Value (value multiplies throughout project lifecycle)

6

• Resource definition stage: <<0.5% of total project costs

• Reduced discovery costs (improved drill design)

• Better MRe confidence/classification

• Early warning of threats and opportunities

• Feasibility study stage: <<0.01% of total project costs

• Reduced risk (truly representative material data/models & Met samples)

• Reduced cost (optimised Met test-work, avoid costly rework)

• Reduced time (all GEOmet classification & modelling complete before feasibility)

• Better Reserve Block Models (quantified material properties for every block)

Acquisition/Exploration Stage

7

Don’t miss the DISCOVERY/DEAL OPPORTUNITY(<10% of all project costs at this stage)

Collecting the right data enables quantitative decision-making

Blind VMS example:

Value of low level

multi-element

pathfinder geochem.

If assays were limited

to Cu, Pb, Zn this target

would have been

missed.

(Halley 2016)

VMS stringer zone. Cu is in chalcopyrite;

restricted to sparse veinlets.

Sb is in the lattice of the pyrite.

Maps the footprint of the stringer zone.

Projected position of

blind massive sulphide

VMS

horizon

Target Invisible with

traditional base metal

geochemistry

Footwall

stratigraphy 1km

Clear anomaly pointing

to blind target

Advanced Exploration Stage

8

Bismuth assays by ICP-MS, detection limit 0.01ppm

Blind Porphyry Cu deposits

300m below surface

Outcropping

Porphyry Cu

deposit

Bi_ppm ICP-MS

0.01ppm Det. Limit

Bismuth assays by ICP-AES, detection limit 5ppm

Bi_ppm ICP-AES

5ppm Det. Limit

Don’t miss the GROWTH OPPORTUNITY(<2% of total project cost)

Inappropriate geochemistry methods lead to missed opportunities

Target is invisible

to the wrong

assay method

Target is obvious

with appropriate

analysis method

3D Mineral System Characterisation – “Ore Body Knowledge”

9

Inputs

Ore

Bo

dy

Kn

ow

led

ge











Applications

Exploration


3D Modelling


Metallurgy



Mine Design


Process Design


Environmental


Ground Selection


Impact











Productora (Cu-Au-Mo) Example: Exploration Value Add

10

Productora

Uranium Play? -> Cu-Au-Mo: IOCG? -> Magmatic

Hydrothermal Breccia -> PCD Discovery

?

3D Calculated Mineralogy Model

• Identified problems with existing geological model, changed exploration focus (twice)

• 3 new discoveries, Resource increases from ~100Mt to ~260Mt, 90 Mt maiden Reserve

• Maiden porphyry copper discovery - directly impacted PFS outcomes

Orebody knowledge delivers “easy” discoveries

11

+0.3% Cu

Mineralisation

Rocoto

Discovery

Rocoto Discovery

Productora Central Pit Area

Section 6821840mN

Preliminary

Central Pit

Design

97m@ 0.6% Cu, 0.1g/t Au, 222ppm Mo including:

25m@ 1.1% Cu, 0.2g/t Au, 342ppm Mo

86m@ 0.5% Cu, 0.1g/t Au, 226ppm Mo including:

8m@ 1.4% Cu, 0.3g/t Au, 384ppm Mo

18m@ 0.8% Cu, 0.2g/t

Au, 193ppm Mo

K-Spar Alteration

Vector

Cu

Proximal

Alteration Classification

Kspar

alteration

(proximal)

Sericite

alteration

(intermediate)

Sericite-albite

alteration

(intermediate)

Albite

alteration

(distal)

Feb 3 2014

Resource Definition Stage

12

Don’t miss the opportunity to REDUCE COSTS, IMPROVE RESOURCE BASE, INCREASE CONFIDENCE,

SAVE TIME and GET EARLY WARNING OF FATAL FLAWS and OPPORTUNITIES

(<0.5% of total project cost)

Resource Extensions: East-dipping Habanero discovery

13

The 3D alteration mineralogy model indicated there should be more Cu, and it looks like its East-dipping?!

Habanero Discovery


Section 6822215mN

Preliminary

Central Pit Design

+0.3% Cu mineralisation


OPEN

Early 2013

72m@ 0.7% Cu,

36ppm Mo

Resource Extensions: East-dipping Habanero discovery

14

Well…… that changes things a bit!Habanero Discovery


Section 6822215mN

Preliminary

Central Pit Design



100m@ 1.0% Cu,

0.3g/t Au, 108ppm Mo

181m@ 1.0% Cu, 0.3g/t Au, 173ppm

Mo (open to end of hole) including:

71m@ 1.6% Cu, 0.4g/t Au, 229ppm Mo

OPEN

Oct 3 2013

49m@ 1.0% Cu,

0.1g/t Au, 207ppm Mo

Feasibility Study Stage

15

Feasibility study stage: <<0.01% of total project costs

Potentially save $Millions…..or….. avoid a possible company killer

• Reduced risk (truly representative material data/models & Met samples)

• Reduced time (all GEOmet classification & modelling complete before feasibility)

• Reduced cost (optimised Met test-work, avoid costly rework/iterations)

• Better Reserve Block Models (quantified material properties for every block)

• Optimised process design (quantified plant feed properties and scheduling)

Feasibility Study Time

16

Potassic Tourmaline Breccia Cu-Au-Mo Ore:

• Majority of ROM ore

• Variable hardness, density & abrasiveness

• K-spar cement dominant - dense and hard

• Tourmaline breccia - soft, friable, abrasive

• Highly variable weathering – ox-trans-fresh?

• Strong localised structural/breccia controls (GC

complexity)

Metallurgist:

“I want a representative bulk sample….. please”

BOCO

TOFR

GEOmet domain considerations

17

Habanero Ore Zone:

• Anomalously E-dipping (Mining)

• Very high Cu grade (Mill feed variability)

• Locally high pyrite content (AMD)

• Relatively soft (Low BWI)

• Sericite + Clays (Float sliming?)

• Clay-rich shears & foliation (Geotech)

BOCO

TOFR

It’s not just about the ore…..

18

Advanced Argillic Domain:

• Silica-Clay (+/- sericite/pyrite)

• Dominantly waste – Pit high-wall

• Sharp E-dipping contact with HG ore (Mining)

• Deep weathering – highwall geotech

• Localised clay-rich shears/foliation (Geotech)

• Very hard silicification (Blasting/Drilling)

• Abundant cavities in oxide zone (Water!)

BOCO

TOFR

When the horse has bolted…..

19

Inputs

Ore

Bo

dy

Kn

ow

led

ge











Applications

Exploration


3D Modelling


Metallurgy



Mine Design


Process Design


Environmental


Ground Selection


Impact











Remedial GEOmet: A complex skarn mid-Feasibility Study

20

Client Requirements:

Deliver the “least complex 3D geometallurgical domain model possible” for a complex Sn-fluorite-Cu-W Skarn deposit that:

• is representative of the critical domains affecting processing, including:

lithological, alteration and ore mineralogy; oxidation and weathering-related

mineralogy; hardness; abrasiveness; grainsize and textural characteristics

• adequately informs metallurgical sampling strategy

• Delivers a reliable, quantitative, 3D GEOmet model as “required to underpin confidence in the Feasibility Study for this perceived marginal Resource”

Modelling material types that impact processing & mining

21

Client’s geological model:Based on detailed logging

Calcareous-

Sediments

Mg Dolomitic-

Sediments

Granite

Mafic

Dykes

ORIG-TIRR

WCR-RUBB

WCR-CLY

SANCLY

LOAM

GRN

WCR_GRN

MTS_GL_3_1

MTS_AND

MTS_MG

NW SE

150m

150m

Simplified Protolith Model:

based on quantitative

lithogeochemistry focused on

mineralogy that might impact

processing

Classifying mineralogy that impacts processing & mining

22

K-spar or Biotite

Muscovite

Kaolinite

Least Altered Granite

Least Altered Sediments

& Mafics/Andesites

Point Density Cloud

Modelling mineralogy that impact processing & mining

23

• Highest-grade Sn mineralisation is spatially correlated with

Quartz-Topaz-Kspar (and/or Biotite)- Mt Skarn +/- Muscovite

• Lower grades occur with Sericite-Chlorite-Magnetite Skarn

• Gangue Mineralogy requires further simplification for GEOmet

Simplify 3D models to focus on what matters!

24

Simplified GEOmet Classification:

• based on quantitative gangue

mineralogy characterisation

(using ICP-MS, XRD, SWIR,

petrography and logging data)

• is simplified to focus on critical

material attributes that

potentially impact processing and

mining.

• Must be Mineable!

• Accuracy over Precision

Modelling material types that impact processing & mining

25

Calc-Silicates

Granite

ORIG-TIRR

WCR-RUBB

WCR-CLY

SANCLY

LOAM

GRN

WCR_GRN

MTS_GL_3_1

MTS_AND

MTS_MG

Qtz-Topaz

Fe-Skarn

Simplified GEOmet Domains

that impact processing & mining:

based on calculated gangue

mineralogy from

lithogeochemistry & spectral

mineralogy

NW SE

150m

150m

Client’s geological model:Based on detailed logging

Value of live implicit 3D models to guide decision making

26

• Accuracy and timeliness is more important than precision and geometric detail

• Design of metallurgical test work holes and selection of representative sample intervals

• Volumetric estimation of material types

• Re-population of model with Met test results (BWi, Abrasiveness, recoveries, energy & reagent consumption etc.,)

• Updating Reserve Block model with material type variables to enable real block value to be estimated for every block

Quantitative weathering domain model

27

Transitional-Oxide Domain

Sulphide Domain

ORIG-TIRR

WCR-RUBB

WCR-CLY

SANCLY

LOAM

GRN

WCR_GRN

MTS_GL_3_1

MTS_AND

MTS_MG

Quantitative Weathering Domain Model

Clients Geological Model

Intense Supergene Clay Domain

NW SE

150m

150m

Quantitative weathering domain

model, based on:

• ME geochemistry

• SWIR Spectral Mineralogy

• qXRD validation

• Core photo validation

• Comparisons with logging

Client’s weathering model:Implicit in geological model

Based only on qualitative

observational geology

Impact of early quantitative 3D mineral system characterisation

28

The skarn GEOmet project “demonstrated that most of the ~$250K preliminary metallurgical test work (predating the GEOmet project) was essentially wasted”

(Client’s independent consultant metallurgist)

Productora 3D Mineral System characterization contributed to tripling the existing

Resource, completely changed the geological model resulting in discovery of the

porphyry copper mineral system

GEOmet approach provides key information at critical decision points in the project

life cycle

Characterise more, characterise earlier & test less

3D GEOmetallurgy – can you afford NOT to do it?

29

Inputs

Ore

Bo

dy

Kn

ow

led

ge











Applications

Exploration


3D Modelling


Metallurgy



Mine Design


Process Design


Environmental


Ground Selection


Impact











Our Global Offices

Vancouver, Canada

Toronto, Canada

Horsham, UK

Moscow, Russia

Dubai, UAE

Singapore

Jakarta, Indonesia

Perth,

Western Australia

Darwin,

Northern Territory

Brisbane,

QueenslandJohannesburg,

South Africa

Dublin, Ireland

Kalgoorlie,

Western Australia

30

mining industry consultants - managing risk with quantitative 3d … · 2019-11-21 · risk 2...

Documents