pmd CRC

Numerical Numerical ModellingModelling in Predictive in Predictive Mineral DiscoveryMineral Discovery::

Geochemical ModelsGeochemical Models

F1-2 pmd Team

Thursday 4th September 2003

pmd CRC Key F1/2 WorkflowKey F1/2 Workflow• Modelling mineral deposit geology and fluid processes using

equilibrium reactor approach (HCh software)• Developing ‘user friendly’ front (ELF) and back (PIG) ends

for HCh, easier problem definition and easier result viewing

• Developing algorithms to model key processes in ore deposit formation with both academic and industry use in mind

• Approaching this development work with the ‘computer grid’ visions of the M-people (“The Matrix”?), and the ‘industry use’ visions of the pmd*CRC sponsors

pmd CRC HChHCh--what?what?

• HCh (Yuri Shvarov & Evgeniy Bastrakov) uses ‘gibbs energy minimization’ to locate the equilibrium point of any system. This is a different approach from log K-type modelling, although the end point should be the same result.

• Advantages to pmd:• Powerful and flexible algorithm generator that can be used to

model a much wider range of geological (fluid-rock) scenarios.

• Well maintained high PT thermo datatset that will soon be ‘online’ thanks to GA developers.

• Close collaboration with code developer(s) that provides greater potential in future development directions – industry focus!

pmd CRC HChHCh conceptconcept

A+BA BInput

P-T PathControl

Output

pmd CRC HChHCh Control FileControl File

• The HCh control file uses simple algebraic notation to handle the interaction between the input systems and PT.

– [*] = [1]+([2]*10^(i-6))

• Key workflow problem: • Geological/ore deposit process concept Algebraic

control algorithm

• The control file manipulation will become more straightforward in ELF (or daughter of ELF), and all plotting needs will be handled by PIG (due Nov 03).??

pmd CRC Examples from Ernest Henry Examples from Ernest Henry

Chalcopyrite rich (higher grade) core

More pyritic rims

Biotite halo

pmd CRC EHM Mixing: Actual EHM Mixing: Actual vsvs 1D Model1D Model

Much better result if Much better result if Au with HCOS fluid, Au with HCOS fluid, Cu with brine, and Cu with brine, and 10% 10% wallrockwallrock as buffer

Pyrrhotite

Chalcopyrite

Pyrite

Plagioclase

Actinolite

Calcite

Titanite

Chlorite

Muscovite

K-feldspar

Magnetite

Biotite

Quartz

Gold

as buffer

KK--altered host volcanic altered host volcanic

MixingD1b

QuartzMagnetiteRutile

Ilm PyPo

CpyMuscovite

K-fspar

(Gold)

BiotiteChlorite

0%

20%

40%

60%

80%

100%

5 6 7 8 9 10 11 12 13titration stepbrine HCOS

Actual paragenesis Actual paragenesis

Gold co-precipitates with cpy

pmd CRC 2D Grid simulations: Assemblage plots2D Grid simulations: Assemblage plots

2D grids of 3 component mixing (Fluid-Fluid-Rock)

A B

C C

Incr

easi

ng ro

ck c

ompo

nent

Fluid Mixing (XB)

pmd CRC

HCOSHCOS BrineBrine

XRoc

kXR

ock

PyritePyrite ChalcopyriteChalcopyrite

pmd CRC Control File AlgorithmsControl File Algorithms

1

2

550oCAu-HCOS

450oCCu-Brine

450oCHm-bearingvolcanics

* * * Primary wave * * *T = 450P = 2500[*] = [1]+(0.1*[5])+(0.1*[8])

General step...T = (i/60)*550+(1-i/60)*450P = 2500[*] = ([1]*(1-(1/60)*i))+(([2]/2)*((1/60)*i))+(0.1*[5])+(0.1*[8])Stop when: i=60

* * * Secondary wave * * *T = 450P = 2500[*] = {A}+(0.1*[5])+(0.1*[8])

General step...T = 450P = 2500[*] = {A}+((0.1*[5])+(0.1*[8]))Stop when: i=60Stop when: N=40

• Control file algorithms can become quite complex in order to model key fluid-rock interaction concepts

• This example mixes two fluids in the presence of rock and looks at the passage of outflow fluid into wall rock

• Once you understand the concepts behind the control file you can conceptualise a large range of ore-forming processes

pmd CRC 2D Grid simulations: Outflow models2D Grid simulations: Outflow models2D grid of dependent reactors (i.e. fluid originates in mix zone)

A B

C C

Fluid Mixing (XB)

Dis

tanc

e in

to w

all r

ock

pmd CRC

pmd CRCIdeal Solid Solutions

Cross check prediction vs reality

pmd CRC Alteration patternsAlteration patterns

• Alteration patterns in the outflow zones form the fluid mixing site vary slightly depending on the proportion of brine:HCOSfluids that mixed.

• The most distinctive change in major element mineralogy is associated with the reactions between Muscovite = K-feldspar +-Biotite

• Use this relationship generate plot for:

– biotite/(biotite + muscovite + k-feldspar) (volumes)

– Termed X(Bt*)

pmd CRC X(BtX(Bt*) values*) values

gold chalcopyrite

Modeling is iterative → Reality checking!

Relating this value to actual distance is dependant on the amount of fluid the rock sees (time integrated fluid flux) – Need feedback from fluid-flow models

pmd CRC X(BtX(Bt*) tracking mix fluid dominance*) tracking mix fluid dominance

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Mass Fraction Brine

X(B

t*)

Background rockChange in the mixing zone

Slice showing XBt along the mixing zone

pmd CRC 2D Grid simulations: Flow Through2D Grid simulations: Flow Through

N0

2D grid of fluid flow through reactor – time/distance relationships

Rock

Flui

d so

urce

at

cons

tant

flux

N0

Nx

Nmax

Ix

Time increasing

Distance increasing

f/r is constant at any one time but ‘time’ integrated f/rchanges.

• Rock unit can contain multiple rock units so fluid can flow across geological boundaries

• Horizontal section – Snap shot in time

• Vertical section – Time evolution of a point in space

• Diagonal section –evolution of infiltration front

pmd CRC Regional Regional AlbitisationAlbitisation FlowFlow--throughthrough

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Distance along infiltration column

Volu

me%

min

eral

s

Albite

Anorthite

Qtz

Ksp

Bt

Cpx

Act

Fluid infiltration to step 21Fluid infiltration to step 21

Korzhinskii Fronts

• Infiltrate Na-modified granite fluid into Ca-silicates

• Fluid infiltration front is at step 21

• Background ‘dry’ rock beyond, saturated rock behind

pmd CRC Fluid chemistry in frontsFluid chemistry in fronts

0.0E+00

5.0E-05

1.0E-04

1.5E-04

2.0E-04

2.5E-04

3.0E-04

3.5E-04

4.0E-04

4.5E-04

5.0E-04

0 5 10 15 20 25 30 35Reaction Step

Fe (m

olal

)

0.0E+00

5.0E-02

1.0E-01

1.5E-01

2.0E-01

2.5E-01

3.0E-01

3.5E-01

Na

& K

(mol

al)

Total Fe(aq)Total K(aq)Total Na(aq)

• Predicted pattern of Fe, Na and K in chemical fronts behind infiltration fornt

• What do FLINCS represent?

pmd CRC AlterationAlteration

• Association of regional Na-Ca alteration with ore-bodies

• Ore proximal K alteration

469000E

7738

000N

470000E 471000E

7739

000N

7740

000N

1 km

N

projected position of albitite geochemistry traverse

Calc-silicate, psammite, schist

Intermediate volcanic rocks

Ernest Henry Diorite

Rock boundary

Fault or shear zone

Magnetite ± biotite alteration

Ernest Henry orebody

Minor (2-15%)

Major (15-30%)

Intense (> 30%)

Na ± Ca alteration

Ore related alteration

Potassic alteration (Kf ± mu, qtz, cc, py)

Breccia, cm- to dm-scale clasts

pmd CRC Effluent modelsEffluent models

QuartzMagnetite

Rutile

AndalusiteMuscovite

K-Feldspar

Clinopyroxene

Actinolite Biotite

Chlorite

Albite

Anorthite

0%

20%

40%

60%

80%

100%

Biotite

Mixing K-, Fe-enriched albitite effluent fluid with pelite�after dropping some Ca as caclite veins

8.48

5.40

4.40

3.40

2.39

1.42

0.43

-0.5

5

-1.5

5

-2.5

5

-3.5

5

Log volumetric f/r

•Effluent fluid run through pelites can produce alteration similar to proximal gangue assemblages in many of the Fe-ox-Cu-Au deposits

pmd CRC Linking GWBLinking GWB--HChHCh

• Developed software utilities that allow the same thermo dataset to be used for GWB and HCh.

• Plot high PT a-a and T-a diagrams to explore potential geochemical pathways (based on mineral assemblages) before going to fluid-rock modelling.

• Linking software will become a web service towards the end of the year.

pmd CRC The Wallaby ExampleThe Wallaby Example

MtPo+(Py) Mt-Py-(Hm)

300oC 450oC

Whats the geochemical pathway?

1 2 3

pmd CRC

–10 –5 0 5 10200

250

300

350

400

450

500

550

log a SO2(aq)/H2S(aq)

T (°

C)

Magnetite

Hematite

Pyrrhotite

Pyrite

jc138398 Tue Jul 29 2003

S-poor

–10 –5 0 5 10200

250

300

350

400

450

500

550

log a SO2(aq)/H2S(aq)

T (°

C)

Magnetite

HematitePyrrhotite

Pyrite

jc138398 Tue Jul 29 2003

S-Rich

?

• Example from wallaby where GWB-HChlinking is proving an important work tool

pmd CRC Gold solubility (log Gold solubility (log mm))

–10 –5 0 5 10100

150

200

250

300

350

400

450

500

550

log a SO2(aq)/H2S(aq)

T (°

C)

H2S(aq)

SO2(aq)

Sulphur

Magnetite

Hematite

Pyrrhotite

Pyrite

Jc140209 Tue Sep 02 2003

Diag

ram

Fe++

, a

[main

] =

10

–3,

a [H

2O]

=

1, a

[H 2S

(aq)

] =

10

–1 (

spec

iates

), p

H

= 4

.5 (

spec

iates

), a

[A

u+ ] =

10

–5;

Sup

pres

sed:

Tro

ilite

-8-9 -10

-7-6-5

-4.5

-5

-4

-3

-2

pmd CRC 3D Gold Solubility Surface3D Gold Solubility Surface

pmd CRC Gold vectors to precipitationGold vectors to precipitation

–10 –5 0 5 10100

150

200

250

300

350

400

450

500

550

log a SO2(aq)/H2S(aq)

T (°

C)

H2S(aq)

SO2(aq)

Sulphur

Magnetite

Hematite

Pyrrhotite

Pyrite

Jc140209 Tue Sep 02 2003

Diag

ram

Fe++

, a

[main

] =

10

–3,

a [H

2O]

=

1, a

[H 2S

(aq)

] =

10

–1 (

spec

iates

), p

H

= 4

.5 (

spec

iates

), a

[A

u+ ] =

10

–5;

Sup

pres

sed:

Tro

ilite

Au-C

l

Au-OH

Au-HS

Au-HSWe can now test different processes in reactor models which will account for all combinations of vectors together

Au-Cl complexes

Au-S complexes

0.001 m ΣS

3 m ΣCl

pmd CRC Future ChallengesFuture Challenges

• Software development that will increase the usability of geochemical modelling, including inputs, conceptualisation and data visualisation.

• Integrating fluid-flow (deformation) results as constraints on inputs/results.

• Integration of data to and from other pmd*CRC projects to increase effectiveness of whole modellingprocess.

• More ‘reality checking’ to better improve predictive nature of this type of modelling (where are we right, how mush have we predicted, where do we need to improve)?