Geometallurgy at Olympic Dam

Transcription

1 Replace with a photograph 2 nd December 2011 Kathy Ehrig Principal Geometallurgist

2 Disclaimer Reliance on Third Party Information The views expressed here contain information that have been derived from publicly available sources that have not been independently verified. No representation or warranty is made as to the accuracy, completeness or reliability of the information. This presentation should not be relied upon as a recommendation or forecast by BHP Billiton. Forward Looking Statements This presentation includes forward-looking statements within the meaning of the U.S. Securities Litigation Reform Act of 1995 regarding future events and the future financial performance of BHP Billiton. These forward-looking statements are not guarantees or predictions of future performance, and involve known and unknown risks, uncertainties and other factors, many of which are beyond our control, and which may cause actual results to differ materially from those expressed in the statements contained in this presentation. For more detail on those risks, you should refer to the sections of our annual report on Form 20-F for the year ended 30 June 2011 entitled Risk factors, Forward looking statements and Operating and financial review and prospects filed with the U.S. Securities and Exchange Commission. No Offer of Securities Nothing in this release should be construed as either an offer to sell or a solicitation of an offer to buy or sell BHP Billiton securities in any jurisdiction. Non-GAAP Financial Information BHP Billiton results are reported under International Financial Reporting Standards (IFRS). References to Underlying EBIT and EBITDA exclude net finance costs and taxation for jointly controlled entities, and exceptional items. A reconciliation to statutory EBIT is contained within the profit announcement, available at our website Slide 2

3 Today s Presentation Olympic Dam Summary Olympic Dam Mineralogy Key Process Drivers Impacted by Mineralogy Olympic Dam Geometallurgy Mineral Mapping Conclusions Slide 3

4 Olympic Dam Operations - Summary Discovered by WMC in 1975 Production commenced 1988 Mechanised sublevel longhole open stope mining Hydrometallurgical treatment Grinding and concentration Acid production Smelting Cu-refining and PM production Fully integrated circuit, problems in one part impact the whole. Slide 4

5 Olympic Dam Geology - Summary Hydrothermal Fe-oxide Cu-U-Au-Ag deposit Olympic Dam Breccia Complex (ODBC) Existing underground Roxby Downs Granite ~1590 Ma ~350m of unmineralised cover sequence Cover Sequence Deposit wide zonation pattern py-cp-bn-cc gangue minerals Orebody ~6km x 3.5km x 800m Deposit exposures are limited to: diamond core: ~740 km from underground ~1,420 km in basement ~400 km of underground development no surface exposures Basement ~350m depth 1% Copper shell Slide 5

6 A few questions to consider How do we predict profitability: next year next 5 years life-of-asset (i.e yrs)? Existing underground What ore deposit characteristics impact: throughput metal recoveries reagent consumption product quality? Cover Sequence Basement ~350m depth 1% Copper shell Slide 6

7 Granite- to hematite-rich breccias RDG GRNH GRNL HEM HEMQ Si (wt%) Fe (wt%) Whole Rock Data Rock Type Continuum Slide 7

8 OD Breccias: Other Components Felsic Dykes Sediments Mafic/Ultramafic Dykes Slide 8

9 Olympic Dam Mineralogy Olympic Dam ORES (>70 minerals) Economic Minerals Sub-Econ. Minerals Gangue Minerals Cu-Sulphides (py-cp-bn-cc) (concentrate quality) Uranium Minerals (uranium recovery) Au-Ag Co-, Zn-, Mo-, REE-, As-, Se-, Bi-, Te-, Sb-, Pbbearing minerals (concentrate quality) Wide spectrum of mineral mixtures many ore types Hematite grinding Quartz grinding Sericite slime K-feldspar Chlorite Siderite acid Fluorite acid Barite +etc acid, gelling Slide 9

10 OD Breccias, Mineralogy, Metallurgy OD breccias are: texturally chaotic mineralogically simple Two Facts: 1. Ore deposits variable mixtures of minerals 2. Mineralogy primary control of metallurgical performance mineralogy effect on process QUANTIFIED process critical minerals MAPPED + metallurgical performance PREDICTED Slide 10

11 Olympic Dam Geometallurgy The primary objective of the Geometallurgy Program is to develop metallurgical performance predictors that reliably describe the process performance of different ore types and spatially distribute these into the resource block model for use as a fundamental input into mine planning. The Geomet Models are predictive mineralogy and recovery models which are applied to blocks in the Mineral Resource model to enable the estimation of mineralogy and metallurgical recovery on a block-per-block basis. The secondary objectives of the Geomet Program are to: Identify any ores which may be problematic to the current plant Provide data of suitable quality for process plant design and future plant optimisations. Chemical, mineralogical, physical property, and recovery models, collectively called the geometallurgical model, provide the data required to support the JORC code modifying factors as it applies to...the metallurgical process proposed and the appropriateness of that process to the style of mineralisation Slide 11

12 Key Process Drivers Impacted by Mineralogy Mill throughput Ore hardness (hematite-quartz-sericite) Grind size (sulphide particle size-liberation characteristics) Concentrate Grade and Quality Sulphide mineralogy (proxy = Cu:S ratio) Sulphide non-sulphide gangue composites (mineral texture) Deleterious elements (i.e. Zn, Mo, Pb, F, As, Se, Te, Bi, Sb, etc) Uranium Recovery Extraction (uranium mineralogy, particle size, and association) Acid consumption (siderite and chlorite) Gelling potential (chlorite) Smelter Throughput Heat balance (mineralogy) Slag make (mineralogy) Refinery Cathode quality (Bi, Se, As, Te, Sb) Different ore types for mill, concentrator, hydromet, smelter and refinery. Slide 12

13 Components of any Geomet Program Resource Delineation > 1% Cu +1,500,000 samples +11,000 samples +1,000 samples Resource Model Ore Characterisation Metallurgical Testing Slide 13

14 Metallurgical Testing Program Comminution BWi, DWi, Ai, limited SPI Flotation Grind establishment Rougher kinetics Cleaner kinetics Locked-cycle tests Leaching Concentrate (standard conditions) Tailings (standard conditions) Detailed Assaying of all head samples Deportment of economic, sub-economic, and deleterious elements Assays and mineralogy on a size-by-size basis: Flotation feed, concentrate, tails leach feed, and tails leach residues Metallurgical performance = ƒ (assays, mineralogy, texture, process conditions) Slide 14

15 Ore Characterisation Program Detailed Chemical Characterisation no surprises potentially economic elements process deleterious elements + Mineralogy (using MLA or QEMSCAN) μm size fraction sulphide, uranium, gangue mineralogy mineral textural data mineral (wt%) = ƒ (assays) Slide 15

16 Resource Delineation Program Routine Drill Core Assaying all drilling below unconformity is assayed includes ore, low grade, and waste all samples are assayed by the same: analytical methods suite of elements density and magnetic susceptibility measurements on all assayed samples database contains >1.5M assayed samples mineral (wt%) = ƒ (assays) sulphide and gangue mineralogy can be accurately predicted on each assayed sample Mineralogy (predicted) on 1.5M samples Very powerful dataset Predicted Minerals pyrite hematite chalcopyrite quartz bornite feldspar chalcocite sericite sphalerite chlorite molybdenite siderite galena fluorite barite Slide 16

17 Geomet Enabled Resource Model Resource model ~ 20 million blocks, each block has: Geostatistically estimated: 26 elements plus density and mag susceptibility 15 process critical minerals (geostatically estimated) Calculated on each block based on estimated elements and minerals: >50 metallurgical performance parameters assessment of a block s real value to recover metal Slide 17

18 Quantitative sulphide mineralogy, WHY? Copper sulphide smelting technology: two stage (~120 worldwide) single stage (direct-to-blister, 1 at OD and 2 elsewhere) selection is dependent on concentrate sulphide mineralogy cc bn-cc bn cv cp-bn cp cp-py Slide 18

19 Quantitative sulphide mineralogy, WHY? bn-cc 3.0 Annual Cu:S Ratio (without pyrite depression) bn bn-cp Cu:S Ratio Shape of curve varies, depending on the mine plan single-stage smelting requires Smelter change DBF hybrid cp 1.0 two-stage smelting 2-stage py-cp OD Mineral Resource Cu:S ~ Year (hypothetical, depends on mine plan) Slide 19

20 Sulphide Mineralogy depth cc bn-cc bn (cp-bn) cp py-cp py Logging/mapping & microscopic observations: occur individually or as distinct pairs over 1-5m scale pseudo-binary sulphide distribution py bn, py cc, cp cc the interface between cp and bn is a mappable surface across the deposit copper occurs in hypogene sulphide minerals only sulphur occurs in sulphides and barite Cu:S ratio used as a proxy for sulphide mineralogy pyrite = 0 chalcopyrite = 0.99 bornite = 2.48 chalcocite = 3.96 Slide 20

21 Sulphide mineral Cu:S ratio vs Cu grade Sulphide mineral Cu grade (wt%) Sulphide Minerals in Copper Deposits chalcopyrite bornite anilite digenite djurleite chalcocite pyrite Cu:S Ratio (Sulphide mineral) Slide 21

22 Sulphide mineral Cu:S ratio vs Cu grade Sulphide mineral Cu grade (wt%) Olympic Dam Cu-Fe-S Minerals (prior to mining) Produce a 'binary-mixing' mineralogical model chalcopyrite 30% py 70% cp 60% py 40% cp 80% cp 20% bn bornite 40% cp 60% bn anilite digenite djurleite chalcocite pyrite Cu:S Ratio (Sulphide mineral) Slide 22

23 Sulphide mineral Cu:S ratio vs Cu grade Sulphide mineral Cu grade (wt%) Olympic Dam Cu-Fe-S Minerals (prior to mining) Produce a 'binary-mixing' mineralogical model Cu% = CuS CuS CuS R² = chalcopyrite bornite % Cu sample = % sulphides = anilite digenite n i = 1 (% Cu % Cu % Cu djurleite chalcocite i *% Mineral ) /100 sample (max, Cu: S) % Cu sample = % sulphides *% Cu(max, Cu: S) i pyrite Cu:S Ratio (Sulphide mineral) Slide 23

24 Sulphide Mineral Maps (400m depth) Mineral % bornite chalcopyrite pyrite Slide 24

25 Sulphide Mineral Maps (550m depth) Mineral % bornite chalcopyrite pyrite Slide 25

26 Sulphide Mineral Maps (700m depth) Mineral % bornite chalcopyrite pyrite Slide 26

27 Outcomes: Quantitative Mineral Mapping Drill Core Predictive Models mineral (wt%) = ƒ (assays) recovery = ƒ (assays, minerals) Block Model 26 elements density magnetic susc Supported by: geology assays mineralogy metallurgical testing Optimal Mine Plans/Schedules 26 elements + 15 minerals density and magnetic susc + 50 metallurgical parameters * Economic return on each block * Maps $$$ 96.0% 94.0% Flotation Copper Recovery 2.50% $$$ $$$ Flotation Copper Recovery (%) 92.0% 90.0% 88.0% 86.0% 84.0% 82.0% Copper Recovery Copper Grade 2.00% 1.50% 1.00% 0.50% Flotation Copper Head Grade (%) 80.0% Financial Year for any mine plan or plant configuration Slide 27

28 Conclusions, What is in it for you? Geometallurgy Minimise Risk: Quantify how the ore will react in the process plant, e.g. throughput metal recoveries reagent consumption product quality Evaluate the economic return on each block of potential ore next year next 5 years life-of-asset (at OD +100 yrs) Investment decisions based on facts, not assumptions! Think about the recovery, revenue, and opportunity losses ($$$) when we (as an industry) get it wrong. Slide 28

29 Olympic Dam Geometallurgy Team Slide 29

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