Exploration Sampling

A Primer for the Papers that follow.

Andrew White Andrew White & Associates

• • Constraints on sampling geology: Geology is fractal (somewhat).

Classification systems for – – Minerals and mineralogy, Rock type and implied genesis, – – Tectonic setting, Deformational history, – Age.

• • • Concept of domain: “any finite three dimensional portion of a rock body that is statistically homogeneous on the scale of the domain” (Turner and Weiss).

Concept that within a domain a sample will be (more or less) representative of the whole domain.

• • • Very significant research into ore genesis. Including definition of the domains that define various ore deposits.

We geologists have a reasonable understanding of many ore types, but still expect the unexpected.

• • • • Geology presents a problem for statistical analysis: It is 3-dimensional.

Geostatistics was invented to deal with the problem and works (very well in most instances).

However a geostatistical approach depends on the existence of an adequate data set.

• • • Sampling theory and sampling practice are well promoted.

The issue especially in early stages of assessing a discovery is what sampling to do?

Sampling is almost always a trade-off between sampling to gain sufficient confidence in an ore deposit, and the cost of sampling.

• • • • • • • • • • • • • ➤ ➤ ➤ ➤ In situ Nugget Effect (NE) Fundamental sampling error (FE) Grouping and segregation errors (GE) Long-range heterogeneity (quality) fluctuation error (shifts and trends, QE1) ➤ Long-range periodic heterogeneity (quality) fluctuation error (cycles, QE2) ➤ Increment delimitation error (DE) ➤ ➤ ➤ ➤ Incremental extraction error (EE) Weighing error (WE) Preparation error (PE) Analytical error (AE).

Pitard’s (2005) list of the ten sources of sampling error.

• • • • The major focus of any sampling early in assessment of a discovery is grade.

There is a trend towards reliance on a geostatistical appraisal of sample data to determine different categories of resource.

Most geostatistical appraisals naturally tend to focus on grade and grade distribution.

The JORC Code demands experience be a factor in resource estimation.

• • History is replete with examples of mines that failed outright, and mines that failed to meet planned completion dates for production rates and product grades (and these are still happening).

The focus just on grade is an invitation for more failures/delays in commissioning to occur.

• • • Not rocket science, just obey White’s First Law of Mining.

White’s First Law of Mining: – First understand the geology of the beast you are dealing with (in other words exercise the appropriate level of geological due diligence).

Before sampling for geostatistical study of grade distribution is carried out, it is critically important to define the fundamentals of the mineralisation.

• • • • Exploration geologists typically work at visual scales.

Much care and attention goes into appropriate drilling methodology and sample logging, but at visual scale.

Assaying exploration samples is normally (or should be) quite comprehensive given the scope and low cost of analytical methods based on ICP.

Mine geologists/resource estimators complain than explorers still don’t sample with mine widths in mind.

• • • Do their own microscopy.

Look at mineralogy and texture as well as grade (except at an basic level).

Look for potential pit falls (or bonuses) in mining and mineral processing that could early be identified.

• • The first four of Pitard’s list of the ten sources of sampling error are of most immediate concern to exploration geologists.

Sampling in early stages of discovery can be very challenging.

• • • • • • ➤ In situ Nugget Effect (NE) ➤ Fundamental sampling error (FE) ➤ Grouping and segregation errors (GE) ➤ Long-range heterogeneity (quality) fluctuation error (shifts and trends, QE1)

• • • One of the first challenges is to understand the nature of freshly discovered mineralisation, often a process of trial and error (Olympic Dam).

Plenty of examples where early drilling was incorrectly oriented because controls on mineralisation were not understood (Bronzewing, Mt Charlton).

Some examples where misunderstanding led to the discoverer missing out on its value, or delay in scoping the discovery (Alumbrera).

• Mineralogy will affect mine economics as it impacts on: – Metallurgical recovery.

– Grade control methodology.

– Comminution.

– Stability of mine workings.

– Environmental impact management.

• • Mineralogy – Ore and gangue minerals.

– The most important feature to identify first.

Techniques - whatever it takes, but: – – Microscopy.

SEM.

– QEMSCAN®

• • Is the gold “free” or locked up in pyrite or arsenopyrite as sub microscopic blebs?

Is the tin assay reflecting tin in – cassiterite, – stannite, – or tin in the lattice of magnetite or gahnite?

• Fabric and structure of ore and gangue: – Affect mine design (indeed, amenity to mining).

– Mining method.

– Grade control.

– Blasting and comminution.

– Mine stability.

– Ground water management during mining.

• • • • Degree of heterogeneity.

– Ore mineralogical distribution.

– Grade distribution.

Location of minerals that impede mineral processing.

Upgrade or processing “game changers”.

Weathering/oxidation and associated mineralogical domains.

• Ore textures and grain size.

– – Affecting comminution.

Liberation and hence process design.

– Recovery.

How to allow for this (especially if it is invisible)?

Which half to sample?

• • • • Clays.

Carbon, talc. Coatings on minerals – Carbon on pyrite framboids/crystals in Mt Isa ores.

– Chalcopyrite coating on galena grains in Thalanga ores.

Impurities in ore mineral grains – Manganese in sphalerite in Dugald River ore.

– Illite and bitumen in sphalerite and galena, McArthur River ores.

– Goethite in gibbsite lattice in bauxite.

• • • • At worst, mine failure at the outset.

Commonly, delay in commissioning new mines to achieve planned production while unforseen (likely missed in sampling) problems were sorted.

Of 13 new metalliferous mines commenced in Australia in the last ten years, at least three failed due to issues with ore or gangue mineralogy – not a good statistic.

One of the failures (Ravensthorpe) caused a massive $3.6bn write-down for the owners.

• Discounted cash flow modelling of two hypothetical mines each with capex of $100M: – A low-grade (1.3g/t) open pit mine with 6 year life.

– An open pit high grade (5.6g/t) mine with 10 year life.

– Impact of 1 year delay in receipt of revenues due to commissioning delay while unforseen metallurgical (geological) issues were fixed.

• • • For low grade mine, NPV at 8% reduces from $48M to zero (IRR reduces from 24% to 8%).

For high grade mine, NPV at 8% reduces from $308M to $278M, IRR from 71% to 40%.

In each case question must be asked how will the year’s delay be funded if indeed it can be?

• • The absolute NPV and IRR values are not that important – they provide a guide.

The modelling highlights the greatly increased risk attached to low grade short life mines if there are delays in commissioning, especially if the delays require extra funding (as they normally do).

• • • Mine failures don’t lift the profile of mining in the eyes of Governments, investors, public perceptions.

Government reaction to failure is generally to increase permitting thresholds since they fear being locked into cleaning up the environmental and social impacts of failure.

There is a heavy responsibility on geologists to do their due diligence and get the basics right.