• January 18, 2019

Benefits of Geometallurgy

Published on January 18th, 2019

Geometallurgy is the integration of geological, mineralogical and metallurgical data in 3-dimensional space to create a spatially-aware predictive mineral processing model. A geometallurgical model is an extension of the mineral resource or reserve model, and although geometallurgy originally emerged as a subdiscipline from the mineral processing world, it has earned its place as an independent discipline that promises to bring numerous benefits during the entire life of a mine. The development of the geometallurgy specialization has gained significant importance during the last 10 years, when a collapse in commodity prices and inflated operational costs at the end of the previous supercycle (ca. 2008) put pressure on mines to reduce costs and improve efficiencies.

Analytical computational technology has advanced significantly in the last decade. Geologists, mining engineers and metallurgists are now able to measure, manage and interpret a much wider range of raw rock attributes, bringing our knowledge closer to the ideal of ‘exhaustive description of the rock’.

The value proposition of the geometallurgy approach is simple and compelling. The efficiency of the extraction and mineral processing at an operation can be improved by enhancing the understanding of the spatial distribution of relevant rock and mineral properties that affect mining and processing circuits, from the design through production and eventually remediation. This typically results in simplifying the problem of accurately predicting physical and financial outcomes by identifying regions of the orebody with negative impact (deleterious elements) very early in the mining cycle as well as reducing the number of unknowns in the value-generation process and allowing business planners to make more accurate cost and revenue models, and hence more accurate valuations.

Example of the mining value chain. Source: Hunt and Berry 2017.
Figure 1: Example of the mining value chain. Source: Hunt and Berry 2017.

Benefits of a 3D-spatial geometallurgical model

Once generated, a 3D spatial geometallurgical (block) model can bring multiple benefits to the decision-makers of a project including:

  • Project de-risking, by having a detailed domain characterization that provides predictive ore behaviour during mining and processing through the life-of-mine. This translates into more accurate feasibility assessments and increasing returns through more efficient mine sequencing/scheduling.
  • Resources/Reserves optimization by providing a realistic initial domain/wire-frame for geostatistical calculations and subsequent reporting.
  • Subsequent optimization of mine and mineral processing design, thus providing a more efficient capital allocation.
  • Subsequent optimization of mine planning and scheduling, including tactical improvements to planning (block selection) and blending strategies in the short to medium term.
  • Project valuation tool for optimizing resources/reserves evaluation and early inform decision-makers during re-valuation of mine assets.

Moss Mine, Northern Vertex Mining.

Figure 2: Moss Mine, Northern Vertex Mining. Source: Google Images.

Geometallurgical Variables

Geometallurgical variables include any rock / mineral property that has economic implications on the mining business model. Geometallurgical variables can be divided into two major groups (Coward et al., 2009):

  • Primary Variables: any intrinsic rock property that can be directly measured. Examples: geochemistry; metal grade; rock density; grain size; alteration classification and strength; redox zones (i.e. spatial distribution of oxides vs sulphides); ore classification (e.g. spatial distribution of sulphide species); spatial distribution of deleterious materials (e.g. arsenic) and geotechnical variables (e.g. RQD, Fracture index).
  • Responsive Variables: these are attributes that describe the rock’s response to various processes. These variables are rarely measured directly and must often be estimated across the orebody using ‘primary variables’ as a proxy. Examples: throughput, ore recovery, grindability and power consumption.

Diagram summarizing primary and responsive geometallurgical variables
Figure 3: Diagram summarizing primary and responsive geometallurgical variables. Redrafted and adapted from Coward et al. 2009.

These variables drive project costs and revenues in a fundamental way and thus geometallurgy has potential to positively impact on the value of strategic and tactical decisions across the life of a mine.

For example, early identification of refractory ore zones can lead to an improved metallurgical recovery performance because it becomes possible to tune the mineral processing circuit according to information from the plant feed beforehand.

Gangue mineralogy can have significant direct impact on mineral processing. For example, talc and/or clay content can cause issues with pulp viscosity, entrainment and bubble ‘clogging’ in flotation. Carbonaceous material can make Au difficult to recover. The presence of deleterious elements (e.g. As, Bi, Cd, F, Hg) can reduce the value of concentrate or make it un-saleable.

Complex intergrowth textures can make it difficult to separate individual sulphide minerals and necessitate expensive fine grinding. The presence of mineralogy with the potential for fast oxidation, or the presence of highly soluble minerals, can require special handling to minimize potential problems (Hunt and Berry, 2017).

The key point here is that the geometallurgy approach allows the operator to spatially model these issues across their orebody and mitigate the operation challenge.

The following diagram illustrates the place of Primary and Responsive variables throughout a geometallurgical study, while putting in context the project pipeline.

Flow diagram of geometallurgical study through the project pipeline. Redrafted and adapted from Lamberg 2011.
Figure 4: Flow diagram of geometallurgical study through the project pipeline. Redrafted and adapted from Lamberg 2011.

A geometallurgical program can be implemented as early as possible in the project pipeline, preferably already in the exploration stage. Characterization of primary variables should be fast, inexpensive and above all practical, meaning that it should routinely provide quantitative data relevant for the responsive variables. The following steps are normally required to identified primary variables:

  1. Collection of geological data through drilling, drill core logging, measurements, rock mechanical analyses, petrophysical parameters and chemical / mineralogical analyses.
  2. An ore sampling program for metallurgical testing where geological data is used in the identification of preferred locations for the samples.
  3. Subsequent laboratory testing of these samples in order to identify responsive variables (sometimes called ore variability testing).

It is important to mention that the weak point of a geometallurgy model is normally in the inadequate data collected from the drill core samples and the generally small number and poor representation of samples collected for ore variability testing. In laboratory tests, quite a small number of samples should represent large tonnages of the orebody (Lund and Lamberg, 2014).


A geometallurgical model combines geological and mineral processing information to create a spatial model that supports the mine and plant design as well as production planning and management. To run and simulate different production scenarios, a geometallurgical model should be developed as early as in the exploration stage and refined and implemented throughout the feasibility and production stages. When implemented early, a geometallurgical model can result in (Potma, 2018):

  • High-value exploration outcomes at deposit-scale
  • Improved confidence in 3D models (e.g. alteration zones; redox zones; etc.)
  • Early flagging of threats and opportunities in a project
  • Project de-risking, minimizing errors & rework
  • Time & money savings throughout the project life cycle

While only a few mines have ongoing geometallurgical programs, this will become more common in the future due to requirements for more effective utilization of ore resources/reserves.

The big question is, can mining companies afford not to perform a geometallurgical assessment? When thinking about the potential negative consequences such as poor recoveries and erroneous mine and plant designs, the answer seems rather obvious.

When should you begin your geometallurgy modelling? As early as in the exploration stage. A thorough understanding of the lithology, chemistry and mineralogy of your project can help identifying issues early, which can lead to reducing costly surprises later in the mine cycle.

To learn more about how your project can benefit from a geometallurgical study, please contact antonio.celis@csaglobal.com or get in touch via LinkedIn.


Coward S., Vann J., Dunham S. and Stewart M., The Primary-Response Framework for Geometallurgical Variables, 7th International Mining Geology Conference, Perth, WA, 17 – 19 August 2009.

Hunt J. and Berry R., Economic Geology Models 3. Geological Contributions to Geometallurgy: A Review. Geoscience Canada, v. 44, pages 103–118, 2017.

Lamberg P., Geometallurgy – what, why and how? Presentation for 8th Fennoscandian Exploration and Mining – FEM 2011, Levi, Finland.

Lund C. and Lamberg P., Geometallurgy – A tool for better resource efficiency, Paper European Geologist 37th May 2014.

Potma W., Managing Risk with Quantitative 3D Mineral System Characterization. GEOmet: A “Cradle to Grave” unifying link across the exploration and mining value chain. Presentation at RIU Explorers Conference 2018

Sola C. & Harbort G., AMEC Mining and Metals, Geometallurgy – Tricks, Traps and Treasures, Conference Paper 2012.

About Antonio Celis

Antonio Celis

Antonio is an economic geologist with 8 years of experience in mineral exploration across the Americas (Northern Chile and Western Canada). His technical background focuses in porphyry Cu-Au targeting using soil/till geochemistry and indicator minerals. He’s well-versed in using a combination of Leapfrog Geo, IoGas, ArcGIS and QGIS software packages. As the former co-founder of Kura Minerals, Antonio was responsible for generating a complete GIS mining library of Chile as well as writing dozens of project factsheets oriented to corporate mining executives.

Contact Antonio Celis on LinkedIn for further information.

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About CSA Global

CSA Global is a mining, geological, technology, and management consulting company providing strategic services for more than 34 years. It has achieved rapid international expansion as result of its technical excellence and high-level services, including, geometallurgical studies, high-level targeting assessments, preliminary economic assessments, pre-feasibility studies, and mine due-diligence. CSA Global advises a wide range of clients internationally; from mining juniors to majors right through to financial and legal groups and currently services clients worldwide from its offices in Australia, Indonesia, Singapore, South Africa, United Arab Emirates, Russia, the United Kingdom, and Canada.

For more information: visit us at csaglobal.com

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