Thursday, November 13, 2014

Application of QEMSCAN in the characterisation of ultra trace PGE phases: a case study for Pt and Pd

For the high definition mineralogical study of precious metals, two methods are necessary: one method is to search, identify and quantify the precious metal mineral(s); and the second method is needed for a quantitative analysis of target element for possible refractory appearance of the target element.

Prior to the QEMSCAN and MLA era, for part one several steps would have to be taken: concentration of the heavy mineral portion of the sample, magnetic separation, and finally hand picking of the minerals of interest. Besides the time consuming and inconvenient workflow, the risk of making mistakes at each step is not negligible. Considering the very low abundances of the phases of interest, one small mistake can easily result in wrong technical conclusions!

Thanks to advances in field emission SEMs, nowadays for the first part all we need are representative aliquots in order to prepare the polished sections, the rest depends on the appropriate measurement settings to detect the trace minerals. Personally, I have detected precious metals with ppb levels in one polished section, with fairly good reconciliation with the chemical assays, that is if we are not dealing with the refractory appearance of the precious metals.

The second part - checking the invisible precious metals - is generally performed using either microprobe or laser ablation ICP-MS on handpicked target minerals, a process still necessitating long hours of hand picking under binocular microscope. In addition,we have to deal with loss of the hand-picked grains during the polishing of the section.

This case study illustrates how QEMSCAN an be applied in the detection of Pt and Pd minerals with abundances of 250-500 ppb in ultramafic rocks; and how we could skip hand picking by mapping the section and marking the target minerals for the EPMA lab.

The Project:

Four samples with elevated abundances of Pt and Pd were chosen for modal mineralogy analysis with the main objective of characterising Pt and Pd minerals/carriers. The samples are amphibolite, pyroxenite and dunite. Samples were stage pulverised to 100% passing 200 µm; 3 grams representative aliquots were split from each sample using a rotary micro plot riffler for preparing 30 mm polished sections.

The modal mineralogy analysis was performed using the default settings for BMA analysis (2.5 um pixel size) and with line space of 200 um (see Fig.1).

Fig.1. Modal mineralogy results showing major and minor minerals.
For precious metal search, we used the SMS measurement mode, while optimising the BSE threshold as well as choosing centroid X-Ray measurement for silicates. Spatial resolution was improved by using a 1 um EDS stepping interval.

With the SMS measurement mode, Pt and Pd minerals could be detected in all samples (see Fig.2). Pt is appearing as Sperrylite (PtAs2), while Pd occurs as fine native Pd grains associated with Sperrylite. Both Sperrylite and Pd appear as encapsulated grains in amphibole and quartz (see Fig.3).

Fig.2. Trace elements, the Pt minerals in each sample is less than 0.01%.

So far, we have detected and characterised the main carriers of Pt and Pd. However, the question remains whether Pt and Pd could also be present in other minerals as trace or minor elements. Regarding the mineral assembly of these samples, the only phase which can potentially accommodate Pt and Pd in their structures are Magnetite and Cr-Magnetite. In order to evaluate this, an additional quantitative elemental method like EPMA or laser ablation ICP-MS is necessary. Considering that the latter is a destructive method, we decided to proceed with EPMA.

Fig.3. Micrographs showing Pt appearing as Sperrylite (PtAs2), while Pd forms fine native Pd grains associated with Sperrylite
We selected samples 1 and 4 for additional EPMA analysis, based on the reconciliation between the Pt and Pd values between elemental assay and QEMSCAN data. Sample 1 has a large enough amount of Magnetite that proved easy to locate under the EPMA. However, this was not the case for sample 4, which has only 0.01% of magnetite. As detailed above, the conventional solution would be hand picking of the target mineral from the HMC portion of the sample under binocular microscope. The hand picked minerals were mounted in the epoxy resin, sectioned, polished and carbon coated.

Sufficient magnetite grains could be detected by QEMSCAN in the polished section of sample 4 for EPMA analysis. However, we do not have a correlative workflow solution capable of exporting the QEMSCAN co-ordinates of these grains into the EPMA system. The alternative option is to manually look for target minerals under the EPMA, which is time consuming, especially when looking for trace minerals and therefore generally not an option when dealing with a large volume of samples. Here, we decided to make a map from the section based on the scanned fields and mark areas of interest.

We stitched the fields using the spatial mineralogy images and highlighted some of the larger particles in the centre and corners of the section, as these can be more easily located under the EPMA. Then we marked a path to the target minerals on print out maps. Our colleague at the EPMA lab could easily locate the target minerals (in this case Cr-Magnetites) using these maps which therefore allowed us to remove the problematic hand-picking step from the workflow.

We have successfully applied this method several times to other projects involving EPMA or laser ablation analysis. We are now looking forward to developing a correlative workflow that facilitates the registration of shared coordinates. Based on the EPMA analysis, it turned out that most of the analysed Magnetites and Cr-Magnetites have up to 0.8% PtO (see Table 1).
Table 1. EPMA results from Magnetites and Cr-Magnetites. Please note that the low total of the EPMA results is because of the uncorrected values of FeO for Fe3 and Fe2.    


  1. With careful sampling and sample preparation procedures in place, MLA and QEMSCAN are able to detect and characterise precious metals at trace levels as low as ppb.
  2. MLA and QEMSCAN can assist in eliminating the laborious and error prone process of substituting mineral concentrate and hand picking process. However, at present the correlative workflow still requires a user to export QEMSCAN coordinates manually for subsequent EPMA or laser ablation analysis.
  3. For detailed mineralogical studies, additional analytical techniques are needed to quantify elemental or isotopic compositions. In this study, the EPMA data revealed that Pt and Pd are present in the oxides as well.

Wednesday, February 19, 2014

Elemental quantification of phases from QEMSCAN measurements

With the recent release of iDiscover 5.3.2, a new feature has been introduced that supports the export of multiple measured EDX spectra into a single 'sum spectrum'. The QEMSCAN sum spectrum approach is quite powerful in that high-count EDX spectra can now be created over any sample selection/area of interest, with additional SIP-level control in selecting pixels of interest. This approach could be complementing the existing workflow of driving the SEM to a phase of interest located on the mineral map and collecting an online high-count spectrum on the sample. Below, the workflow of both approaches to quantifying the elemental composition of phases of interest measured in QEMSCAN data is detailed.

Targeted high-count EDX spectrum acquisition

The first approach works by selecting a field or particle of interest either in Particle View (iExplorer → Report → Particle View) or the Debug Measurements window (SIP Editor → Tools → Debug Measurements), and driving the SEM stage to the selected area. This requires the measured sample to be located within the SEM chamber, and iDiscover running on the Support PC.

In order to collect a reference spectrum on the sample, iMeasure is opened → Identify Minerals is selected in the tab, and the number of photon counts is set to something like 1,000.000, while toggling off the continuous count option. The measured spectrum can now be saved as either as an .ems or .msa file. iMeasure will need to be closed in order to open the Bruker Esprit software for elemental quantification (see below).

Combining low-count spectra into a single high-count sum EDX spectrum

The second approach is combining low-count spectra of a single or multiple phases of interest into a single high-count 'sum spectrum'. This approach can be applied to QEMSCAN data offline if the raw X-rays have been saved at the time of the measurement (Datastore Explorer → New Measurement Setup box → Field Settings → toggle on ‘Save Raw Xrays’). Sum spectra are created within the Debug Measurements window by selecting pixels, particles, fields, or samples of interest, and right-clicking → Export Pixel Data.

Step 1: Selecting the area from which particular phases are to be combined into a sum spectrum

Now, a box opens that reflects the individual and combined phases mirroring either the SIP, the Primary Mineral List, or any Secondary Mineral List selected in the above tab in ‘View using:’. In addition to combining measured spectra which have been classified into a phase by a single or multiple SIP entries, all the spectra of the selected area of interest can be combined by using a customized Secondary Mineral List that combines all phases.
Step 2: Select low-count spectra from selected area reflecting selected SIP, Primary Mineral List or Secondary Mineral List

Elemental quantification in Bruker Esprit

Quantification of the measured or exported high-count EDX spectra is performed in the Bruker Esprit software. The spectrum is opened in the Spectrum module. Elements are identified by using the Esprit Quant Tool (Quant, following selection of methodology – e.g. Interactive Oxides → Continue → Display Periodic Table → Select elements → Continue → check primary energy setting (keV) are correct → background correction (e.g. Automatic) → in options for result presentation toggle on ‘Net intensity’ (photon count without the background) → Accept. Optionally, the data can be exported into Excel (Select spectrum → Export results table).

It is advisable, to run a quality check on the number of photos making up the EDX spectrum by creating the sum of all photons in the ‘Net column’. Note that approximately a third of the photons would have been removed as background.

Step 3: Semi-quant sum spectrum. The example is based on multiple SIP definitions including boundary phase definitions for the apatite phase in the drill cutting shown above.

Thursday, November 28, 2013

QEMSCAN mineral identification and quantification – explained

QEMSCAN mineral identification is performed online during sample measurement. If X-ray raw data are saved, mineral identification can be re-run offline using different mineral identification rule sets. QEMSCAN mineral identification is performed in two steps:

1) Elemental identification and quantification by the Spectral Analysis Engine (SAE)
2) Matching of elemental concentration ranges with phase (mineral) definitions in the Species Identification Protocol (SIP)

QEMSCAN mineral quantification is performed offline in iDiscover.

Spectral Analysis Engine
The SAE is fitting up to 72 pure elemental spectra, measured on a given SEM platform-EDS detector configuration, into a measured low-count energy-dispersive X-ray (EDX) spectrum. The SAE so called ‘element concentration’ approach then calculates the best match, by a) recording the presence of elemental spectra, and b) quantifying the relative contribution of each elemental spectra in the measured spectrum. The quality of the spectral match, as well as the measured X-ray count rate and backscatter brightness (BSE), is also recorded. The ‘elemental concentration’, or more specifically the relative contribution of elemental spectra in a measured mineral EDX spectrum, is different to the elemental mass percentage of the mineral. In the dolomite example below, the elemental weight percentages are Ca 21.7, Mg 13.2, C 13.0, and O 52.1, while ‘elemental concentrations’ are given as Ca 12.8, Mg 21.2, C 12.8, and O 23.1.

If all elements are selected for the spectra fitting operation, computing time increases. More importantly, noise is being introduced by an increasing number of partially overlapping elemental spectra. Overlapping element and element substitution rule sets are in place to limit element mismatches. If elements present in a measured spectrum have been disabled, the result would be a poor spectral match. Best results are achieved if the list of enabled elements coincides with those present in the measured sample. For O&G applications, the following 19 elements are commonly selected: C, O, Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Cu, Zr, Ba.

Species Identification Protocol
The elemental ‘concentrations’ reported by the SAE for each individual measurement point (EDX spectrum) are compared online to the Species Identification Protocol, a list of phase definitions commonly referred to as ‘SIP list’. A measured spectrum is being assigned to a single phase, if it matches all criteria of the phase definition. Mineral phase definitions include ‘must have’ and optional ‘may have’ elemental ranges. Elemental ranges reflect the fact that multiple iterations of low-count spectra (typically 1,000 X-rays) of a single high-count spectrum necessarily results in statistical variation. In the example below, simulated low-count ranges of the high-count spectrum fit are provided for the four elements that make up dolomite: Ca 32-53, Mg 14-28, C 7-20, and O 15-32. Elemental ranges can also be used to account for natural chemical variation in a mineral. However, significant chemical variations are best approached by defining multiple end member SIP entries for a given phase. In addition to elemental ranges, elemental ratios and more complex formula can be set as rules in SIP definitions. Furthermore, optional thresholds for BSE brightness, X-ray count rate, and spectral match quality (aka ‘composition confidence’) can be defined.

In contrast to the best-match elemental fitting approach in the SAE, phase identification in the SIP is performed on a first-match basis. Phase definitions are therefore position dependent. The measured elemental concentration, BSE brightness, count rate and spectral match data of a measured spectrum is sequentially compared to all phase definitions, and mapped to the first in the SIP list that provides a match. If a measured data point does not match any predefined entry, it remains unclassified and will be reported as ‘Others’.

Expertise in SIP development is exercised by establishing elemental ranges that reliably capture all the variability inherent in low-count spectra, while preventing phase definitions to become too broad and potentially capturing spectra of non-identical phases. This expertise is often treated as valuable IP by some QEMSCAN service providers. A number of software tools are available in iDiscover, the QEMSCAN expert analysis and reporting software component, to facilitate this task. A layered approach to SIP development has previously been presented (Haberlah et al., 2011), utilising the sequential SIP approach to its full advantage. However, mineral phases characterised by large chemical variability such as some clay minerals, or mixed spectra obtained from excitation volumes that include multiple minerals, traditionally addressed as a ‘boundary phases, can remain a challenge. Boundary phases between up to four minerals can be assigned to neighbouring minerals in a statistically correct and controlled way by applying rules defined in the ‘Boundary Phase Processor’.

Primary Mineral List
Once all phases have been defined on a pixel-by-pixel basis by online SIP classification and offline application of pre-processors such as the ‘Boundary Phase Processor’, individual phases need to be grouped into real minerals or phases of interest in order to be reported as volume or weight percentage contributions. Conversion of measured phase data into chemical assays reports can be of further interest. Both are performed by grouping similar SIP phases in the ‘Primary Mineral List’ and assigning them a single density and chemical composition. In our example, dolomite would be assigned a bulk density of 2.83 g/cc and a chemical composition of weight percentages Ca 21.73, Mg 13.18, C 13.03, and O 52.06. For analysis and reporting purposes, multiple Primary Mineral List entries can further be grouped into Secondary Mineral List entries. For example, an Fe-rich version of dolomite will require a separate SIP entry for identification, and Primary Mineral List entry for adequate chemical and density characterisation. However, both dolomite entries can be grouped in the final modal mineralogy and elemental assay reports without compromising reported accuracy in composition.

The task of assigning relevant compositional data to identified phases requires a good understanding of the chemical variability inherent in some minerals comprising the sample. This task can be facilitated by including bulk compositional data from X-Ray Fluorescence (XRF) analysis. QEMSCAN software tools, such as the ‘Assay Reconciliation Report’, assist with the task of optimising chemical composition and density assumptions. Some applications in O&G are best advised to limit modal mineralogy reports to volume percentages as opposed to weight percentages, if the density values for identified phases are poorly constrained.

Sample presentation
Any discussion of automated mineralogy mineral identification and quantification would be incomplete without highlighting the impact of sample preparation and measurement setup on the results and data interpretation. Sample preparation and selected measurement area and EDX acquisition setup ultimately define reported results and how representative these are of the sample at large.

Haberlah, D., Owen, M., Botha, P.W.S.K., Gottlieb, P., 2011. SEM-EDS based protocol for subsurface drilling mineral identification and petrological classification, in: Broekmans, M.A.T.M. (Ed.), Proceedings of the 10th International Congress for Applied Mineralogy (ICAM), 01 05 August 2011, Trondheim, Norway. Trondheim, Norway, pp. 265–273.

Example illustrating the fitting of elemental spectra (coloured) into a (high-count) measured dolomite spectrum (grey). The nominal elemental weight percentage of dolomite is provided, as well as the ‘concentrations’ or relative contributions of elemental spectra providing the best fit. Finally, simulated ranges for 1,000 X-rays low-count spectra are given, which would be used in the SIP entry to reliably identify the measured spectrum as dolomite.

Monday, October 21, 2013

Trace elements detection limits using QEMSCAN

In geochemistry, a trace element is a chemical element whose concentration is less than 1,000 ppm or 0.1% of a rock's composition. This definition however does not take into account the type of distribution of the element; is the element a minor and/or optional component of complex minerals (REE), and is it likely to occur as discrete mineral phases. Distribution plays a major part in how well automated mineralogy analysis can detect trace elements.

There are two different methods for QEMSCAN to detect trace elements: the first is through phase identification and calculation; the second is through direct identification in the measured energy-dispersive X-ray (EDX) spectra.

1) Trace element detection through phase identification

There are a number of requirements for trace elements to become accurately identified by mineral/phase identification:

  • The phase with the trace element is intercepted in the sample surface and measured by the electron beam. Depending on the homogeneity and concentration of the phase, for this to occur will require a certain statistically defined minimum area of measurements and therefore possibly multiple cuts through the sample.
  • Presence of an adequate SIP entry that correctly identifies the phase. Otherwise, it will remain 'Unclassified' (see Fig. 1).
  • Presence of a phase pure enough not to result in a mixed EDX spectrum. This generally requires phases to be large enough >3-5 cubic um, depending on the accelerating e-beam voltage (15-25 kV).
  • Accurate calculation of the trace element also requires that it is accounted for in the nominal or known composition of the phase, i.e. that it is a priori assigned in the Primary Mineral List and used for the elemental assay report.
2) Direct trace element detection from EDX spectrum

Similarly, for successful trace element identification from the EDX spectrum in QEMSCAN elemental maps, a number of requirements need to be fulfilled:

  • The trace element must be detectable by QEMSCAN, i.e. is one of the 72 elements currently supported by the SAE (see Fig. 2).
  • The element must be activated in the SIP. SIP development using the ‘element concentration method’ introduced in iDiscover 5.1 will generally try to limit the number of elements to those required to identify all typically present phases in a particular applications (e.g. 16 in the current FEI O&G SIP). The reason is that additional non-essential elements increase the background noise and thereby reduce the overall quality of mineral identification.
  • The X-ray count must be adequate to discriminate the elemental energy peaks in the spectra in the elemental spectral match step of the spectral analysis. Automated mineralogy is working with low-count spectra, and depending on the application QEMSCAN is typically set to 1-5,000 X-rays per acquisition points.
  • Adequate energy peak discrimination is further a function of the accelerating e-beam voltage, which, depending on the application, is set to 15-25kV. The higher the voltage, the better the resolution of the energy lines of heavier elements. However, the increase in voltage comes with an increase in the size of the excitation volume which results in more mixed spectra.
  • Finally, the elemental concentration must be within the detection limits. Limited empirical tests suggest that the detection limit for lighter elements such as Mg is within 1.5-3%, REE >20%, and those in-between around 7-8%.

In conclusion, a lot depends on a priori knowledge of the sample, and the quality and relevancy of the SIP being used to measure the sample. The very fact that elemental assays are calculated in iDiscover, using assumptions on the phase composition, and that phases need to be intercepted by the measurement, arguably make X-ray_fluorescence (XRF) the preferred choice for basic reports on the chemical  bulk composition of rock samples. However, XRF cannot provide context, such as elemental deportment and textural phase association. Automated mineralogy has clear advantages over X-ray crystallography (XRD) when it comes to accurately identifying trace elements and phases - as long as they are intercepted.

Figure 1. Trace element Ti here identified in Rutile (TiO2 in red - black circles), but unidentified in Ilmenite (FeTiO3 in black - red circle)
Figure 2.  All 72 elements currently supported by elemental spectra in the new Spectral Analysis Engine (SAE) in iDiscover 5.1 (QEMSCAN)

Thursday, January 5, 2012

Automated Petrography

Petrography of sectioned drill cutting as seen under optical microscope, Scanning Electron Microscope, and QEMSCAN
(the image is not part of the conference paper below) 
While compiling the 2011 publications relevant to Automated Mineralogy and Petrography for the Zotero Automated Mineralogy Library, I came across an abstract presented by Dirk van der Wal and Hans Kruseman at the VIII Congress of CIS Dressers (Ore Mining & Dressing Plants), held in Moscow during February 28 to March 2 at the at Congress Centre of the World Trade Center and organised by the Department of Enrichment of Ores NITU "MISIS", University of Moscow. This abstract presents an excellent introduction into how Automated Petrography fits into the bigger picture of Petrology and Petrograpy and is re-posted here:

Examples of Automated Petrography systems (MLA/QEMSCAN) used in the Minerals Industry

Dirk van der Wal, Hans Kruesemann
FEI Company , PO Box 80066, 5600 KA  Eindhoven, The Netherlands

Petrology is a Geoscience involved with understanding the provenance of rocks, such as depositional environment in the case of hydrocarbon reservoir rocks, or metamorphism in the case of “hard rocks”. Petrography is a branch of Petrology that involves microscopic description of mineral content and texture (microstructure) to aid interpretation of provenance.

The predominant petrographic system is a polarized optical microscope, used to identify minerals from their optical properties in crossed polarized light such as birefringes. Due to the manual and laborious nature of the technique, the Minerals Industry has struggled to deploy petrography in industrial use cases such as Geometallurgy or Mineral Processing.

Scanning Electron Microscopy (SEM) based Automated Petrography systems have been developed since the late 1980’s, predominantly in Australia (CSIRO, Univ. of Queensland), known as QEMSCAN® and Mineral Liberation Analyzer (MLA) respectively. Mineral classification technology based on backscattered electron intensity and elemental composition from energy dispersive spectroscopy has been developed over decades and provides a robust solution of automated fast mineral classification with the superior spatial resolution obtained from electron microscopy.

There are currently more than 150 Automated Petrography systems in use worldwide, predominantly in the precious and base metal mining industries. They are used to measure ore properties such as ore typing, precious metal host mineralogy, mineral associations (e.g. mineral liberation) or phase purity from core samples or grinded material.

Recent developments include advances in sample preparation and speed of analysis, aiming at reducing sample turn-around time to a point where data can be used near real-time to optimize the grinding and flotation circuit.

In this contribution, the Automated Petrography technology will be introduced and use cases illustrated from the precious metal (e.g. Pt, Au, U) and base metal mining industries (e.g. Cu, Fe).

Tuesday, January 3, 2012

Automated petrography applications in Quaternary Science

Cover of Quaternary Australasia Volume 28/2 December 2011 issue featuring QEMSCAN images of 2010 Brisbane Flood sediments 
While there is a strong industry focus on applying automated mineralogy and petrography analysis to petroleum exploration, mining and mineral processing, expert solutions such as QEMSCAN also provide exciting opportunities in other areas of Geoscience such as Quaternary Research. The December 2011 issue of Quaternary Australasia features a review article on "Automated petrography applications in Quaternary Science" by David Haberlah, Craig Strong, Duncan Pirrie, Gavyn K. Rollinson, Paul Gottlieb, Pieter W.S.K. Botha and Alan R. Butcher. The paper includes three compelling case studies on aeolian, fluvial and coastal sediments based on samples from the "red dawn" dust event in eastern Australia on September 23, 2009, the Icelandic Eyjafjallajökull volcanic eruptions in April 2010, the Brisbane floods in January 2011, and medieval mining impact on estuary systems along the coast of Cornwall, UK.

Automated petrography analysis integrates scanning electron microscopy and energy-dispersive x-ray spectroscopy (SEM-EDS) hardware with expert software to generate micron-scale compositional maps of rocks and sediments. While automated petrography solutions such as QEMSCAN® and MLA are widely used in the mining, mineral processing, and petroleum industries to characterise ore deposits and subsurface rock formations, only few Quaternary scientists have applied SEM-EDS compositional mapping to palaeo-environmental research. This paper explains the fundamentals behind the analytical method, describes the type of data that can be generated, and presents the latest advances. Potential applications in Quaternary Science are discussed, including the study of: 1) depositional and formation environments; 2) weathering and diagenetic history; 3) sediment provenancing and pathways; and, 4) the provision of complimentary data in chronostratigraphic studies. Three case studies illustrate potential applications in fluvial, aeolian and coastal research. The first case study applies automated petrography analysis to dust fingerprinting on samples collected from the ‘red dawn’ dust event that swept across eastern Australia on the 23 September 2009, and from the Icelandic Eyjafjallajökull volcanic eruptions that caused enormous disruption to air travel across Europe in April 2010. The second case study investigates flood deposits collected across Brisbane in the aftermath of the January 2011 floods. In the final case study we consider how automated petrography can aid the understanding of human impacts on the environment. Automated SEM-EDS technology was first developed by CSIRO in Australia, and made commercial by companies based in Brisbane. This proximity has proven an advantage to a wide range of researchers in Australasia pioneering innovative applications.


Haberlah, D., C. Strong, D. Pirrie, G. K. Rollinson, P. Gottlieb, P.W.S.K. Botha, and A.R. Butcher. 2011. Automated petrography applications in Quaternary Science.Quaternary Australasia 28 (2): 3-12.

Tuesday, December 13, 2011

Bauxite in Southern Italy by QEMSCAN®

Researchers at the University of Napoli, Italy (Prof. Maria Boni, Giuseppina Balassone, & Nicola Mondillo), teamed up with Dr Gavyn Rollinson at the Camborne School of Mines, University of Exeter, UK, to examine bauxites from Southern Italy using QEMSCAN®. Initial results were presented at the 11th Biennial Society for Geology Applied to Mineral Deposits (SGA) Meeting held in Antofagasta, Chile, September 26-29 2011, followed up by a publication in ‘Periodico di Mineralogia’. The textural (maps) and modal data, combined with the trace mineralogy that QEMSCAN was able to offer, added an extra dimension of evidence to the study that had already used EPMA, XRD, SEM and optical microscopy techniques. Further work may be carried out to explore the issue of bauxites using QEMSCAN.

QEMSCAN® Fieldscan Image (10 mircon x-ray resolution) showing both the ooliths and matrix of a bauxite sample from Southern Italy. Field of view is 8 mm approx. It was possible to detail the variation of mineralogy in the concentric oolith rings as well as subtle differences in matrix mineralogy

Mondillo, N., Balassone, G., Boni, M. & Rollinson, G. 2011. Karst bauxites in the Campania Apennines (southern Italy): a new approach. Periodico di Mineralogia. 80(3).
Balassone, G., Boni, M., Mondillo, N. & Rollinson, G. 2011. Bauxite in Southern Italy: a new approach. SGA, Antofagasta, Chile. September 26th – 29th, 2011.

Environmental Mineralogy: QEMSCAN® image display

The University of Exeter, UK, has announced that it will be producing a large version of the Hayle Estuary contaminated mine waste QEMSCAN® image (produced during research at the Camborne School of Mines), to be displayed in the Library on the main campus from next spring. The image was judged with many other entries and was successfully chosen as a handful of exciting images representing research carried out at the University.

Thursday, November 3, 2011

QEMSCAN WellSite launch

FEI booth at SPE ATCE 2011 launching the ruggedised, mobile QEMSCAN® WellSite™ automated petrography solution.
This has been a very exciting week and a milestone for automated mineralogy and petrography. At the Society of Petroleum Engineers' Annual Technical Conference and Exhibition (SPE ATCE 2011) in Denver, the rugged, mobile QEMSCAN® WellSite™ automated petrography solution has been launched. QEMSCAN WellSite has been developed for operation on oil and gas (O&G) drilling platforms and provides unprecedented analysis of drill cuttings. QEMSCAN WellSite has been successfully field-tested in challenging on- and off-shore drilling rig environments, in close collaboration with leading surface logging service providers and oil companies. The results of these field tests are published in the form of application notes, including this one reporting from the highlands of Papua New Guinea and conducted in collaboration with Halliburton and Oil Search Limited.

QEMSCAN WellSite is an integrated workflow solution, including sample preparation, measurement, and data analysis and export. As a result, near-real time QEMSCAN data is made available onsite, which can be used to support downhole tool data interpretation and time-critical drilling decisions.

The FEI Natural Resources website has been updated with detailed information on the QEMSCAN WellSite productQEMSCAN WellSite technology, QEMSCAN WellSite workflow, QEMSCAN WellSite field trials, and a section on conventional and advanced mud logging. For those less familiar with petroleum exploration and production, a QEMSCAN WellSite product brochure is made available for download. Finally, for those interested in specs I recommend looking at the QEMSCAN WellSite Product Data Sheet.

Friday, October 28, 2011

QEMSCAN® elemental mapping

Colour-coded mineral and elemental QEMSCAN® maps of single dust grain collected during the 'Red Dawn' dust storm.

With iDiscover™ version 5, the new QEMSCAN® Spectral Analysis Engine (SAE) translates low-count energy-dispersive x-ray (EDX) spectra into up to 72 elemental concentrations for each measurement point. The QEMSCAN® Species Identification Protocol (SIP) assigns phase and mineral names based on elemental ranges/ratios, and optional backscatter electron (BSE) brightness thresholds, x-ray count rates, and confidence levels. An exciting new capability has been added to iDiscover version 5.2, which will officially be released in the coming week: elemental mapping.

There are likely to be many applications where elemental mapping will improve mineral identification. Here is one example, which Alan Butcher and I have developed ahead of our INQUA presentation on “SEM-EDS based particle-by-particle characterisation of a large Australian dust storm”. We presented QEMSCAN data from the massive dust storm event that swept across eastern Australia on 23 September 2009, which has been nicknamed the 'red dawn event'. As we were processing the data we asked ourselves the obvious question why don’t we see anything “red”, where are the iron oxides?

The red colour of dust is linked to sub-micron coatings of iron oxides (hematite) on mineral grains. With an excitation volume of 2-5 microns at 20 keV accelerating e-beam voltage, these coatings are too thin to be measured directly. However, they will contribute to mixed spectra. Mineral definitions generally allow for up to 5% of “other elements” to deal with matrix interference effects. Clay mineral definitions often allow for even higher iron concentrations, to account for cation exchanges. As a result, the Fe-oxide coatings did not show up in the standard SIP definitions (figure on the left). However, the Fe-oxide coatings were clearly highlighted in the Fe elemental map (centre figure). This prompted us to duplicate the quartz definition, exclude iron in the standard definition, and add an iron-rich quartz definition below. The result is shown in the figure to the right.

This simple example demonstrates three things: 1) the ability of the new QEMSCAN SAE to decompose low-count EDX spectra into elemental concentrations; 2) the ability of elemental maps to highlight the mineral context in which elements of interest occur, even in mixed spectra; 3) the beauty of position dependency of the SIP, with the first-match approach allowing to create “elemental discriminator” phase definitions.

Friday, September 16, 2011

QEMSCAN® clay mineral identification

QEMSCAN®  compositional maps of selected Source and Special Clays and other reference material providing examples for the kaolin, smectite, illite and chlorite mineral groups.

Clay minerals are the product of chemical weathering, diagenesis and hydrothermal alteration of rocks. They are ubiquitous on earth and comprise a wide range of very fine-grained, layered, and often plastic aluminium silicates. The primary residual alteration products are easily eroded and moved by wind and water. As a result, extensive sedimentary accumulations of clays form in low-energy depositional environments such as lake beds and on the ocean floor. These deposits undergo diagenesis and the resulting materials are referred to as mudstone and shale.

Clays are among the most important minerals used in numerous applications by manufacturing and environmental industries. Some of their unique physical and chemical properties include the high surface-area-to-volume ratio, and high cation-exchange and swelling capacities. These properties are expressed in the characteristically high plasticity and adsorption qualities of some clay minerals.

Clay minerals have important applications and implications in the natural-resource industries, particularly in petroleum exploration and production, and in mining and mineral processing.

Clay minerals occur in all rock formations of siliciclastic petroleum systems, including source, reservoir and seal rocks. While playing a fundamental role in acting as impermeable barriers "trapping" the buoyant hydrocarbons in subsurface reservoirs, clay minerals can also pose significant challenges to exploration efforts and reservoir management.

The presence of clays in ore is a significant mining challenge. Ore bodies are typically marked by a close spatial relationship between fresh and weathered clay-rich zones, with different processing requirements. Small particle sizes and large surface areas result in high chemical reactivity that makes clays very responsive to changes in the mineral processing environment. As a result, mining, throughput, and recovery rates, can be significantly impacted by clays and require changes in the design of the process circuits.

At FEI Natural Resources, we have developed a clay mineral identification protocol using the new QEMSCAN® Spectral Analysis Engine at 20keV to discriminate important clay minerals in natural-resource applications. The protocol has been successfully applied to reference material from the Clay Minerals Society including the Source and Special Clays shown in the figure above. The work has been presented at EUROCLAY in an oral presentation earlier this year.

Saturday, September 10, 2011

Environmental mineralogy by QEMSCAN®

Resin-impregnated plug recovered from a core taken from the Hayle Estuary, Cornwall, UK, showing the impact of historical mining. The image shows pre-mining sediments in purple (mostly marine-derived carbonate sands) overlain by laminated muds (brown/red-purple) containing heavy minerals such as cassiterite, pyrite, chalcopyrite, sphalerite and galena.
The image is 27 mm diameter scanned at 10 microns X-ray resolution.

The importance of understanding mineralogy related to contaminated soils and sediments has been highlighted for a number of years by researchers  at the Camborne School of Mines, University of Exeter, UK. Duncan Pirrie, Gavyn Rollinson and Matthew Power have examined samples taken from both estuaries and contaminated land (e.g. Pirrie et al. 2009*).

During these studies automated mineralogy (QEMSCAN®) has successfully been used as a tool to help characterise mineralogy, locate trace phases and determine diagenetic alteration that may lead to bio-availability of heavy metals. In combination with bulk chemistry and mineralogy techniques such as x-ray fluorescence (XRF) and X-ray diffraction (XRD), our understanding of environmental mineralogy can be improved and environmental processes be better understood and managed.

* Pirrie, D., Rollinson, G.K., Power, M.R. 2009. Role of automated mineralogy in the assessment of contaminated land. Geoscience in SW England, 12. 162-170.

Friday, September 9, 2011

Mapping the world ... one micron at a time

Watch out for the August issue of Elements, the International Magazine of Mineralogy, Geochemistry, and Petrology. On page 228 you will find the first in a series of thematic advertisements of FEI's SEM-based Petrographic Analyzers. Elements publishes peer-reviewed papers which are part of a theme based collection. This issue is on "When the Continental Crust Melts", and FEI Natural Resources contributed with an amazing QEMSCAN® image of a garnet schist from Brittany, France.

The compositional map of the schist shows spectacular regional metamorphic textures. Schistocity is the term referring to a mode of foliation typical for medium-grade metamorphic rocks, where platy minerals such as micas and clays (in shades of green) are parallel aligned. Quartz (pink) occurs in form of elongated, drawn-out grains. The garnet group crystals (purple) are hexagonal in cross-section and really stand out in this piece of art. Garnets are important minerals in establishing the temperature-time histories of metamorphic rocks.

We would like to take this opportunity and thank Michael Garrick for providing the sample, a beach pebble he picked up as an undergraduate geology student. We used this sample to develop QEMSCAN® mineral identification protocols based on the latest generation of the Spectral Analysis Engine in iDiscover version 5.x.

Thursday, August 11, 2011

Ash from Eyjafjallajokull, S. Iceland 2010

Ash particles from Eyjafjallajökull 2010, analysed by QEMSCAN®
Researchers at the Camborne School of Mines (CSM), University of Exeter, UK, have examined ash samples taken on Iceland during the volcanic eruptions in 2010. Work was undertaken by Dr Gavyn Rollinson and Dr Duncan Pirrie, with samples taken by Dr Stuart Bearhop who was on fieldwork on Iceland at the time of the eruptions.

The ash sample mineralogy reflects the geology of Iceland being dominated by plagioclase feldspar (cyan) and other silicates (olivine, pyroxenes/amphiboles, clays) with glass phases, minor ilmenite and Ti-magnetite. Grain sizes are <30 microns, and generally <15 microns. Initial work was presented by Dr Alan Butcher of FEI at the INQUA XVIII conference in Bern, Switzerland, Saturday 23rd July 2011.

The study demonstrates the value of automated mineralogy to airborne pollution and builds on previous and current work being carried out on air particulates at the Camborne School of Mines.

Saturday, July 30, 2011

QEMSCAN studies of metamorphic rock samples

Identification of key minerals is of great importance to determining the tectonic history of metamorphic samples. These key minerals may be few in number and present only as small micro-inclusions making them difficult to identify, if at all, with a petrographic microscope.

Once relict minerals of earlier metamorphic assemblages are located, thermobarometry and geochronology can then be applied, resulting in a wealth of information on previous segments of the pressure-temperature-time-deformation path. The relict mineral textures and their relationship to the fabric of the entire thin section can be easily seen in mineral maps yielding important textural information.

The QEMSCAN at the Camborne School of Mines (CSM) is being used to study metamorphic samples from the Central Metamorphic terrane of the Eastern Klamath Mountains, Northern California.

Intial results were presented at the European Geosciences Union, General Assembly, Vienna, 2-7 May 2010 "Application of Automated SEM-EDS Based Mineral Identification Systems to Problems in Metamorphic Petrology" by Robert Fairhurst, Wendy Barrow and Gavyn Rollinson.

Friday, July 29, 2011

QEMSCAN Filament Life

Currently the tungsten filament in our QEMSCAN system Camborne School of Mines, University of Exeter, UK has been running for 6376 hours (since 15th December 2011) which is well over 8 months. Is this a record for tungsten filaments? Comments welcome on life spans of your filaments!

QEMSCAN: Beyond Minerals

Automated Mineralogy can characterise more than just minerals. At the Camborne School of Mines (CSM), University of Exeter, UK, we have been experimenting with a wide range of sample types for over 7 years. To the left is an example of a historical smelter slag (from historic mining activity pre-1900's) taken from Calenick Creek, near Truro, Cornwall, UK.

The image shows mainly Fe-As (blue) and Sn metal (red) phases, and identified various other phases. Sample size is approx. 4 x 2 cm, fieldscan mode at 10 microns resolution with about 2 million analysis points.

TOP 10 AM papers: Riley et al. 1989, Hydrobiologia 176/177, 509-524

QEMSCAN mineral map of fluvial sediments collected from the 2010 Brisbane flood
Review of Riley et al. 1989, Hydrobiologia 176/177, 509-524

In terms of early adopters of automated mineralogy technology beyond mineral processing, due credit must be given to Riley, Creelman, Warner, Greenwood-Smith and Jackson from Australia. In their groundbreaking journal paper on "The potential in fluvial geomorphology of a new mineral identification technology (QEM*SEM)" they pioneer the application of automated SEM-EDS compositional mapping to the study of depositional environments and sediment sources. I only became aware of this paper after publishing a similar study on late Pleistocene flood deposits in the Flinders Ranges, South Australia as part of my PhD thesis, and would like to take this opportunity to give due credit to a study well ahead of it's time.

Riley et al. set out explaining why mineral analysis remains an underutilized diagnostic tool in fluvial geomorphology and sedimentology. Optical microscopy on fine fluvial sediments is cumbersome, statistically questionable and of limited value when it comes to discriminating source areas based on clay mineralogy.

The objective the authors face is to establish the maximum flood level the Nepean/Warragamba Rivers in New South Wales over the recent geological past. The deeply entrenched river terraces in the lower reaches are inappropriate indicators because they represent a different hydrological regime. In the absence of slack water deposits, veneers of alluvium mixed with colluvium by bioturbation remain the only record.

The task was to perform micron-scale textural analysis and discriminate non-fluvial from fluvial material as well as to quantify the sediment contributions from different tributaries in the catchment. Challenges in fingerprinting and provenancing fluvial sediments by mineralogical analysis include sorting and differential comminution during transport, post-depositional weathering, contamination by reworking and aeolian deposition, and mixing of sediments from different source rocks. All this requires a focus on minerals resistant to weathering, and sophisticated statistical analysis of the data.

The methodology section is exceptional. The paper provides detailed information on the QEM*SEM system configuration including a schematic diagram and a technical discussion of the data acquisition and processing in the appendix. The authors developed their own application-specific mineral identification protocol (SIP) and primary mineral lists, differentiating readily identifiable mineral species from broadly related silicate groups. This was at a time when no interactive Measurement Debug module for SIP development was available as in later versions of the iDiscover software package. Unfortunately, it was also before the powerful categorizer tools were developed that provide classification of particles by mineral association, size and shape. As a result, the authors had to make do with modal mineralogy data for 4 physical size fractions. Riley et al. clearly set an example by applying principle component analysis to produce and assess independent variables. The spatial relationship between minerals from different sampling locations was investigated by multiple cluster analysis.

The results are convincing and clearly differentiating fluvial from non-fluvial deposits by QEM*SEM mineralogical data. The conclusion was that the estimate of Probable Maximum Flood discharge for the downstream dam had to be revised. The conclusion after reading this paper is that sedimentologist can clearly benefit from revisiting this pioneering paper before applying the latest image analysis capabilities that compositional mapping solutions such as QEMSCAN provide.

Wednesday, July 27, 2011

TOP 10 AM papers: Grant et al. 1976, Scanning Electron Microscopy/1976 (III)

1982 prototype of QEM*SEM with mini-computer to the right at CSIRO Melbourne

Review of Grant et al. 1976, Scanning Electron Microscopy/1976 (III)

In the 70's, it was not uncommon to publish outstanding geoscientific research in workshop proceedings. This groundbreaking paper on "Multicompositional particle characterization using the SEM-microprobe" by Grant, Hall, Alan Reid and Martin Zuiderwyk is published in the Proceedings of the Workshop on Techniques for Particulate Matter Studies in SEM held at the IIT Research Institute in 1976. Over three decades ago, the authors - listed in alphabetical order - demonstrated the first computer-controlled automated mineralogy system and outlined a number of principle functions in mapping particles which changed little over the years despite the revolution in computational power and software languages since.

Grant et al.'s vision, as outlined in the introduction, is to determine sizes and composition of complex particles, and to "measure rather than infer" areas and perimeters to derive shape functions to better understand physical and chemical behaviors of particles in industrial and mineral treatment processes, i.e. mineral flotation and the degree of liberation during grinding processes.

The original instrument design consists of a mini-computer controlled e-beam which is automatically moved across the sample along a user-defined pattern. An initial "fast scan" locates the particles for detailed scanning. The dwell time for x-ray acquisition at each point can be defined. In addition, an "event acceptance filter" is in place to only record changes in material composition. Composition and e-beam coordinates are saved in form of digital maps composed of line segments and points.

The original system is setup to accept secondary and backscattered electron signals, absorbed specimen current, as well as energy-dispersive x-ray counts from either EDS detectors or microprobe. The measurement mode outlined is a proto-type for what is to become the Particle Mineral Analysis (PMA) in the QEM*SEM and later QEMSCAN solution. It uses either rapid BSE or SE signals to locate the edges of particles which are in turned scanned in detail using x-ray signals. The software includes algorithms linking particles extending across multiple frames, similar to the "field stitch" pre-processor in iDiscover. In addition, mixed signals between particle and mounting medium, referred to as "boundaries" are resolved, a first step in the development of Species Identification Program (SIP) boundary phase definitions and the award-winning "Boundary Phase" pre-processor by Paul Gottlieb in the iDiscover software package. Touching particles are discussed beyond shape parameters using secondary electron images to discriminate same phases in the discussion with reviewers at the end of the paper.

The boundary coordinates are saved allowing for a visual display of the data and basic image analysis functions, including the particle-by-particle calculation of area, perimeter, centroid and even the option to report phase contributions to the perimeter. Particular consideration is given the stereological challenges of reporting unbiased particle size, shape and composition from 2-D data. Interestingly, the authors point out to future investigations in mounting particles onto surfaces and using a second set of detectors providing biaxial views to better estimate particle sizes in three dimensions.

It can be fairly said that the authors laid the foundation for automated mineralogy and future software developments. It is a testimony to their visional capacity that they discus ways to move forward in 3-D particle analysis and even applications of their algorithms beyond rock particles, such as the analysis of pore space or images of macro-scale objects.

Thursday, July 14, 2011

Automated Mineralogy publication trends

Peer-reviewed journal and conference papers on automated mineralogy applications as of mid-2011

There are now >100 papers on automated mineralogy which I have compiled using the open source reference management software Zotero. The library includes book chapters, journal papers, peer-reviewed conference papers, public reports, research thesis, as well as important press releases on automated SEM-based solutions for compositional mapping of minerals and rocks. The bibliographic information is openly accessible via the Internet as a group library with closed membership.

In the process of compiling the literature and organizing the papers by publication type, field of application, as well as analytical technology solution, some interesting trends emerged. The most significant one is a marked increase in automated mineralogy publications over the past couple of years. This year alone, papers published in 2011 or in press account for nearly one third of all automated mineralogy publications.
The two industry-standard automated SEM-EDS solutions QEMSCAN® (formerly QEM*SEM) and the Mineral Liberation Analyzer (MLA) make up the large majority (>90%) of the published literature. This literature is dominated by papers applying QEMSCAN® in areas ranging from oil and gas, coal and flyash, ore characterization, mineral processing optimisation, environmental mineralogy, archaeology and forensics. Papers based on the MLA solution are more focused on the optimization of mineral processing and bright phase mineral detection.

Considering the dominant position of QEMSCAN® in the scientific literature (~80%), it is an interesting exercise to compare the historical stages of QEMSCAN® development with the scientific output. As QEM*SEM (Quantitative Evaluation of Minerals by Scanning Electron Microscopy) was first developed at CSIRO Australia, a limited number of authors lead by the developers such as Alan Reid, Paul Gottlieb, David Sutherland and Alan Butcher laid out the methodological groundwork for early applications. In 2003, when QEM*SEM was turned into a commercial solution by Intellection, publication output picked up by early adopters such as Norman Lotter and Duncan Pirrie, but the overall volume remained low. Scientific writing gathers momentum in 2006, coinciding with the first international meeting to focus on automated mineralogy technologies held in Brisbane: the Automated Mineralogy ’06 conference which was co-organized by MEI and Intellection. The most significant increase in scientific productivity aligns with the acquisition of QEMSCAN and MLA technologies by FEI, a leading developer and provider of scanning electron microscope and focused ion beam solutions.

Friday, May 27, 2011

TOP 10 AM papers: Jackson et al. 1984, Proc. Australas. Inst. Min. Metall. 289, 93-97

QEMSCAN® Sample Blocks
Review of Jackson et al. 1984, Proc. Australas. Inst. Min. Metall. 289, 93-97

The quality of information provided by automated mineralogy solutions, as with any other analytical technique, depends first of all on the quality of the measured sample. It depends on proper sample collection, subsampling procedures, sample preparation and presentation. It is therefore most appropriate to start this review of the Top 10 Automated Mineralogy papers with this classic, although difficult to access, technical note by Jackson, Reid and Wittenberg on "Rapid production of high quality polished sections for automated image analysis of minerals". The 1984 seminal paper on sample preparation describes in great detail a method developed to mount particles for automated mineralogy analysis which remains, with minor adjustments, the standard protocol applied in SEM-EDS laboratories across the world to this day.

The paper starts with laying out the requirements for the production of a representative section mount, so that the mineralogy in the cross-section is representative of the overall parent sample mineralogy. The fundamental requirements are to provide a random, even 3-D distribution of particles, without segregation of particles by mass, density or grain size, or introducing preferential orientation. In addition, a high degree of surface integrity in the sectioned plain needs to be achieved in order to minimise bias introduced by pitting, plucking, grain shattering, or the preferential removal of less competent minerals.

Jackson et al. describe seven steps of sample preparation, including screen sieving, rotary riffling, mechanical dilution, epoxy mounting, grinding, polishing and conductive coating.

1) Screen sieving and/or cyclosizing in an ultrasonic ethanol bath to remove loose aggregation, oversized foreign matter, and provide optimal narrow size ranges for measurement.
2) Random subsampling of the parent material to a few grams is achieved by a rotary riffler.
3) Mechanical dilution of the particle samples is discussed to prevent segregation, minimizes particle-to-particle contact, and to assists random particle orientation. Jackson et al. suggest mixing the sample with crushed graphite of similar size range and surface angularity as an inert filler.
4) Even and random 3-D distribution is achieved by mechanical shaking the mixture in cylindrical plastic moulds. Subsequently, the dry mixture is cast by covering it in resin and hardener and stirring the sample. The epoxy-sample slurry can be evacuated to remove air bubbles.
5) Grinding the hardened block consists of two stages; the first to cut away surface epoxy and particle layers of preferred orientation well into the mixture of particles and filler, the second to remove damaged damage areas.
6) Final polishing with a sequence of diamond paste cloth laps is applied to improve the surface finish. Between grinding and polishing stages, the sample block is cleaned in an ultrasonic bath using a detergent solution or alcohol.
7) Finally, the sectioned sample surface is sputter-coated with a 20-30 nm carbon film making it electro-conductive.

The fundamental sample preparation protocol for automated SEM-EDS analysis laid out by Jackson et al. remains largely unchanged over the past 25 years and is widely applied by the leading service providers in the mining industry.

Friday, March 25, 2011

ICAM 2011 presentation on rock typing of PDC drill cuttings

FEI Australia Center of Excellence for Natural Resources in collaboration with CO2CRC is going to present a third talk at the 10th International Congress for Applied Mineralogy (ICAM 2011) in Trondheim, 1-5 August 2011. Here is a preview of our presentation on a novel lithotyping approach performed on Polycrystalline Diamond Compact (PDC) drill cuttings. Micron-scale compositional mapping, using FEI's QEMSCAN® automated mineralogy solution, and cutting-by-cutting classification by the iDiscover™ software package are demonstrated to provide detailed reservoir rock properties beyond chemical assays and modal mineralogy reports.

Petrological reconstruction of the subsurface based on PDC drill cuttings: an advanced rock typing approach"
by David Haberlah, Pieter W.S.K. Botha, Nicole Dobrzinski, Alan R. Butcher and John G. Kaldi

Polycrystalline Diamond Compact (PDC) drill bits are increasingly used in conjunction with motors and turbines as a fast and cost-effective way to drill wells. However, produced rock cuttings, in particular from clastic reservoir rocks, are often considered too fine and problematic for conventional petrological analysis. Recent advances in automated scanning electron microscopy and energy-dispersive x-ray spectroscopy (SEM-EDS) have transformed the petrological analysis of drill cuttings by replacing conventional qualitative descriptions of handpicked samples, with ultra-fast, quantitative and repeatable petrological analysis. Further developments include moving from chemical and mineralogical whole-rock analysis towards textural characterisation performed on a cutting-by-cutting and mineral grain-by-grain basis. Automated SEM-EDS compositional mapping allows for the definition of rule sets that classify individual cuttings into categories based on parameters such as mineral associations, grain sizes and shapes. As a result, drill cuttings can be classified into lithotypes representing subsurface rock types.

This study demonstrates that accurate and detailed reservoir characterisation can be based on SEM-EDS compositional maps of PDC drill cuttings. Lithofacies associations are reported from rock cuttings at continuous 5 m depth intervals from the CO2CRC’s CRC-1 well for a continuous stratigraphic interval in the Late Cretaceous Skull Creek Fm and Lower Paaratte Fm of the Otway Basin, Victoria, Australia. The CO2CRC Otway Project is the world’s largest research and geological storage demonstration project of the deep geological storage of carbon dioxide (CO2). For each cutting interval, a chemical assay, modal mineralogy report, and corresponding compositional maps were reported. A key advantage of automated SEM-EDS solutions such as QEMSCAN® is that particles can be categorised on the basis of mineralogy, grain size and texture. Here, the drill cuttings were first classified into three general lithotypes, corresponding with sandstone, shale and cemented clasts, and subsequently divided into more specific lithotypes. These provide detailed information on the depositional environment and diagenetic history of the rock formations, and highlight intervals of cementation and intra-formational seals within the reservoir. The lithotyping results were plotted against wireline log data and show a strong correlation with the gamma-ray log.

This study demonstrates that combining automated SEM-EDS measurements of PDC cuttings with advanced digital image analysis and processing, can significantly contribute to the petrological reconstruction of the subsurface. This reinforces drill cuttings are a valuable source of geological and engineering information, potentially reducing the requirements for routine coring and wireline logging.