Analytical Testing in Mining and Oil and Gas Applications Using Optimization

Elemental analysis of drilling core sections has an economically important role to play in the developing oil shale and mining industries. Obtaining oil from shale relies on finding optimal drilling sites and analyzing core samples to establish the most productive extraction sites.1

Mining analysts look for rapid, large area, and economical techniques to investigate core sample features, like the mineral composition and grain size, in order to increase their success in finding the best sites. In the conventional analysis, a bulk tool is first used to establish complete elemental composition of a core sample, followed by secondary tools to define specific compound mineral grains and their distribution. An optimized analytical method for improving the effectiveness of shale location testing would be a single technique that analyzes vast areas of a sample rapidly, as well as identifies individual grains.

Comparison with Existing Solutions

  • X-ray Fluorescence (XRF) is a widely preferred tool for determining key elements, such as sulfur (S) or iron (Fe) found in a large area of shale drill core section. Although the collection area using this method includes the bulk shale matrix, only the grains within the matrix are helpful for identifying the characteristics of the optimal shale drilling area. Thus, most of the collection time is wasted by collecting from areas that are not preferred. As the data is obtained from the grains and matrix, element weight ratios of the XRF data can only be employed to infer information on the grain chemistry.
  • Backscatter Electron Imaging (BSE) with a Scanning Electron Microscope (SEM) is employed to view the grains present in the matrix of the core shale sample. This image signal depends on the mean atomic number and with the increase in atomic weight, the signal intensity also increases. For instance, an iron-containing grain will have a stronger BSE signal when compared to a calcium-containing grain with a lower atomic number. Thus, an image of individual grains can be obtained, and size and distribution information is computed by image analysis routines. However, an accurate quantitative or compound evaluation of the grains is not offered by this imaging method.
  • X-Ray Diffraction analysis offers the required compound associations of elements present and thus compound minerals such as Pyrite (FeS2) can be determined to be present in the material. Regrettably,  this is also a bulk method and produces total area fraction of the compound, but not the sizing or distribution information useful for characterizing the environment of the core sample section.2

2048 X 1600 pixel resolution image of the first field of view from a 10 X 10 field run, for a total of 20 K X 1.6 K pixel images in the analysis.

Figure 1. 2048 X 1600 pixel resolution image of the first field of view from a 10 X 10 field run, for a total of 20 K X 1.6 K pixel images in the analysis.

The EDAX Solution — Energy Dispersive Spectroscopy (EDS) Particle Analysis with EDAX Silicon Drift Detectors (SDDs)

Using a BSE signal from the SEM, EDS analysis creates an image underlining the grains of choice from a sample and then gathers data directly from the chosen grains alone. This devoted analysis detects the elements present, measures spectral data, and then employs a feature-specific library to quickly organize and classify grains in shale.

  • EDAX SDD technology produces high-resolution data even at high input count rates for collection times below one second per particle or grain.
  • Data from the shale matrix, which is not required, is not extracted, and therefore large areas can be covered quickly.
  • Spectra are obtained, element peaks are determined, and quantitative weight percentages are used to correspond with the compounds possible.
  • Automated software categorizes data against libraries generated uniquely for the compounds of choice at the fast collection rates.
  • In addition, the software provides information on particle sizing, shape, and area fraction, which demonstrate the distribution of the particle or grains of choice within the matrix shale.
  • As elements differ with drill core depth and location, this information is used by miners to determine the optimal drilling sites.

Analytical Methods and Results

Optical images of a part of an epoxy mounted and polished shale revealed the presence of grains of different colors, sizes, and luster, which denoted metallic variations; however, this could not be verified just by optical analysis alone. An Octane Super SDD was subsequently used to analyze the section in a Water Vapor Permeability (WVP) SEM which produced very fast collection rates with high quality.

Individual examples of a single field of view, 1.35 X 1.055 mm with particles as small as 1.2 X 1.2 um shows the metallic particles colored according to their key (right). Darker particles, which are lower atomic number non-metallics are not of interest for this analysis and remain unclassified (blue).

Figures 2a and 2b. Individual examples of a single field of view, 1.35 X 1.055 mm with particles as small as 1.2 X 1.2 um shows the metallic particles colored according to their key (right). Darker particles, which are lower atomic number non-metallics are not of interest for this analysis and remain unclassified (blue).

Collection Conditions

  • 500 kcps analytical throughput in stored data
  • 1.2 million X-rays per second on the particles
  • Spectral resolution stability even at high count rates enables classification matching
  • Collection speeds of 0.1 second per particle

Example of Copper and Sulfur peaks in spectrum at 1.2 M cps and 0.1 sec collection time. Quant results confirm the particle is Cu2S, or more specifically, chalcocite.

Figure 3a and 3b. Example of Copper and Sulfur peaks in spectrum at 1.2 M cps and 0.1 sec collection time. Quant results confirm the particle is Cu2S, or more specifically, chalcocite.

Drilling or “fracking” employs the oxidization of pyrite in the shale to deteriorate it and drilling becomes considerably easier in areas where there are higher concentrations of pyrite in the shale rock.3 Thus precise search and classification of pyrite is a key aspect of system performance.

  • Figure 4 represents quantitative accuracy confirming compounds such as pyrite (FeS2). Other compounds were also matched as per the class library based on the quant results: chalcocite (Cu2O) and chalcopyrite (CuFeS2).
  • More than 30,000 metallic particles were analyzed in the sample with an area of 142 mm2 in just one overnight run.
    • Particles Counted: 62133
    • Particles Analyzed: 60376
    • Stub % covered: 37.13
    • Area Covered (sq. mm): 142.38
  • Data review enables particles to be organized as per the elemental contribution and association in a ternary diagram.

Quantitative analysis of the particles system showing extreme performance quality at 0.1 second collection time at approximately 1.5 million cps.

Figure 4. Quantitative analysis of the particles system showing extreme performance quality at 0.1 second collection time at approximately 1.5 million cps.

In this ternary view, S (red), Cu (green) and Fe (blue) were selected and each particle was displayed on the diagram according to its contribution from each of the elements, the colors are blended accordingly and the size of the particle is also displayed.  This diagram is interactive, so when clicking on the area between S and Fe, an FeS particle will be selected and quant can be performed, leading to the FeS2 spectrum and quant above.

Figure 5. In this ternary view, S (red), Cu (green) and Fe (blue) were selected and each particle was displayed on the diagram according to its contribution from each of the elements, the colors are blended accordingly and the size of the particle is also displayed.  This diagram is interactive, so when clicking on the area between S and Fe, an FeS particle will be selected and quant can be performed, leading to the FeS2 spectrum and quant above.

Conclusion

The EDAX SDDs enable extremely rapid collection of particulate data, collecting only from the preferred area and excluding collection from the substrate. More than 1 M cps collection rates enable over 10 particles per second to be collected with adequate signal and quality to measure, as well as classify the particles of interest, rapidly offering volumes of data, which can be characterized, thus allowing analysts to find the optimal drilling site location.

References

  1. http://www.netl.doe.gov/file%20library/Research/oil-gas/enhanced%20oil%20recovery/FE0001243_TOR-GreenRiver.pdf  
  2. http://progradingrock.com/x-ray-diffraction/  
  3. http://www.freshpatents.com/-dt20130207ptan20130035264.php
  4. http://geology.about.com/od/more_sedrocks/a/aboutsandstone.htm  
  5. http://www.claysandminerals.com/materials/shales

This information has been sourced, reviewed and adapted from materials provided by EDAX Inc.

For more information on this source, please visit EDAX Inc.

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