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Abstract

Scanning electron microscopy (SEM) has become a common way to estimate porosity and organic matter (OM) content within shale resource rocks. Since quantitative SEM analysis has emerged as a means for assessing the porosity of shale, a common goal has been to image polished samples at the highest possible resolutions. Because nanopores are visible at pixel resolutions ranging from 5 to 10 nm, it is natural to consider the possibility of a pore regime below 5 nm that could contribute a significant amount to the total porosity of the system. When considering that a molecule of methane gas is on the order of 0.4 nm diameter, pores smaller than 5 nm could contribute significant storage volume and transport pathways in a reservoir. These nanopores may be a significant source of porosity within certain OM bodies, where total detectable pores using SEM (i.e., ~10 nm pore body diameter and up) have been observed to be volumetrically equivalent to the OM body volumes themselves. With the potential to examine the population of pores below ~10 nm in diameter using the helium ion microscope (HIM), it is possible to construct a rock model that is more representative of the varied pore size regimes present. The primary goal of this study was to quantify the amount of organic-associated pores below the resolution of conventional field emission scanning electron microscope (FESEM). In this study, 51 individual imaging locations from 12 organic shale samples were selected for systematic imaging using a HIM. These samples and locations were selected because of the presence of porous OM identified from previously completed SEM imaging. After methodical HIM imaging and digital segmentation, it was concluded that most samples had no significant incremental, resolvable, organic pore fraction below the detection threshold of conventional FESEM imaging. The advanced resolution of the helium ion beam provides sharper definition of pore boundaries, but the total porosity fraction of these <10 nm diameter pores within the OM in most samples was negligible. We also notice that FESEM and HIM can be considered complementary techniques, as each provides beneficial information that cannot be obtained from using only one method.

Introduction—Overview of Helium Ion Imaging

The benefit of the helium ion microscope (HIM), compared to a conventional field emission scanning electron microscope (FESEM), is the reduced spot size of the ion beam and consequent reduction of areal extent for produced secondary electrons, producing a pixel size as small as 0.35 nm (van der Drift and Maas, 2011). As with conventional FESEM, maximum resolution is dependent on the material being analyzed and details of beam energy, spot size, and field of view. In this investigation of shale in a HIM system, a pixel size of approximately 0.4 nm resulted in resolution of features as small as 4-5 nm. Much previous work has shown bulk measurement evidence for shale pores in the range below 5 nm. Curtis et al. (2010) used mercury injection capillary pressure (MICP) results to indicate shale pore throat radii at 1.8 nm or lower; Loucks et al. (2009) also use capillary pressure measurements as evidence of pore throats as small as 5 nm. These nanometer-scale pores can be an important component of storage volume and flow units in many hydrocarbon reservoir formations (Chalmers et al., 2012). Therefore, accurate quantification of these pores, which are not readily resolved using FESEM imaging, could be significant to computations of total porosity using image analysis methods.

Prior studies have also indicated systemic evidence of lower computed porosity from SEM image analysis compared to bulk laboratory methods. Loucks et al. (2009) suggest a host of reasons that SEM image analysis-based calculations of porosity in Barnett shale samples are significantly lower than helium porosim-etry values for the same depths. Rine et al. (2010) also indicate large differences between porosity values derived from FESEM images and crushed helium porosity measurements.

One explanation for such discrepancies between SEM-computed porosity and bulk measurements of porosity could be the inability of even the best FESEM systems to consistently resolve organic nanopores below 10 nm in diameter. By this theory, the enhanced resolution capability of the HIM system could allow detection of nanopores that are too small in diameter to be quantified using FESEM images.

Previous work by Yang et al. (2013) indicates that ion-polished shale samples imaged using HIM showed numerous pores between 5 and 15 nm diameter accounting for up to 10% of the imaged area, while noting discrepancies between SEM image analysis-based values of porosity and pore throat size when compared to bulk laboratory measurements. Kliewer et al. (2012) describe using HIM to detect pores as small as 2 nm in diameter on non-ion-polished shale surfaces and suggest that these values are consistent with MICP results of pore throat size. Walters and Kliewer (2014) also suggest that the increased depth of field in HIM images compared to FESEM on ion-polished samples can remove some of the need to acquire three-dimensional (3-D) SEM data sets using FIB-SEM to study the pore network geometries. King et al. (2015) suggest that HIM images support the 2 nm pore throat size as measured by MICP and small-angle neutron scattering techniques. Curtis et al. (2014) indicate that the very small pore fraction does not contribute a significant amount of porosity.

This chapter builds on these previous studies by directly comparing previously imaged surfaces of ion-polished shale to determine the abilities of HIM to augment conventional FESEM imaging and quantification of organic nanopores. If the porosity in the sub-10 nm range detected by HIM imaging is additional to porosity detected by FESEM, it may explain lower porosity values from image analysis when compared to bulk rock measurement methods.

This study also seeks to examine the nature of the organic matter (OM) itself and whether the secondary electron response using the helium ion beam can provide information regarding the division between kerogen and bitumen. Loucks and Reed (2014, 2015) note that determining OM type from FESEM is virtually impossible using image analysis. Since both kerogen and bitumen have similar density and atomic number properties, they appear indistinguishable in grayscale from FESEM secondary electron and backscattered electron images. Advanced imaging and spectroscopy techniques, such as scanning transmission x-ray microscopy (STXM) and x-ray absorption near edge structure (XANES), have been used to combine spatial information and chemical information about kerogen and bitumen (Bernard et al., 2012). The nature of HIM contrast formation may provide some supplemental information regarding OM type.

Sampling and Methods

For the bounds of this study, 12 samples were selected based on the presence of porous OM that was previously observed in SEM imaging. This study focused specifically on samples that contained significant amounts of porous OM and was designed to explore the advantages of helium ion imaging of sub-10 nm pores contained within OM bodies. The 12 samples originated from 7 wells representing 4 formations (Table 1; Figure 1).

Detailed locations of wells and samples used in this study, and the number of helium ion microscope images acquired and processed from each well.

Table 1.
Detailed locations of wells and samples used in this study, and the number of helium ion microscope images acquired and processed from each well.
WellFormationCounty, StateNumber of SamplesNumber of Images
Well XMarcellusHarrison, WV416
Well YMarcellusTaylor, WV12
Well SWolfcampReagan, TX27
Well REagle FordBee, TX217
Well ZEagle FordZavala, TX14
Well QEagle FordLaSalle, TX13
Well TBossierHarrison, TX12
WellFormationCounty, StateNumber of SamplesNumber of Images
Well XMarcellusHarrison, WV416
Well YMarcellusTaylor, WV12
Well SWolfcampReagan, TX27
Well REagle FordBee, TX217
Well ZEagle FordZavala, TX14
Well QEagle FordLaSalle, TX13
Well TBossierHarrison, TX12
Figure 1.

Location of wells and formations where samples for this study originated.

Figure 1.

Location of wells and formations where samples for this study originated.

Prior to SEM imaging, the samples had been ion polished using a Gatan Ilion + Argon ion polishing system. The previously completed set of FESEM images were acquired with a pixel resolution of 10 nm at a beam energy of about 1 keV or less. Although FESEM images can have pixel sizes less than 2 nm, 10 nm pixel size was used as the base case for comparison because it provides a balance between resolution and field of view for many organic shales. Samples were imaged in the helium ion system using varying parameters to optimize image quality. Each HIM image was acquired as a subset of an extant FESEM image, at higher resolution, to allow for a direct comparison of gray scale, resolution, and porosity fraction of various materials (Figures 12, 13, 14, 15). The accelerating voltage of the HIM is fixed at 30 keV. This is much higher than the energies used in the FESEM in this study; however, because of the difference in mass between electrons and helium ions, a helium ion at 30 keV energy has approximately the velocity of an electron at 3 keV (Joy, 2012). The smallest pixel size of the HIM images was 0.5 nm.

It is worth emphasizing that the reported porosity and OM shown in Figure 2 was computed for small fields of view that contained mostly organic material. The average OM value from the FESEM fields of view chosen for HIM imaging is 66.8%; this is atypical and reflects the bias involved in imaging organic-rich regions of these samples. It was determined incidentally that the use of helium ion imaging for the imaging of intergranular pores may in fact be of limited use compared to conventional FESEM, as the contrast between pore and mineral grain is less pronounced in the helium ion images. Because of these considerations, it should be understood that the porosity and OM values are not typical of the bulk porosity and OM content of each sample.

Both HIM and FESEM image sets were processed in order to establish the area percentage of pore body and OM so that the effect of better resolution of pore detection could be determined. Segmentation of mineral and OM from HIM images was difficult because of unusual contrast effects from helium ion-specimen interaction. In typical FESEM analysis, both secondary electron and backscattered electron signals are influenced, to differing degree, by the atomic number of the various materials present, though the influence of Z on secondary electron generation is comparably very small (Goldstein et al., 2003). These produce a consistent relative grayscale signal, with those materials that have higher atomic number having a higher grayscale value. In a shale sample, mineral crystals are lighter in appearance than OM because of their higher effective atomic number. Effective atomic number (Zeff) for typical shale-forming minerals ranges from approximately 11.8 for quartz to approximately 22 for pyrite (Edmundson and Raymer, 1979). By contrast, the Zeff value for coal is only about 6, whereas kerogen is lower still.

Figure 2.

Example of segmentation of porosity and OM from a helium ion image. Left: Cropped FESEM image of Bossier shale sample; pixel size is 10 nm. Image acquired at 0.9 keV accelerating voltage. Center: Same region imaged using HIM; pixel size is 5.9 nm. Image acquired at 30 keV accelerating voltage. Right: HIM image with overlay of digitally segmented OM (green) and porosity (blue).

Figure 2.

Example of segmentation of porosity and OM from a helium ion image. Left: Cropped FESEM image of Bossier shale sample; pixel size is 10 nm. Image acquired at 0.9 keV accelerating voltage. Center: Same region imaged using HIM; pixel size is 5.9 nm. Image acquired at 30 keV accelerating voltage. Right: HIM image with overlay of digitally segmented OM (green) and porosity (blue).

Upon first viewing, images from HIM can be difficult to interpret, particularly for a viewer who is accustomed to conventional scanning electron microscope (SEM) imagery. Scanning electron micrographs have a well-understood system of contrast with increasing grayscale signal essentially related to the atomic number of the material or compound being analyzed. With a helium ion imaging system, the method of creating grayscale contrast is not based on atomic number, so that the relationship between grayscale values of two materials may be completely inverted in comparison to an SEM image (Bell, 2009; Notte and Goetze, 2014). While the helium ion images result from secondary electrons recorded by an Everhart-Thornley type detector, differences from FESEM may arise from the effects of the positively charged ion beam creating a different charging environment at the sample surface. In investigations using a HIM system, OM typically appears lighter than surrounding mineral grains, suggesting that the mechanism for grayscale contrast is not dependent solely on atomic number. Interestingly, it appears that the darkest materials are the silica/ quartz and carbonate crystals that typically compose the majority of a shale sample, whereas mineral crystals such as pyrite appear bright as they do in SEM.

Given that the impinging particle beam is composed of positively charged helium ions, as opposed to negatively charged electrons, there is a different reaction at the specimen-beam interface. In the event of a high amount of ion buildup and charging, there may be a net loss of detected secondary electrons from a given point on the surface, which would appear as a dark or black region of the image. What the image results suggest in conjunction with this is that the silicate and carbonate grains are more susceptible to buildup of positive charge from the helium ions than the regions of OM. This appears to be the inverse of the charging interactions created by an impinging electron beam, where organic-rich regions are highly susceptible to electron charging. Although positive charging may be an explanation for the low signal from silica and calcite, it may not be the only or even the primary mechanism for the observed effect.

Porosity

The primary goal of this study was to quantify the amount of organic-associated pores below the resolution of conventional FESEM. Each acquired HIM image was processed by separating its components into porosity, OM, or mineral grain, as a function of area percentage. For SEM images this was accomplished with a modified thresholding routine using in-house algorithms, to separate components based on their grayscale values and visual characteristics. Because of the unusual contrast in the helium ion images, proper segmentation of these images required a much greater degree of manual input to properly delineate boundaries between different materials.

Following segmentation, the original SEM images were cropped to match the areas of the HIM images to maintain a direct comparison, and these cropped images were processed using a modified threshold segmentation routine. Figure 3 presents the results of segmented HIM images and segmented FESEM images on the same 51 imaging fields of view from 12 samples. While outliers are present, the trends for both porosity and solid OM content show little systematic bias. The red dashed lines in Figure 3 represent equality of x and y values.

Figure 3.

Left: Scatterplot of data comparing segmented porosity from helium ion-imaged samples (Y-axis) to same region imaged using conventional FESEM (X-axis). Right: Scatterplot of data comparing segmented OM from helium ion-imaged samples (Y-axis) to same region imaged using conventional FESEM (X-axis). Colored data points indicate formation: purple = Eagle Ford; yellow = Marcellus; blue = Wolfcamp; red = Bossier. Figures 1215 present examples from each formation studied.

Figure 3.

Left: Scatterplot of data comparing segmented porosity from helium ion-imaged samples (Y-axis) to same region imaged using conventional FESEM (X-axis). Right: Scatterplot of data comparing segmented OM from helium ion-imaged samples (Y-axis) to same region imaged using conventional FESEM (X-axis). Colored data points indicate formation: purple = Eagle Ford; yellow = Marcellus; blue = Wolfcamp; red = Bossier. Figures 1215 present examples from each formation studied.

Pore Size Distribution Measurements

One method to understand the change in porosity values at varying resolution is through the computation of two-dimensional (2-D) pore size distribution. This technique uses the acquired and segmented images to digitally determine the largest circular diameter that can fit within a certain pore without intersecting a pixel classified as grain or OM. It would be expected that imaging a porous region with higher resolution and therefore smaller potential feature detection would increase the population of pores detected at the lowest end of the distribution graph. Figure 4a and b shows an example of this comparison, in which one region of porous OM was imaged using HIM at both 2 nm and 0.8 nm pixel size. When computing pore size distribution on only the identical area of the images, there was a slight but noticeable increase in pores detected below a threshold of approximately 10-12 nm in diameter, which would be the equivalent of 5-6 pixels in the lower resolution image. The image of this region at 2 nm pixel size was segmented to a porosity of 0.56%, while the image at 0.8 nm pixel size yielded a porosity of 1.87%. The total increase in porosity on this 0.64 μm2 area is 1.31 porosity units. This increase is within a very small area and concentrated in the OM and should not be considered representative of the increase over the entire sample. For example, in a sample with measured 20% OM, an increase of this scale throughout the OM would add approximately 0.3 porosity units to the total.

Figure 4a.

Identical region of Marcellus Shale sample imaged with 2 nm pixel size (left) and 0.8 nm pixel size (right) acquired with HIM.

Figure 4a.

Identical region of Marcellus Shale sample imaged with 2 nm pixel size (left) and 0.8 nm pixel size (right) acquired with HIM.

Figure 4b.

Pore size distribution measurements for the same region imaged with 2 nm pixel size (blue) and 0.8 nm pixel size (green). Left graph shows the amount of porosity measured by each pore diameter; right graph presents cumulative fraction of pore area as a function of pore diameter. Note that the total pore area imaged is greater in this limited region with 0.8 nm pixels than with 2 nm pixels.

Figure 4b.

Pore size distribution measurements for the same region imaged with 2 nm pixel size (blue) and 0.8 nm pixel size (green). Left graph shows the amount of porosity measured by each pore diameter; right graph presents cumulative fraction of pore area as a function of pore diameter. Note that the total pore area imaged is greater in this limited region with 0.8 nm pixels than with 2 nm pixels.

Increased Depth of Field Using HIM

One notable effect of imaging with the helium beam is the greatly increased depth of field that is present. This can be interpreted as both a favorable and unfavorable effect on data acquisition: The increased depth of field provides information from multiple focal planes; however, in quantitative analysis it is desirable to consider an image within a single 2-D plane similar to a thin-section analysis. The reason for the 3-D effect is the higher energy of the larger helium ions compared to the relatively small electrons of the electron beam. The charged helium ions are able to travel a greater distance at high energy, creating secondary electrons upon impact at multiple planes. In these polished samples, this is evident as the effect of seeing down into open pore space, which in SEM images generally appears black because of a lack of electron production from within the pore (Figure 5). Although there is supplemental information from being able to see inside these pores, it should be considered qualitative, as it is not clear how this off-plane pore space should be quantified.

Figure 5.

FESEM and HIM image of Bossier shale sample. Helium ion image provides much sharper pore boundaries, as well as detailed information from within pores. FESEM image acquired at 10 nm pixel size; 1 keV accelerating voltage. HIM image acquired at 1.2 nm pixel size; 30 keV accelerating voltage. mp = micropore; np = nanopore; om = organic matter; min = mineral.

Figure 5.

FESEM and HIM image of Bossier shale sample. Helium ion image provides much sharper pore boundaries, as well as detailed information from within pores. FESEM image acquired at 10 nm pixel size; 1 keV accelerating voltage. HIM image acquired at 1.2 nm pixel size; 30 keV accelerating voltage. mp = micropore; np = nanopore; om = organic matter; min = mineral.

Mineralogy

In the introduction it was mentioned that certain crystals appear to be predisposed to appearing dark in the helium images, suggesting an accumulation of positive charge on the surface blocking or neutralizing secondary electron emission. This is in contrast with FESEM imaging, in which most carbonate and silicate mineral grains yield a similar grayscale value. This is particularly true in Everhart-Thornley detection mode, which measures secondary electrons and was used in this study because of its ability to resolve fine pores more clearly than backscattered electron images. The lack of grayscale definition makes even qualitative assessment of mineral composition unreliable. To determine which minerals appear darker in HIM imaging, a subset of four of the samples used in the previous section were subsequently imaged using SEM-EDS (energy-dispersive x-ray spectroscopy) to determine a qualitative elemental composition, from which mineralogical compositions were inferred.

The four samples were selected based on the presence of dark/black signal grains evident from HIM overview images. For each sample, a series of SEM-EDS elemental maps were acquired on a field of view matching the HIM overview. In addition, the overview images were segmented to highlight the dark regions and threshold them for comparison to the elemental maps. The majority of the dark regions appear to correspond to calcite and, to a lesser extent, silicate minerals (Figure 6).

Figure 6.

Marcellus shale sample; comparison of FESEM secondary electron signal (left), helium ion secondary electron signal (center), and SEM-EDS mineralogy (right). EDS color map: green = clay; yellow = quartz; blue = calcite; white = pyrite; orange = potassium feldspar; red = apatite.

Figure 6.

Marcellus shale sample; comparison of FESEM secondary electron signal (left), helium ion secondary electron signal (center), and SEM-EDS mineralogy (right). EDS color map: green = clay; yellow = quartz; blue = calcite; white = pyrite; orange = potassium feldspar; red = apatite.

Images were also obtained for minerals that appear to be a type of clay or other phyllosilicate. Figure 7 shows what appears to be a structure of laminated illite possibly containing some organic material between the clay laminations. The porosity between clay laminations is mostly not detected at 10 nm/pixel using the FESEM but is clearly visible in the helium ion image. It is not clear whether this clay-associated porosity amounts to any significant storage space for hydrocarbons or if it is most likely filled with water in situ. Imaging interparticle clay porosity may prove to be an important strength of the helium ion imaging system for shale resource plays.

Figure 7.

Left: Helium ion image of clay particle from Marcellus shale sample. Red box inset. Right: Detail of clay particle, visible sheets and porosity in-between sheets.

Figure 7.

Left: Helium ion image of clay particle from Marcellus shale sample. Red box inset. Right: Detail of clay particle, visible sheets and porosity in-between sheets.

Clay Particle Segmentations

Because gas reservoir shales can typically be comprised of upwards of 50% clay minerals (Passey et al., 2010), the potential pore space occupied by clay-bound water can be of significant value in calculating the bulk rock property. Clay interlayer porosity is below the detection threshold for conventional FESEM imaging: the interlayer spacing of illite is approximately 1 nm and smectite between 1 and 2 nm (Klimentidis and Mackinnon, 1986). In Figure 7, it is evident that at 0.97 nm pixel size, there are pores that can be measured within this clay particle. The interlayer features at this pixel size would be equivalent to 1-2 pixels, which is not enough to truly resolve these features. Segmentation of total pore space yielded a value of 4.71% porosity within the boundary of the clay particle (Figure 8). Based on the size of the pores in these images, it is unlikely that they are representative of clay particle interlayer porosity and more likely show secondary porosity, pores between compressed clay particles, or crystal defects. Resolution of clay particle interlayer space would require another type of imaging such as transmission electron microscopy (TEM). Figure 7 also shows slight variations in gray scale that are suggestive of OM partially filling the space between clay laminations. Because of weak contrast, no attempt was made to quantify this possible filling material.

Figure 8.

Segmentation result from Figure 7. Image pixel size is 0.97 nm. Green area represents boundary of clay particle; blue regions represent segmented porosity within clay particle. Total segmented porosity from this image is 4.71%.

Figure 8.

Segmentation result from Figure 7. Image pixel size is 0.97 nm. Green area represents boundary of clay particle; blue regions represent segmented porosity within clay particle. Total segmented porosity from this image is 4.71%.

Organic Matter Imaging

Another noteworthy result of high-resolution helium ion imaging is the existence of a clear subtexture within large OM bodies (Figure 9). It is unclear if this is an integral part of the OM in situ or an artifact created as a result of electron/ion beam interaction or some other preparation mechanism. What is clear is that these textures are not visible in conventional SEM images, but appear well defined under the helium ion beam. Without a clear understanding of the relationship between helium ion-derived grayscale value and atomic number, however, it is difficult to interpret what these textures may represent if they are indeed a real feature under in situ conditions.

In addition, the enhanced resolution of the helium ion beam can be used to examine areas of OM that seem nonporous at 10 nm SEM pixel size. Figure 10, an example from the Marcellus shale, seemingly confirms that a solid OM body is in fact largely nonporous and perhaps a fundamentally different type of material than the adjacent porous OM body. Other images indicate variation in texture within a single OM body. Figure 11, also from a Marcellus shale sample, shows a clear zone of nonporous organic structure surrounded by highly porous OM. The shape of the nonporous zone is suggestive of some difference in original microstructure of the organic bodies.

Figure 9.

Helium ion image of porous OM from Eagle Ford shale sample (left); same region imaged using conventional FESEM at 0.9 keV accelerating voltage (right). The mottled texture of the OM is only visible in the helium ion image.

Figure 9.

Helium ion image of porous OM from Eagle Ford shale sample (left); same region imaged using conventional FESEM at 0.9 keV accelerating voltage (right). The mottled texture of the OM is only visible in the helium ion image.

Figure 10.

Left: FESEM image of porous and seemingly solid OM from Marcellus shale sample. Right: Helium ion image of same region suggests that solid organic region is mostly free of pores and shows distinct boundary between solid and porous OM.

Figure 10.

Left: FESEM image of porous and seemingly solid OM from Marcellus shale sample. Right: Helium ion image of same region suggests that solid organic region is mostly free of pores and shows distinct boundary between solid and porous OM.

Figure 11.

HIM image of Marcellus shale sample OM body, depicting highly porous OM and a defined zone of solid, nonporous OM. Field of view is 1.5 pm; pixel size is 1.4 nm.

Figure 11.

HIM image of Marcellus shale sample OM body, depicting highly porous OM and a defined zone of solid, nonporous OM. Field of view is 1.5 pm; pixel size is 1.4 nm.

Interpretation of Results

The image and data results suggest that within the selected samples, there is not a significant incremental pore fraction within the OM that exists below the detection threshold of conventional FESEM imaging for most samples. The advanced resolution capabilities of the helium ion beam provide much sharper definition of pore boundaries and demonstrate the presence of very small pores in ion-milled samples, but show little evidence of a significant OM pore fraction below ~10 nm pore body diameter. Pore size distribution measurements seemingly confirm the observation that the additional pores that can be resolved by the HIM system as well as the better definition of micropore edges do not add a significant component to the total sample porosity. The sample selection bias and the nature of the contrast formation of the HIM greatly reduce the information that is obtained regarding interparticle or intraparticle porosity. One potential explanation for this is that much of the difference that has been reported between SEM image-based porosity and standard laboratory methods (Loucks et al., 2009; Rine et al., 2010) is because of the presence of clay-bound or capillary-bound water. This water is removed and counted as porosity in traditional core procedures, but SEM images may not measure this clay-bound or adsorbed porosity.

Kliewer et al. (2012) and King et al. (2015) indicate the presence of pores as small as 2 nm using HIM imaging. Consistent imaging of features at this size was not able to be replicated in this study. Better understanding of the evident scarcity of additional pores below 10 nm would require further research, including integration of laboratory-based bulk rock measurements from the same samples used for HIM imaging, to determine whether the selected samples do not have a significant nanopore population in this size regime, or whether the pores are not being resolved as a result of sample preparation or imaging procedures.

Likewise, some evidence exists for the differentiation of OM type based on conductivity and subsequent HIM image contrast; however, making any quantitative determinations would require future study integrating geochemical measurements to distinguish kerogen from bitumen and observe how those different OM types behaved under HIM.

Figure 12.

FESEM and HIM images of Eagle Ford shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 12.

FESEM and HIM images of Eagle Ford shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 13.

FESEM and HIM images of Bossier shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 13.

FESEM and HIM images of Bossier shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 14.

FESEM and HIM images of Marcellus shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 14.

FESEM and HIM images of Marcellus shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 15.

FESEM and HIM images of Wolfcamp shale sample. np = nanopore; om = organic matter.

Figure 15.

FESEM and HIM images of Wolfcamp shale sample. np = nanopore; om = organic matter.

Conclusions

Helium ion microscopy on polished shale samples provides images with excellent definition and resolution of nanometer-scale pores within the OM. The additional resolution provides greater understanding of pores below 10 nm in diameter as well as information from within pore bodies. Imaging and analysis of 51 regions from 12 samples across 4 formations, in comparison with conventional FESEM images (at 10 nm/pixel) of the matching locations, show that overall, the total resolvable organic-associated porosity is not significantly impacted by the additional resolution. Mineral-hosted porosity is not reliably imaged by HIM, although porosity between clay lamina can sometimes be resolved. Additional qualitative information suggests potential uses regarding the heterogeneity of OM bodies and distribution of crystalline mineral solids.

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Huynh
,
2015
,
Pore architecture and connectivity in gas shale
:
Energy Fuels
 , v.
29
, no.
3
, p.
1375
1390
.
Kliewer
,
C. E.
C. C.
Walters
C.
Huynh
L.
Scipioni
D.
Elswick
R.
Jonk
N.
Austin
M. W.
French
,
2012
, Geological applications of helium ion microscopy: accessed, 2016, http://www.imaging-git.com/file/track/3336/1.
Klimentidis
,
R. E.
I. D. R.
Mackinnon
,
1986
,
High-resolution imaging of ordered mixed-layer clays: Clays & Clay Minerals
 , v.
34
, no.
2
, p.
155
164
.
Notte
,
J.
B.
Goetze
,
2014
, Imaging with the helium ion microscope, in
V.
Smentkowski
, ed.,
Surface analysis and techniques in biology
 :
Springer International
,
Switzerland
, p.
171
194
.
Passey
,
Q. R.
K. M.
Bohacs
W. L.
Esch
R.
Kimentidis
S.
Sinha
,
2010
,
From oil-prone source rock to gas-producing shale reservoir: Geologic and petrophysical characterization in unconventional shale-gas reservoirs
: Chinese Petroleum Society/Society of Petroleum Engineers International Oil & Gas Conference and Exhibition,
Beijing, China
,
June 8–10
, 2010.
Rine
,
J.
W.
Dorsey
M.
Floyd
P.
Lasswell
,
2010
,
A comparative SEM study of pore types and porosity distribution in high to low porosity samples from selected gas-shale formations
:
Gulf Coast Association of Geological Societies Transactions
 , v.
60
, p.
825
.
van der Drift
,
E.
D.
Maas
,
2011
, Helium ion lithography, in
M.
Stepanova
S.
Dew
, eds.,
Nanofabrication techniques and principles
 :
Springer International
,
Switzerland
, p.
93
116
.
Walters
,
C. C.
C. E.
Kliewer
,
2014
,
Helium ion microscopy—Geologic applications
:
Goldschmidt Conference
,
Sacramento, California
,
June 8–13
, 2014.
Yang
,
J.
S.
Peng
R.
Loucks
S.
Ruppel
,
2013
,
Organic matter pore characterization in gas shale by HIM
: White Paper, http://www.pdf-archive.com/2016/04/13/organic-matter-pore-characterization-in-gas-shale-by-him, accessed May 25, 2016.

Acknowledgments

We would like to acknowledge Bernhard Goetze, Chuong Huynh, and Germán Neal of Carl Zeiss Microscopy for their assistance with helium ion microscope imaging for this project. We also wish to acknowledge the constructive reviews by Kitty Milliken and an anonymous reviewer, as well as editorial assistance from Terri Olson.

Figures & Tables

Figure 1.

Location of wells and formations where samples for this study originated.

Figure 1.

Location of wells and formations where samples for this study originated.

Figure 2.

Example of segmentation of porosity and OM from a helium ion image. Left: Cropped FESEM image of Bossier shale sample; pixel size is 10 nm. Image acquired at 0.9 keV accelerating voltage. Center: Same region imaged using HIM; pixel size is 5.9 nm. Image acquired at 30 keV accelerating voltage. Right: HIM image with overlay of digitally segmented OM (green) and porosity (blue).

Figure 2.

Example of segmentation of porosity and OM from a helium ion image. Left: Cropped FESEM image of Bossier shale sample; pixel size is 10 nm. Image acquired at 0.9 keV accelerating voltage. Center: Same region imaged using HIM; pixel size is 5.9 nm. Image acquired at 30 keV accelerating voltage. Right: HIM image with overlay of digitally segmented OM (green) and porosity (blue).

Figure 3.

Left: Scatterplot of data comparing segmented porosity from helium ion-imaged samples (Y-axis) to same region imaged using conventional FESEM (X-axis). Right: Scatterplot of data comparing segmented OM from helium ion-imaged samples (Y-axis) to same region imaged using conventional FESEM (X-axis). Colored data points indicate formation: purple = Eagle Ford; yellow = Marcellus; blue = Wolfcamp; red = Bossier. Figures 1215 present examples from each formation studied.

Figure 3.

Left: Scatterplot of data comparing segmented porosity from helium ion-imaged samples (Y-axis) to same region imaged using conventional FESEM (X-axis). Right: Scatterplot of data comparing segmented OM from helium ion-imaged samples (Y-axis) to same region imaged using conventional FESEM (X-axis). Colored data points indicate formation: purple = Eagle Ford; yellow = Marcellus; blue = Wolfcamp; red = Bossier. Figures 1215 present examples from each formation studied.

Figure 4a.

Identical region of Marcellus Shale sample imaged with 2 nm pixel size (left) and 0.8 nm pixel size (right) acquired with HIM.

Figure 4a.

Identical region of Marcellus Shale sample imaged with 2 nm pixel size (left) and 0.8 nm pixel size (right) acquired with HIM.

Figure 4b.

Pore size distribution measurements for the same region imaged with 2 nm pixel size (blue) and 0.8 nm pixel size (green). Left graph shows the amount of porosity measured by each pore diameter; right graph presents cumulative fraction of pore area as a function of pore diameter. Note that the total pore area imaged is greater in this limited region with 0.8 nm pixels than with 2 nm pixels.

Figure 4b.

Pore size distribution measurements for the same region imaged with 2 nm pixel size (blue) and 0.8 nm pixel size (green). Left graph shows the amount of porosity measured by each pore diameter; right graph presents cumulative fraction of pore area as a function of pore diameter. Note that the total pore area imaged is greater in this limited region with 0.8 nm pixels than with 2 nm pixels.

Figure 5.

FESEM and HIM image of Bossier shale sample. Helium ion image provides much sharper pore boundaries, as well as detailed information from within pores. FESEM image acquired at 10 nm pixel size; 1 keV accelerating voltage. HIM image acquired at 1.2 nm pixel size; 30 keV accelerating voltage. mp = micropore; np = nanopore; om = organic matter; min = mineral.

Figure 5.

FESEM and HIM image of Bossier shale sample. Helium ion image provides much sharper pore boundaries, as well as detailed information from within pores. FESEM image acquired at 10 nm pixel size; 1 keV accelerating voltage. HIM image acquired at 1.2 nm pixel size; 30 keV accelerating voltage. mp = micropore; np = nanopore; om = organic matter; min = mineral.

Figure 6.

Marcellus shale sample; comparison of FESEM secondary electron signal (left), helium ion secondary electron signal (center), and SEM-EDS mineralogy (right). EDS color map: green = clay; yellow = quartz; blue = calcite; white = pyrite; orange = potassium feldspar; red = apatite.

Figure 6.

Marcellus shale sample; comparison of FESEM secondary electron signal (left), helium ion secondary electron signal (center), and SEM-EDS mineralogy (right). EDS color map: green = clay; yellow = quartz; blue = calcite; white = pyrite; orange = potassium feldspar; red = apatite.

Figure 7.

Left: Helium ion image of clay particle from Marcellus shale sample. Red box inset. Right: Detail of clay particle, visible sheets and porosity in-between sheets.

Figure 7.

Left: Helium ion image of clay particle from Marcellus shale sample. Red box inset. Right: Detail of clay particle, visible sheets and porosity in-between sheets.

Figure 8.

Segmentation result from Figure 7. Image pixel size is 0.97 nm. Green area represents boundary of clay particle; blue regions represent segmented porosity within clay particle. Total segmented porosity from this image is 4.71%.

Figure 8.

Segmentation result from Figure 7. Image pixel size is 0.97 nm. Green area represents boundary of clay particle; blue regions represent segmented porosity within clay particle. Total segmented porosity from this image is 4.71%.

Figure 9.

Helium ion image of porous OM from Eagle Ford shale sample (left); same region imaged using conventional FESEM at 0.9 keV accelerating voltage (right). The mottled texture of the OM is only visible in the helium ion image.

Figure 9.

Helium ion image of porous OM from Eagle Ford shale sample (left); same region imaged using conventional FESEM at 0.9 keV accelerating voltage (right). The mottled texture of the OM is only visible in the helium ion image.

Figure 10.

Left: FESEM image of porous and seemingly solid OM from Marcellus shale sample. Right: Helium ion image of same region suggests that solid organic region is mostly free of pores and shows distinct boundary between solid and porous OM.

Figure 10.

Left: FESEM image of porous and seemingly solid OM from Marcellus shale sample. Right: Helium ion image of same region suggests that solid organic region is mostly free of pores and shows distinct boundary between solid and porous OM.

Figure 11.

HIM image of Marcellus shale sample OM body, depicting highly porous OM and a defined zone of solid, nonporous OM. Field of view is 1.5 pm; pixel size is 1.4 nm.

Figure 11.

HIM image of Marcellus shale sample OM body, depicting highly porous OM and a defined zone of solid, nonporous OM. Field of view is 1.5 pm; pixel size is 1.4 nm.

Figure 12.

FESEM and HIM images of Eagle Ford shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 12.

FESEM and HIM images of Eagle Ford shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 13.

FESEM and HIM images of Bossier shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 13.

FESEM and HIM images of Bossier shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 14.

FESEM and HIM images of Marcellus shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 14.

FESEM and HIM images of Marcellus shale sample. np = nanopore; om = organic matter; min = mineral.

Figure 15.

FESEM and HIM images of Wolfcamp shale sample. np = nanopore; om = organic matter.

Figure 15.

FESEM and HIM images of Wolfcamp shale sample. np = nanopore; om = organic matter.

Detailed locations of wells and samples used in this study, and the number of helium ion microscope images acquired and processed from each well.

Table 1.
Detailed locations of wells and samples used in this study, and the number of helium ion microscope images acquired and processed from each well.
WellFormationCounty, StateNumber of SamplesNumber of Images
Well XMarcellusHarrison, WV416
Well YMarcellusTaylor, WV12
Well SWolfcampReagan, TX27
Well REagle FordBee, TX217
Well ZEagle FordZavala, TX14
Well QEagle FordLaSalle, TX13
Well TBossierHarrison, TX12
WellFormationCounty, StateNumber of SamplesNumber of Images
Well XMarcellusHarrison, WV416
Well YMarcellusTaylor, WV12
Well SWolfcampReagan, TX27
Well REagle FordBee, TX217
Well ZEagle FordZavala, TX14
Well QEagle FordLaSalle, TX13
Well TBossierHarrison, TX12

Contents

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2012
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,
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I. D. R.
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High-resolution imaging of ordered mixed-layer clays: Clays & Clay Minerals
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34
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2
, p.
155
164
.
Notte
,
J.
B.
Goetze
,
2014
, Imaging with the helium ion microscope, in
V.
Smentkowski
, ed.,
Surface analysis and techniques in biology
 :
Springer International
,
Switzerland
, p.
171
194
.
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,
Q. R.
K. M.
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W. L.
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S.
Sinha
,
2010
,
From oil-prone source rock to gas-producing shale reservoir: Geologic and petrophysical characterization in unconventional shale-gas reservoirs
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Beijing, China
,
June 8–10
, 2010.
Rine
,
J.
W.
Dorsey
M.
Floyd
P.
Lasswell
,
2010
,
A comparative SEM study of pore types and porosity distribution in high to low porosity samples from selected gas-shale formations
:
Gulf Coast Association of Geological Societies Transactions
 , v.
60
, p.
825
.
van der Drift
,
E.
D.
Maas
,
2011
, Helium ion lithography, in
M.
Stepanova
S.
Dew
, eds.,
Nanofabrication techniques and principles
 :
Springer International
,
Switzerland
, p.
93
116
.
Walters
,
C. C.
C. E.
Kliewer
,
2014
,
Helium ion microscopy—Geologic applications
:
Goldschmidt Conference
,
Sacramento, California
,
June 8–13
, 2014.
Yang
,
J.
S.
Peng
R.
Loucks
S.
Ruppel
,
2013
,
Organic matter pore characterization in gas shale by HIM
: White Paper, http://www.pdf-archive.com/2016/04/13/organic-matter-pore-characterization-in-gas-shale-by-him, accessed May 25, 2016.

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