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Pore architecture and connectivity in gas shale Hubert E. King, Aaron P. R Eberle, Clifford C Walters, Chris E Kliewer, Deniz Ertas, and Chuong Huynh Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef502402e • Publication Date (Web): 12 Feb 2015 Downloaded from http://pubs.acs.org on February 18, 2015
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Energy & Fuels
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Pore architecture and connectivity in gas shale
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Hubert E. King, Jr. †* Aaron P. R. Eberle,† Clifford C. Walters,† Chris E. Kliewer,† Deniz Ertas, † and Chuong Huynh‡
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†
ExxonMobil Research and Engineering Company, 1545 Route 22 East, Annandale NJ
08801-0998 ‡
Carl Zeiss Microscopy, LLC, Applications Laboratory, One Corporation Way, Peabody,
MA 01960- 7925
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Corresponding Author
12
*
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Tel: 908-730-2888
email:
[email protected] 14
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Abstract
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The pore size distribution and architecture in gas shale were studied using a combination of
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small-angle neutron scattering (SANS), mercury injection capillary pressure (MICP), and helium
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ion microscopy (HIM). SANS analysis shows that the pore size population is not a power-law
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distribution across many length scales, typical of sedimentary rocks, but contains an anomalous
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population of pores on-the-order ~2 nm, housed primarily in the organic matter. A model is
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presented showing how a "foamy porosity" with such a characteristic size is a direct result of
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diagenetic evolution of kerogen. Cross-linking of the kerogen combined with phase separation
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of gas/oil, leads to arrested coarsening with a length scale set by cross-length density. These
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pore populations determined by the scattering model are directly supported by HIM images.
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Pore connectivity determined through pore size-to-pore-throat analysis, suggests that inter pore
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connections are also distinct from typical sedimentary rocks. The pore/throat ratio, unlike the
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simple ratios predicted from sphere packing and found for clastic rocks, is nearly constant over
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all pore sizes. Kerogen diagenesis is a recognized source of excess internal pressure. When this
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pressure causes failure of the material surrounding the kerogen to create escape pathways for the
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phase-separated fluid, it is likely that escape pathways will connect intergranular porosity via
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microfractures, producing the relatively narrow aperture size distribution.
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Keywords : shale, pores, pore architecture, pore connectivity, SANS, He-ion microscopy
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1. Introduction
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In the last several years, natural gas production from shale has grown to become a major
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energy supply for the United States 1and potentially the world 2. This is a result of improved
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drilling and extraction methods that allow for the commercial production of gas from low
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permeability rocks. The rapid development of US shale gas plays has outpaced scientific study
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and, unlike conventional oil and gas deposits where the rock-fluid interactions are well
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understood, our understanding of the physics of gas storage and flow in shale is evolving. A
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more complete understanding of the fundamental processes of shale gas generation, storage and
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transport may help improve U.S. domestic production efficiency and is crucial for the evaluation
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of international plays.
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Shales are sedimentary rocks consisting of both organic and inorganic matter. In geologic
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nomenclature these rocks fall into the mudstone classification, which acknowledges their
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composition as well as the inherently small particle size of the constituent minerals. The total
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porosity can vary significantly, ranging from almost zero to greater than 10 vol. % (e.g.,
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Clarkson et al.3 and references therein). While it is well established that the productivity of shale
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is a function of both gas storage and transport, the pore architecture in most shales is poorly
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understood. The pores are known to be distributed between the inorganic and organic phases, but
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their fine-grained nature and the predominance of meso- and micro-pores presents significant
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challenges. A correlation can be seen between total organic carbon (TOC) and total porosity in
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many organic-rich shales4-6 , and sorption measurements suggest that for productive shales the
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majority of the porosity resides within the organic matter5, 7, 8 . This is in contrast to coarser-
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grained clastic sedimentary rocks, where the pores form within a mostly mineral framework.
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In this work, we studied the pore architecture and connectivity in gas shales using a
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combined set of analyses: Porosimetry, both helium and mercury (mercury injection capillary
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pressure, MICP), along with small angle neutron scattering (SANS) measurements give
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measures of pore size and number. This is complimented by direct imaging using helium ion
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microscopy (HIM). We propose that by combining analyses that are diagnostic of pore and pore
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throat size distributions with advanced imaging a more complete representation of the pore
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architecture and connectivity can be determined for gas shales.
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MICP is used to infer the entry (threshold) pressure and pore aperture size where there is a
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continuous path for hydrocarbon flow. In this technique, a sample is evacuated and flooded with
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mercury. The pressure on the mercury is incrementally increased forcing the mercury into
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progressively smaller pore throats and at maximum pressure pore throats as small as 3-4 nm can
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be sampled. Throughout the literature, MICP measurements have been used to correlate porosity
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and permeability for conventional reservoir rocks9. Similar studies have applied MICP in their
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examination of the pore structure in shales3, 6, 10-16 . When combined with data from gas
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adsorption experiments, the inferred pore size distributions are complex and variable.
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Nevertheless, the overall findings are fairly consistent in that mesopores (2-50nm) are
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predominant, micropores (50 nm) are variable; see Ref. 17 for a description
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of pore size nomenclature. Furthermore, there is consensus that shales have inherently high
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threshold pressures, low mercury withdraw efficiencies, and a significant portion of the smaller
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meso- and micro-pores are not accessible to mercury injection.
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Neutron scattering techniques have proven very powerful in describing the porosity in
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conventional clastic rocks18-22. SANS data can be described through an approximate power-law
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intensity decline with increasing scattering angle, Intensity ~ Q-x, where Q = 4πsin(θ)/λ with θ
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the scattering angle, λ the radiation wavelength, and x is a coefficient typically between 2 and 3
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for sedimentary rocks. This characteristic behavior can equivalently be described as a surface
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fractal or a power-law size distribution of pore sizes23 and the analytic solution for power-law
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pore size distribution has been extended through use of a numeric model for the size-number
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relationship, the polydisperse sphere model (PDSM)21. This technique, when appled to
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conventional reservoir rocks, has shown consistent measurements made by various porosimetry
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techniques and SANS.
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SANS analysis of shale structure has a long history beginning with early studies24, 25 that
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identified a characteristic power-law scattering behavior. This behavior was identified as arising
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from a surface fractal pore structure22, 26. In one early study, Ma et al.27 related the implied
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surface area to gas uptake. More recently, use of the PDSM model has led to several applications
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of SANS to investigate shale porosity13, 28, 29 . Clarkson et al.3, 30 combined SANS analyses, gas
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adsorption and MICP measurements showing the presence of nanometer to micrometer sized
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pores. Jin et al.31 showed that that pore size distributions can be altered by secondary effects such
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as weathering.
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MICP, gas adsorption, and SANS analyses all rely on models to infer pore architecture, while
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microscopy can provide direct evidence. Optical microscopy is sufficient to image the majority
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of the pores in conventional reservoir rocks, but it can only image a fraction of the pores in
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shale5, 32. In recent years, scanning electron microscopy (SEM) has become a standard tool for
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pore analysis of fine grained shales and is very capable of imaging the macropores and some of
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the mesopores. However, a flat surface is critical for SEM to achieve the highest resolution, and
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numerous methods have been used including external Ar-ion milling4, 5, 7, 32-34, internal broad ion
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beam polishing (BIB-SEM)12, 35 and focused ion beam milling (FIB-SEM)36-39. The latter
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techniques can be used to create a 3D display of a small rock volume by stacking a series of 2D
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images10, 14, 40-42. Focused ion beam systems are also used to prepare thin shale samples for
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scanning transmission electron microscopy (STEM)36, transmission electron microscopy
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(TEM)41 and scanning transmission X-ray microscopy (STXM)43-45. SEM and STEM
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applications to organic-rich shales can resolve mesopores >5 nm and smaller pores >2 nm are
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suggested in the best images. However, these imaging techniques can detect only a small fraction
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of the total porosity measured by gas absorption with as little as ~2% detected in highly organic
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rich shales4 up to ~40% in an organic-lean shale39. In theory, atomic force microscopy could
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resolve micropores ?@ Aℎ?CA~DE>?@ FCG@A@, where M ~ 4 A1/2 for the fit shown. It is not obvious why
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square root dependence would arise, but this sub-linear scaling tells us that pore-throats are
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relatively unchanging as pore diameter changes. All samples show this behavior, suggesting that
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the pore throats between pores in shales occur with a characteristic narrow throat size. As is
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apparent if the function is taken to very small pore sizes, this scaling eventually breaks down, i.e.
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at about 1.5 nm, pore size ~ pore throat. Lacking MICP data, we have no information on the
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actual pore throats, but we can speculate that this may indicate a limiting behavior as the pores
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reach the size of the characteristic peak in SANS distribution. If, as has been proposed the pores
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in shales result from fluid expulsion5, perhaps these narrow pore throats are a diagenetic feature
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arising from internal fracturing of the rock when internal stress exceeds the yield strength of the
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matrix.
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3.3 Helium Ion microscopy imaging of shale porosity Pores of varying size were observed in both mineral and organic material within all samples.
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Macro- and meso-pores are common features in the Mowry Fm. shale, occurring in mineral
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dissolution features that frequently contain secondary cements as seen in Fig. 7. Macropores are
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common in quartz matricies and appear to be connected by relatively large pore throats while
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mesopores are more typical of clays that occur in isolation with no connecting pore throats.
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FIGURE 7
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The nature of the organic matter seen in the Mowry Fm. shales (Fig. 8) is similar to what we
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have observed in other gas shales47. The organic material appears to be composed of thin ( 200 nm
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are rarely observed. The very large pore, > 500 nm in diameter, shown in Fig. 8(b), could be a
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mineral pull out feature, although rounded mineral grains corresponding to this feature are not
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obviously present. Assuming this feature is a macropore within the organic matrix, the pore is
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connected to the underlying porous organic matter through openings typically < 10 nm,
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consistent with the SANS and MICP data that indicate that pore throats do not scale with pore
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size. This pore also illustrates the anisotropic nature of macropores present in organic matter as
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the width parallel to the bedding plane exceeds its height. Such pores would be exceedingly
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difficult to detect by SEM in samples ion milled perpendicular to the bedding planes.
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FIGURE 8
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The HR1 sample from Middle Otter Park Fm. is a moderately organic-rich shale (TOC =
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4.20 %) with a total porosity of φ = 0.067. The sample is predominantly composed of quartz
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(57%) and clays (26%) with minor amounts of pyrite, calcite, plagioclase, and ankerite/Fe-
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dolomite.
Macro- and meso-pores are present in both mineral and organic materials, as
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illustrated in Fig. 9. Here, we find intercrystalline mesopores (1) and intracrystalline macropores
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(2) in close association with porous organic matter. The organic matter can be seen lying under a
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thin layer of clay that has been pulled up (3) and is completely exposed where it forms a very
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large macropore (4). Higher magnification of the organic matter connected to the macropore
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displayed in Fig. 9(b) shows it to contain pores typically ranging from 2 to 10 nm in size. The
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macropore itself is connected to smaller pores ranging from 2 to ~ 30 nm. These smaller pores do
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not appear to be connected laterally, but there may be pore throats to pores underlying the
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macropore surface.
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FIGURE 9
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To facilitate comparison with the SANS results, we have calculated the expected pore size
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distribution for Fig. 9b and (c). Consider the HIM image of pores to be an approximately 2D
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view with a thickness dependent on pore diameter (e,g, can we expect the pore to intersect the
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observation plane). Under this assumption, we use the 3D volume distribution from SANS. The
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calculations were performed as follows. Using the PDSM pore size distribution, which gives the
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number of pores per unit volume, we calculate the pore population in a 300nm x300nm x D
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volume. Using a random number generator the x,y,z position of each pore is assigned. We then
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generate a circle, positioned at x,y and having a diameter equal to the sphere at height z=0.
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These scaled images are shown in Fig. 9. Given the assumptions, the comparison between SANS
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and HIM images appears rather good.
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HR2 is an organic-lean shale (TOC = 0.54 %) from the Lower Otter Park Fm. with a total
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porosity of φ = 0.026. It is composed primarily of clays (49 %) and quartz (28 %) with minor
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amounts of ankerite/Fe-dolomite, feldspar, dolomite, calcite, and pyrite. As expected from the
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low organic content, the observed porosity occurs within the mineral matrix, primarily at grain
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boundaries as seen in Fig. 10. These macropores appear to be formed by mineral dissolution and
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the connected network can be observed to extend for several 100 nm. Smaller mesopores are
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seen within mineral grains that also appear to result from mineral dissolution.
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FIGURE 10
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HR3 from the Evie Fm. contains a moderate amount of organic matter (TOC = 5.57%) with a
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total porosity of φ = 0.056. It is composed primarily of quarz (74%), calcite (11%) and clays (9
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%) with minor amounts of dolomite, plagioclase, and pyrite. The porosity imaged by HIM for
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this sample of Evie shale is primarily due to mineral dissolution (Fig. 11). The exposed grains
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appear to be eroded (Fig. 11a) and large, connected macropores are present along grain
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boundaries ( Fig. 11b). Mesopores are abundant, displayed in Figures. 11c and 11d), but are
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largely isolated and unconnected.
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Organic porosity was not readily imaged in this specific sample of Evie FM. shale, even
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though the bulk rock has sufficient organic carbon to account for some of the measured porosity.
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We attributed the lack of organic carbon in the piece we examine to sample heterogeneity. The
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characteristics of the organic matter in Evie Fm. shale is better seen in samples from a nearby
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well at nearly the same depth, which has higher TOC (10.04 %) but a lower total porosity (φ =
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0.0472). Highly porous organic matter is readily imaged as existing as thin layers between clays
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and around larger clastic grains (Figs. 12 and 13). Mesopores in the organic material are
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typically < 20 nm and micropores as small as 1 nm can be imaged. For the most part, these small
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pores appear to be isolated from each other but they can aggregate forming much larger pores as
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is seen in Figures. 12b and 13b. Openings can be seen within these structures and may represent
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pore throats to the underlying pore network. Porous organic matter can be seen typically under
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thin layers of non-porous clays and are best imaged along fractures that occur across bedding
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plane. Occasionally we see examples where the generally non-porous top surface manifests
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meso- and micro-pores similar to those displayed in Figures. 13c and 13d.
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FIGURE 11
609 610
FIGURE 12
611 612
FIGURE 13
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The porosity distributions and pore network morphologies observed by helium ion
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microscopy supports the conclusions drawn from the SANS and MICP models with the
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exception that there little evidence for micropores < 2 nm in size. Pores of such size are near the
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instrument's resolution.
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underlying structure, within either the mineral or organic phases that is not exposed by the
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mechanical sample preparation. One likely possibility is that the micropores are isolated and are
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not prone to fracture open. Even if fractured, few of these micropore would manifest their full
The absence of imaged micropores suggests that there is still an
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diameter and < 1 nm features are not readily imaged. Another possibility is that the micropores
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are mostly restricted to organic macerals derived from terrestrial plant debris. These macerals
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would resemble coal, which based on gas sorption measurements contains mostly micropores.
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Such macerals would be hard to locate and identify by HIM.
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4. Conclusions
628 629
1. Pores in gas shale reservoirs rocks exhibit a two component pore size distribution: a
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broad power-law distribution and a narrow size range population of small (0.1 A-1 b Derived from Hard Sphere fit parameters using ρorganic =3.0 x 10-6A-2
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Table 4. Hard Sphere fit parameters and calculated scattering length density for foamy porosity in organic matrix. The parameters are described in the text. Sample
RHS
PD
φHS
nm
ρfit
ρcalc
(A-2, x 10-6.)
(A-2, x 10-6.)
M1
0.97±0.05
0.01
0.086±0.034
1.090±0.212
4.268±1.88
M2
0.61±0.02
0.01
0.091±0.016
1.595±0.153
7.029±1.41
HR1
0.69±0.03
0.28±0.11
0.281±0.106
0.956±0.114
3.110±1.21
HR2
0.97±0.08
0.01
0.384±0.068
0.342±0.034
3.105±0.63
HR3
0.83±0.02
0.09±0.10
0.347±0.022
0.639±0.171
1.805±0.79
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7. Figures
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Figure 1. Example from sample HR3 showing A) SANS data showing characteristic high Q
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scattering feature arising from population of the O ~ 2 nm pores B) pore size distribution from
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PDSM analysis compared to power-law volume fraction (solid line) found in typical sedimentary
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rock C) calculated pore population from PDSM result, and D) helium ion image taken at same
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magnification as simulated image with selected pores labeled.
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Figure 2. a) SANS, and USANS for sample M2 with fit of the PDSM model, Eq. (2). b) The
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pore size distribution function extracted from fit in a) using the different low-Q limits for the
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model given in the legend.
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Figure 3. a) SANS scattering intensity for shale samples and PDSM model, Eq. (2), fits. Data
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shifted vertically by factors of 1, 0.6, 0.005, 0.001, and 0.00002 for clarity. b) Pore volume
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fraction distribution function. Line represents average size between samples for peak in
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distribution outlined in grey region.
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Figure 4. a) Sample M2: Hard Sphere Model fit to Q>0.1 A-1 data , b) pore size distribution
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from Hard Sphere Model plus PDSM scattering from mineral porosity. Height of three bars
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shows relative porosities of three models for D
