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Quantitative Analysis of Nano-pore Structural Characteristics of Lower Paleozoic Shale, Chongqing (Southwestern China) - Combining FIB-SEM and NMR Cryoporometry Shaoqing Tong, Yanhui Dong, Qian Zhang, Derek Elsworth, and Shimin Liu Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b02391 • Publication Date (Web): 30 Nov 2017 Downloaded from http://pubs.acs.org on December 3, 2017
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Energy & Fuels
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Quantitative Analysis of Nano-pore Structural Characteristics of
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Lower Paleozoic Shale, Chongqing (Southwestern China) -
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Combining FIB-SEM and NMR Cryoporometry
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Shaoqing Tong1,2, Yanhui Dong1,2,3, *, Qian Zhang1,2, Derek Elsworth3, Shimin Liu3
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ABSTRACT
Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Department of Energy and Mineral Engineering, G3 Center and EMS Energy Institute, The Pennsylvania State University, University Park, PA-16802, USA * Corresponding author: Yanhui Dong, Email:
[email protected] 12
Characterizing nano-pore structure is one of the most important factors in understanding gas
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storage and transport in shale reservoirs – but remains a significant challenge. In this work, we
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combine the benefits of Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and
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Nuclear Magnetic Resonance Cryoporometry (NMR-C) to characterize the nano-pore structure of
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lower Paleozoic shales from Chongqing, southwestern China. Mineral composition is qualified
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through X-ray Energy Dispersive Spectroscopy (EDS) and Field-Emission Scanning Electron
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Microscopy (FE-SEM). Voids in 2D micrographs are classified into types of meso- and/or micro-
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fractures, inter particle pores (InterP pores), intra particle pores (IntraP pores), and pores in
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organic matter (OM pores). An Otsu thresholding algorithm and an edge detection algorithm (in
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Avizo) are used to segment OM, InterP and IntraP pores, and to establish 3D pore structure for
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pore-network modeling (PNM). The pore size distribution (PSD) is measured via PNM and
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compared to the PSD recovered from NMR-C. Results indicate that the small OM pores have
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favorable potential for storing adsorbed gas because of their apparent interconnectivity and higher
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porosity; conversely, InterP and IntraP pores are mostly isolated and with low porosity. The peaks
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of the PSD, recovered from PNM, range from 10~20 nm and 60~120 nm and are in good
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agreement with the peaks of the PSD recovered from NMR-C. This suggests that the NMR signals
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in these discontinuous ranges are due to OM pores, InterP and IntraP pores. This indirectly
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demonstrates that the probe liquid in the NMR-C experiments (i.e. water), may indeed enter the
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nano-scale pores in the shale during centrifugal saturation. Comparison of the PSD from PNM and
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NMR-C also shows that NMR-C has a higher sensitivity and accuracy in detecting nano-pores.
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Combining FIB-SEM and NMR-C is a promising technique in detecting and characterizing the
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nano-porosity of shales.
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KEYWORDS
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Lower Paleozoic Shale, Nano-pore Structure, Pore Network Modeling, FIB-SEM, NMR-
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Cryoporometry
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1 Introduction
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Shale gas is an important energy resource with rapid development in North America.1,2 It is
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expected to play an increasingly important role in Asia and Europe in the near future.3 In recent
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years, the lower Paleozoic shale gas reservoirs in the Sichuan Basin, southwestern China have
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been explored and developed.4,5 The Weiyuan 201 Well (W201) was the first in China to provide
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industrial-scale gas flow after hydraulic fracturing. The thermal maturity of the organic matter of
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the ancient shales in the Sichuan Basin is higher than that of the Barnett, Marcellus and
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Haynesville formations in North America - implying a lower degree of development of the pores
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in the organic matter.6 Previous studies have indicated that shale pore structure has a strong
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influence in controlling the shale gas content and storage and transport mechanisms.7,8,9 In
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particular, pores in organic matter provide the main storage space for absorbed gas in shale.10,11,2,12
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Hence, a correct understanding of the 2D and 3D pore structure is the foundation for shale gas
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exploration, development, and research.9,13
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As a typical low permeability reservoir, shale is a fine-grained, compact sedimentary rock
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comprising pores and fractures at a variety of scales. The 2D and 3D pore morphology, size
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distribution, and other features of shale have been investigated by many imaging methods. These
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include field-emission scanning electron microscopy (FE-SEM), FE-SEM coupled with focused
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gallium ion beam (FIB-SEM), or Nano X-ray computed tomography (Nano-XCT).2,5,14,15,16 Four
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scales of void space can be identified in shale reservoirs. These include mesoscale fractures, meso-
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and/or micro-fractures, inter particle and intra particle pores (InterP pores and IntraP pores), and
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nano-scale pores in organic matter (OM pores).2,3,14,17,18 Imaging methods are able to distinguish
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different types of pores in shale. Nano-XCT is a non-destructive method using transmission X-ray
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attenuation imaging and is widely applied to detect pores in shales. Nano-XCT has been used to
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study the development of nano-pores in the Chang 7 Member of organic-rich shale19 and to
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characterize the connection of typical 3D pores in the Jiulaodong formation.2 However, the
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minimum voxel size for Nano-XCT is approximately 32~50 nm, limiting the resolution of pores
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that must transit of the order of three voxels.
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FIB-SEM usually provides 3D micrographs at fine spatial resolutions of ~10 nm.20,21
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Although milling and imaging layer-by-layer can partly damage the shale sample, nano-pores in
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FIB-SEM micrographs are much more distinguishable than those recovered by Nano-XCT.
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However, previous studies3,11,14 show that the majority of pores in organic matter range from 2 nm
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to tens of nanometers. A few could be as large as few hundred nanometers. This means that
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FIB-SEM could not cover the entire pore size distribution (PSD). In addition, FIB-SEM has a
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small field of view.
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Nuclear magnetic resonance cryoporometry (NMR-C) is a novel and emerging technique that
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can probe PSD from 2 nanometers to several micrometers—generally more than three orders of
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magnitude. It is based upon the pore-diameter-related melting and/or freezing points of pore
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imbibed materials.11,12,22,23,24 For detection, NMR-C exploits the transverse relaxation time
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difference between solid and liquid NMR signals.22 Firouzi, et al.22 first reported PSD
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measurement using NMR-C method on shale, but they failed because the NMR instrumentation
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used could not scan temperatures lower than 253 K to detect pores with size smaller than 6nm.
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Using improved NMR instrumentation and saturation methods, later researches11,23,24 successfully
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measured the PSD of shale using NMR-C. Fleury, et al.23 report compared the PSDs from NMR
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relaxation and NMR-C in shales and found that the PSD obtained from cryoporometry is quasi
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uniform from 2 nm up to 100 nm. Zhang, et al.24 utilized NMR-C to examine both bulk matrix and
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pulverized shale samples with the results surprisingly indicating that the porosity of the bulk
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sample was apparently smaller than that of the pulverized shale sample. Li, et al.25 used NMR-C,
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NMR, mercury intrusion porosimetry, and gas adsorption methods to study pore structure
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characteristics of shales in Southwestern China. They obtained good agreement in the PSDs
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between the various methods. All previous studies focused on the applicability and accuracy of
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NMR-C compared with other intrusive methods when probing pore structures in shale. However,
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comparisons of NMR-C with imaging methods have been rarely reported.
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In this work we use both FIB-SEM and NMR-C to characterize nano-pore structure of shale.
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3D pore structure modeling and pore network modeling (PNM) are used for the analysis of OM
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pores as well as InterP and IntraP pores in shale from the W201 well. Additionally, the PSDs
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obtained from PNM and NMR-C are quantitatively analyzed and contrasted to understand and
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assess the corresponding types and structure of nano-porosity in shale.
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2 Geological setting
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The shale samples were taken from the Weiyuan area in western Chongqing, from the upper
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Yangtze Platform. The Weiyuan area extends over about 2700 km2 in the southwestern part of the
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Sichuan Basin (Figure 1). The region is bordered on the southeastern edge of the Leshan-Longnvsi
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paleo-uplift and is characterized as a large-scale NEE-SEW oriented domal anticline.26 The
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Weiyuan area has experienced three major orogenies, comprising the Caledonian, Indosinian, and
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Himalayan. Emergent Proterozoic and Paleozoic strata in this area are mainly from the Sinian,
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Cambrian, Ordovician, Silurian, and Permain periods (Figure 2).
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The upper Sinian is subdivided into the Dengying and Doushantuo formations with a total
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thickness of 659~670 m.26,27 The Dengying formation, comprises mainly microcrystalline dolomite
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and is the main conventional gas reservoir in the Weiyuan area.27,28 A total of 766~1019 m of the
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Cambrian unit consists of the Jiulaodong and Yuxiansi formations in the lower Cambrian and the
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Xixiangchi formation in the upper Cambrian.29,30 The Jiulaodong formation contains two-thirds of
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the sedimentary column of organic-rich black shales with a thickness of 230~400 m. It is uniformly
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distributed in the Weiyuan area and is the dominant source rock and caprock of the Sinian gas
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reservoir. It is also the target formation of the W201 well.29 The Yuxiansi and Xixiangchi formations
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are primarily interbedded with sandstone and siltstone. The Ordovician strata are poorly developed
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in the Weiyuan area due to the advent of the Caledonian orogeny. The lower Ordovician Luohanpo
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and Dachengsi formations are dominated by dolomite with a total thickness of 131~280 m, while
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the middle Ordovician Baota formation mainly consists of limestone with thick shales.
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The lower Silurian Longmaxi formation is a set of organic-rich sedimentary black shales and
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silty mudstones in southeastern Weiyuan, but is absent in the northwestern part of this region. The
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Longmaxi formation is an important shale gas source in southwestern China. However, the 0~140 m
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thinness of the Longmaxi formation in this region limits the reserves of shale gas. Due to uplift
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during the Hercynian orogeny, the Devonian and Carboniferous strata are absent.26,28 The Permian
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contains the lower Permian Maokou formation and the upper Permian Longtan formation. The
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Maokou formation is characterized by limestone with a thickness of 204~459 m, while the Longtan
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formation consists of limestone and shale with a thickness of 188~242 m.26
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Figure 1. Schematic illustrating the primary structural outline and border of the Sichuan Basin, the
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location of the Weiyuan gas field and the W201 well, relative to major cities.
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Figure 2. Stratigraphic column of the Weiyuan area including chronology, lithology, and thickness. The grey denotes shale gas source formations in southwestern China.
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3 Samples and Methods
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3.1 Sample preparations
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Shale samples from the marine sedimentary Jiulaodong formation were sourced from a depth
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of 2749~2751 m the W201 well – a well with industrial-scale gas flow that was drilled by China
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National Petroleum Corporation (CNPC) in 2011. 4 mm diameter cylindrical samples both
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perpendicular and parallel to the beddings were recovered from core plugs and prepared for
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FE-SEM, FIB-SEM and NMR-C experiments (Figure 3).
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Figure 3. Sample preparation details. (A) 4 mm diameter cylindrical shale samples recovered by core drill. A third of the sample (orange box) is used for FE-SEM and FIB-SEM with the other two-thirds (blue box) used for NMR-C. (B) Argon ion polished samples for FE-SEM and FIB-SEM, sheathed in a silver conductive adhesive. (C) Shale sample in the NMR-C sample tube with ultrapure water.
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3.2 Experimental facilities
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3.2.1 FE-SEM and FIB-SEM
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A FE-SEM equipped with an EDS and FIB-SEM were used to characterize the nano-pore
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structure of the shale samples. The FE-SEM samples were prepared using a 4-mm diameter core
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drill and argon ion polished (Figure 3 (A) and (B)). Both bedding-parallel and -perpendicular
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surfaces of the shale samples were imaged with a Zeiss Merlin scanning microscope at 5 kV. A
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voltage of 15 kV was subsequently used to qualitatively analyze minerals and organic matter via
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EDS (BRUKER XFlash 6130).
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The shale samples were placed into the chamber of a ZEISS Crossbeam 540 FIB-SEM, and
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electron-beam-scanned at a vacuum of < 5×10-6 mbar. The region containing varied organic matter
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and quartz, calcite, and pyrite was selected as the principal region of interest (ROI). To align the
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shale sample surface perpendicular to the gallium ion beam, the sample desk was rotated 52 °
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(Figure 4 (A)). Moreover, the ROI was sprayed with a thin layer of Pt to counter any damage from
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the gallium ion beam. After deposition of Pt, the FIB-SEM automatically milled and imaged the
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sample layer-by-layer (Figure 4 (B)). The layer thickness was 10 nm and the selected ROIs were
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10.07×9.64×4.28 μm and 11.3010.268.18 μm respectively (Figure 4 (C)). Avizo 9.0 software
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was used for analysis of the 2D and 3D micrograph data.
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Figure 4. (A) Schematic diagram of the milling and imaging procedure in a FIB-SEM system. (B) Site on the shale sample milled in cross section by the gallium ion beam. (C) and (D) are the two ROIs containing organic matter, quartz, calcite and pyrite, with an extent of 10.07×9.64×4.28 and 11.3010.268.18 , respectively; voxel size is 10 nm3.
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3.2.2 Nuclear magnetic resonance cryoporometry and nitrogen adsorption method
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measurement
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NMR-C method based on the differences in nuclear magnetic resonance relaxation between the
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freezing or melting point of the fluids in the porous media. Since NMR distinguishes between the
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solid and liquid states of the permeant (quantifying only the liquid mass) the NMR-C signal gives a
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clear transition of permeant state with high signal-to-noise ratio. NMR-C relies on the
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pore-size-dependent depression of the melting temperature of a pore-filling material given by the
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Gibbs-Thomson equation,
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∆
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Here, and are the bulk and the pore melting temperatures, is the solid-liquid surface
∆
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free energy, ∆
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diameter. For arbitrarily shaped pores, depends on the average curvature of the pore wall
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rather than on a uniform size parameter such as . Then equation (1) may be rewritten as:
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∆
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Here, &' is the melting point depression constant. In a typical experimental protocol, the NMR
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signal intensity I(T) from the molten fraction of the pore-filling material is measured as the initially
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frozen sample is incrementally-warmed. To separate this signal from that of the frozen fraction, the
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difference between the transverse relaxation times T2 in the respective states is exploited. The PSD
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is then calculated by numerical differentiation of I(T). The PSD can be estimated if the NMR signal
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intensity is properly calibrated in terms of the mass of the sample. Smaller temperature increments
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may resolve smaller differential pore sizes and finer PSD features.11
#$%
! is
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the latent heat of melting, " is the density of the solid, and is the pore
(2)
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In these measurements, the NMR-C shale samples were first pretreated in a drying oven for 24
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hours at 378.15 K. The dry 4-mm diameter cylindrical shale samples were then placed in 10 mm
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diameter thin-walled high-resolution NMR sample tubes. The tubes are approximately 2 cm in
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length and filled with ultrapure water (Figure 3 (C)). The shale samples in the tubes were immersed
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in water for ~10 h and then centrifuged at 5000 rpm for 3 h at room temperature to help saturate the
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shales. The NMR-C measurements were conducted in a low field nuclear magnetic resonance
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device manufactured by Niumag Corporation Ltd., China with a resonance frequency of 21 MHz
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and a 10-mm probe. The temperature control system consisted of a compressor and a cooling bath to
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provide a minimum temperature limit of 208.15 K. During the experiments, the temperature was
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incremented at 0.1~2 K in each step with NMR signal measurements made over 10 mins to allow for
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thermal equilibration at every temperature point.
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Shale samples were also characterized by the nitrogen adsorption method (NAM), and the
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results were then compared to the NMR-C measurements to support the PSDs interpretation.
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NAM measurements were conducted on a 3H-2000PS2 Instrument at 77.4 K with pulverized shale
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samples.
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3.3 Modeling methods
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3.3.1 Pore structure modeling
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Pore structure modeling is a direct approach to acquire the geometry of the pore space and was
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employed here to characterize porosity and tortuosity.31 The first step is to segment the voids. This is
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achieved simply by using the thresholds from the gray scale image histograms from each slice
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followed by restacking the binarized three-dimensional images. Based on pore structure modeling,
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the equivalent pore diameter can also be recovered and is widely used in quantitative analysis
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defining the PSDs. The equivalent pore diameter can be given as:
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()* +
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Here, ()* is the equivalent pore diameter and 4567) is the summation of voxels of connected pores.
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Obviously, equation (3) describes a ball based equivalent pore diameter. However, one difficulty is
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in accurately separating the connected pores at the correct throat links. Commonly, the connected
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pores would not be divided in the pore structure modeling especially in the nano-pore structure of
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shales. And then voxels of two or more connected pores are treated as one whole pore in calculating
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the equivalent pore diameter with equation (3). This exaggerates the pore diameter. Therefore, pore
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structure modeling is not a good idea to quantitatively analyze the PSD in this study.
3
,-./01 2
(3)
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3.3.2 Pore network modeling (PNM)
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An alternative technique to calculate equivalent pore diameters is to use pore network
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modeling (PNM). This extracts a topologically representative network with idealized geometric
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properties derived from the binarized three-dimensional images. We use the PNM method of Dong
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and Blunt (2009),32 based on the maximal ball approach33 where spheres centered on each void
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voxel are expanded to fill the pore. Both inflating and deflating search algorithms32 are used to
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guarantee that the maximal ball is inscribed to the grain or image boundary. In principle, the larger
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spheres represent pores defined as the larger voids in the rock, whereas the chains of smaller spheres,
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connected by narrower pathways, define throats. Consequently, the PNM can distinguish pores and
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throats as well as separate connected pores. Thus the pore diameter recovered from the PNM is not
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overestimated, as it may be in pore structure modeling. The pore radius, throat radius, and pore
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throat length are measured as shown in Figure 5 using Euclidean distances. Moreover, the PNM
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provides other effective properties of pores and throats such as volume, shape factor, and
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coordination numbers of pores. Further important output parameters contribute to the analysis of
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nano-pore structure in shales.
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In this study, 2D micrographs were imaged by FE-SEM for the analysis of nano-pore structure
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in shales. 3D micrographs obtained from FIB-SEM were subsequently segmented into binary
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images. The segmentation of OM pores in the 3D micrographs were based on an Otsu thresholding
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algorithm34, while IntraP and InterP pores were segmented by an edge detection algorithm in Avizo
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(due to depth of field issues with the larger size pores resolved by FIB-SEM35,36). Both pore
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structure modeling and PNM were established for OM pores and IntraP/InterP pores in different part
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of shales.
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Figure 5. Illustration of the definitions of length and radius for pores and throats of PNM. 89 and 8: are the pore radii, while 8; is the radius of the connective throat. Terms
