Journal of Petroleum Science and Engineering 187 (2020) 106856
Contents lists available at ScienceDirect
Journal of Petroleum Science and Engineering journal homepage: http://www.elsevier.com/locate/petrol
Porosity characteristics of different lithofacies in marine shale: A case study of Neoproterozoic Sinian Doushantuo formation in Yichang area, China Wei Yang a, Sheng He a, *, Stefan Iglauer b, **, Xiaowen Guo a, Gangyi Zhai c, Zhi Zhou c, Tian Dong a, Ze Tao a, Sile Wei a a b c
Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education, China University of Geosciences, Wuhan, 430074, China School of Engineering, Edith Cowan University, 270 Joondalup Drive, Joondalup, Western Australia, 6027, Australia Oil & Gas Survey, China Geological Survey, Beijing, 100083, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Doushantuo Shale Yichang area Lithofacies classification Porosity development Controlling factors
This study evaluates the pore network systems in the Neoproterozoic Sinian Doushantuo (Z1d) shale from the Yichang area in the Middle Yangtze region. All samples were classified based on their lithologies and examined by geochemical and petrographic analysis, assisted with carbon dioxide/nitrogen (CO2/N2) adsorption, mercury injection capillary pressure (MICP) and focused ion beam-scanning electron microscopy (FIB-SEM) to fully characterize the nanoscale porosity of the different shale lithofacies. The calcareous shale lithofacies group (I-C), mixed shale lithofacies group (II-M) and siliceous shale lithofacies group (III-S) were identified via ternary mineralogy diagrams. Clearly, different lithofacies had different porosities, pore volume, surface area and poresize distribution (ranging from 0.3 nm to 36 μm). The mixed shale lithofacies group (II-M) had the highest total pore volumes, followed by the calcareous shale lithofacies group (I-C) and the siliceous shale lithofacies group (III-S). Peak values (2–20 nm, 20–50 nm, 50–60 nm and 90–200 nm) obtained from N2 adsorption were iden tified as organic matter (OM) pores, dissolution intraparticle (intraP) pores and intercrystalline pores (using SEM images analysis). Porosity shows a non-monotonic trend with TOC content and a maximum at 4.5–5.5 wt% TOC, similar relationships between TOC and mesopore/macropore volumes, due to pore collapse and compaction. The FIB-SEM images revealed solid bitumen and two types of kerogen in the examined shale samples; these different organic matter types had clearly distinct pore characteristics. While the smaller OM particles mobilized and converted into solid bitumen in which OM pores were larger and more abundant, the large-scale OM particles were most likely non-porous inert maceral and sponge-like porous amorphous kerogen. Overall TOC content and organic types are the key controlling factors to the nanoscale porosity development in the Doushantuo Shale, and the Sinian Doushantuo shale (in the Yichang area) has great potential for shale gas exploration and exploitation in South China.
1. Introduction Shales can not only serve as source rock but also provide a seal for unconventional oil and gas reservoirs. Nowadays, extensive and suc cessful application of horizontal drilling and hydraulic fracturing tech niques (Curtis, 2002; Jarvie et al., 2007; Bustin et al., 2008) contribute to low-cost exploitation from shale gas reservoirs. Shale gas reservoirs are commonly characterized by fined grains-complex, highly brittle mineral content with an organic-rich nanoscale pore networks system (Loucks et al., 2009, 2012; Curtis, 2002) and extremely low porosity and
permeability. Detailed studies on porosity and pore characteristics contribute to the success of US and China shale gas as the most signifi cant controlling factors in terms of shale gas storage capacity can be identified (Loucks and Ruppel, 2007; Milliken and Reed, 2010, 2013; Slatt and O’Brien, 2011; Bernard et al., 2012; Milliken and Curtis, 2016; Dong et al., 2017; Yang et al., 2016). While shale gas production is widely applied in the USA, Chinese shale gas development is still concentrated on individual blocks. For example, Fuling, China’s largest shale gas field, has produced more than 6 billion m3 of shale gas in 2018, ranking the gas field first in production
* Corresponding author. ** Corresponding author. E-mail addresses:
[email protected] (S. He),
[email protected] (S. Iglauer). https://doi.org/10.1016/j.petrol.2019.106856 Received 14 April 2019; Received in revised form 6 December 2019; Accepted 21 December 2019 Available online 24 December 2019 0920-4105/© 2019 Published by Elsevier B.V.
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Fig. 1. (a) Location map of the Yichang area with red square in the Mid-Yangtze area, South China. (b) Structure outline map and wells position of Yichang area includes Huangling anticline, Zigui Depression, Huaguoping-Sangzhi Fold Belt, Yichang Slope Belt and Dangyang Fold Belt. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
and sales in China. Fuling shale gas field cumulatively produced more than 21.5 billion m3 until the end of 2018. In 2019, the well 192-6HF of Jiaozuo-Pingqiao Block was released for testing at a high pressure of 25.60 MPa with 234,200 m3 per day of high-yield industrial airflow gas. China has continuously produced more than 100 million m3 of shale gas in its Changning, Huangjinba blocks until 2019; while until 2017 a total of 163 wells were put into operation in the Changning-Weiyuan area with a shale gas production of 2.473 billion m3. From 2018 to 2020, the number of shale gas wells that need to be newly put into operation in China needs to reach 1384. In addition, a major breakthrough has been made in the investigation of shale gas in western Hubei, this area is thus expected to become a new base for multilayers shale gas exploration and exploitation in South China (Zhai et al., 2017; Chen et al., 2018). The shale gas resource po tential evaluation shows that the shale gas geological resources in western Hubei Province amount to 11.68 trillion m3 with a resource base of 10 billion m3 of annual production capacity. Since 2014, wells ZD1, ZD2 and EYY1 in western Hubei of China have been consecutively drilled by the China Geological Survey (Zhai et al., 2017; Li et al., 2017; Wang et al., 2017; Chen et al., 2018, Fig. 1) to reveal the Sinian Doushantuo (Z1d) Shale, which is identified to be a prospective shale gas target layer due to its widespread burial thickness distribution, rela tively high TOC content, moderate thermal maturity and high brittle
minerals. The field analytical gas contents of Z1d shale in these three wells average 0.93 m3/t, 0.92 m3/t and 2.21 m3/t, respectively. Furthermore, the Sinian Doushantuo Shale, especially in well EYY1, has attracted considerable attentions and approximately 5460 m3/d shale gas was produced by vertical fracturing (Wang et al., 2017); it thus contributes to the development of multilayer shale reservoirs and also serves the economy in western Hubei. Here, aimed at this identified shale gas reservoir, a total of 45 typical Doushantuo core samples were selected from wells EYY1, ZD1 and ZD2 in Yichang area to: (1) investigate TOC content and lithofacies classification of shales; (2) characterize the porosity, pore volume and surface area, pore-size distribution and pore geometry on different lithofacies; and (3) clarify the controlling factors on porosity development. This study will thus promote the implementation of further industrial-scale shale gas production in China. 2. Geological setting Yichang area is conveniently located in the west of Hubei province in the Mid-Yangtze area, China. Yichang was identified and created as a 2
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Fig. 2. Simplified stratigraphic units of wells EYY1, ZD1 and ZD2 in the Yichang area, which include the Doushantuo and Nantuo Formations. The Doushantuo Formation is divided into four intervals: 1) Z1d1 shale, 2) Z1d2 shale, 3) Z1d3, and 4) Z1d4 shale. GR: gamma ray; RS: shallow resistivity log; RD: deep resistivity log. Arrows represent shale samples in these three wells with corresponding burial depth and all red arrows represent SEM samples. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
significant natural base for studying and prospecting unconventional oil and gas reserves (Fig. 1a). With the onset of the metamorphic crystalline basement, some significant tectonic events shaped the current geolog ical complexities including a stable period of the Yangtze Craton in Caledonian-Hercynian, then a tectonic deformation stage during the Indosinian and Yanshanian orogeny (Xu et al., 2013). Pre-Nanhua, Sinian (Z)-Nanhua, Cambrian (є), Ordovician (O) and Silurian (S) strata constitute a complete set of Neoproterozoic and Paleozoic strata in the Yichang area; the Sinian Doushantuo (Z1d) and Cambrian Niutitang (є1n) Shales are the main target formations. In the early Sinian period, large scale marine transgression occurred from east to west in the Yangtze platform because of the warming climate and melting glaciers, which eventually formed a typical stable platform shallow sea carbonate sedimentary facies (Liu et al., 2003; Yang et al., 2012; Zhang et al., 2016). Huangling anticline is surrounded by the Zigui Depression, the Huaguoping-Sangzhi Fold Belt, the Yichang Slope Belt and the Dangyang Fold Belt (Fig. 1b). Four dominant faults devel oped in the Yichang area, which mainly include the Xianniushan, Tia nyangping, Tongchenghe and Wuduhe Faults (Fig. 1b). Xianniushan and Tongchenghe Faults trend approximately NS, yet Tianyangping and Wuduhe Faults trend NW (Fig. 1b). Wells EYY1, ZD1 and ZD2 are located in the south of the Huangling anticline (Fig. 1b), and simplified stratigraphic units and sampling lo cations of these three wells are respectively shown in Fig. 2a–c. Well EYY1 is located in the middle of Tianyangping and Xianniushan Faults with current Doushantuo Shale burial from 3245 m to 3458 m; well ZD1 is close to Zigui city with current Doushantuo Shale burial from 634 m to
855 m, and well ZD2 is close to Yichang city with current Doushantuo Shale burying from 1235 m to 1445 m. The main target layer, Sinian Doushantuo (Z1d) Shale, can be divided into four intervals (Fig. 2): 1) Z1d1 is grey siliceous dolomite; 2) Z1d2 is composed of calcareous mudstone, dolomite and carbonaceous shale; 3) Z1d3 is grey dolomite; and 4) Z1d4 shale consists of black carbonaceous shale and siliceous shale. 3. Samples and analytical methods Fig. 2a–c illustrate the lithology, electrical characteristics and sample distributions of each well. We firstly select numerous shale samples based on the gamma ray (GR) values which correlate with TOC content (Zhao et al., 2016, 2017). After this screening, samples were selected in a way that they covered a large TOC range, and TOC was measured directly. Finally, we chose 45 typical shale samples with varying TOC content from three separate wells. Each shale sample was cut into several sub samples to investigate XRD, CO2 and N2 adsorption, MICP and FIB-SEM. Prior to analysis, all powdered and cubic shale samples were totally oven-dried at a temperature of 100 � C for 48 h, then cooled to room temperature in a desiccator in occlusive environment to ensure the integrity during the drying process, and these powdered shale samples, approximately 1–2 g, will be oven-dried and degassed in a vacuum tube wrapped by Heating mantle at 110 � C for 480 min in Outgas stations in the gas adsorption experiments.
3
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Table 1 Depth, TOC, porosity, normalized mineral composition and lithofacies for the 45 Sinian Doushantuo shale samples tested. Sample
Depth (m)
TOC (wt.%)
Porosity (%)
Mineral composition (%)
Lithofacies
Siliceous
Carbonate
Clay
EYY1-01 EYY1-02 EYY1-05 EYY1-08 EYY1-10 EYY1-13 EYY1-15 EYY1-17 EYY1-22 EYY1-24 EYY1-25 EYY1-27 EYY1-29 EYY1-31 EYY1-33 EYY1-36 Average
3309.85 3327.50 3334.50 3348.46 3356.62 3366.51 3375.84 3381.49 3395.01 3403.99 3409.49 3417.74 3424.44 3431.99 3436.04 3444.29
0.90 3.40 0.50 4.45 1.20 2.33 1.64 0.60 2.22 1.50 2.18 0.82 2.42 1.08 1.27 9.20 2.23
1.79 4.07 1.42 2.22 0.73 2.93 0.36 1.10 3.20 2.16 2.19 0.36 2.52 1.42 1.79 1.57 1.86
39.82 37.95 21.03 39.65 25.22 26.66 37.47 31.14 35.27 22.88 40.50 22.20 37.25 24.14 31.17 78.55 34.43
58.16 40.04 71.48 34.24 57.91 39.53 58.35 49.40 49.34 53.24 51.55 46.22 40.66 60.61 65.29 1.33 48.58
2.02 22.02 7.49 26.11 16.87 33.81 4.18 19.46 15.39 23.88 7.95 31.58 22.09 15.25 3.53 20.11 16.98
C-1 M-1 C-2 M C-1 M C-1 M-1 M-1 C-2 C-1 M-3 M-1 C-2 C-1 S /
ZD1-60 ZD1-58 ZD1-55 ZD1-50 ZD1-49 ZD1-43 ZD1-39 ZD1-30 ZD1-27 ZD1-21 ZD1-15 ZD1-12 ZD1-08 ZD1-06 Average
634.52 636.82 728.46 741.26 748.86 756.06 766.96 778.00 785.69 792.09 799.94 805.00 819.60 825.59
10.73 9.95 1.89 2.62 1.63 0.85 5.67 0.69 1.54 2.37 0.48 0.78 1.78 1.76 3.05
3.20 4.03 3.96 6.45 4.73 3.25 5.36 3.20 4.51 3.91 1.79 2.91 3.58 6.36 4.09
88.89 72.16 25.25 37.76 39.80 18.37 56.25 25.51 15.31 13.13 36.00 13.13 42.27 44.90 37.77
5.05 16.49 40.40 40.82 36.73 40.82 14.58 41.84 37.76 60.61 44.00 53.54 22.68 15.31 33.62
6.06 11.34 34.34 21.43 23.47 40.82 29.17 32.65 46.94 26.26 20.00 33.33 35.05 39.80 28.62
S S-2 M M-1 M-1 M-3 S-3 M M-3 C-3 M-1 C-3 M-2 M-2 /
Z2-27 ZD2-35 ZD2-39 Z2-30 ZD2-47 Z2-31 ZD2-56 ZD2-59 ZD2-63 ZD2-86 ZD2-66 ZD2-72 ZD2-79 ZD2-82 Z2-35 Average
1238.40 1330.76 1338.40 1346.00 1351.01 1365.20 1376.20 1381.43 1390.95 1394.6 1402.6 1410.05 1426.61 1432.51 1433.61
4.65 1.02 1.81 1.29 1.58 2.50 0.35 1.74 1.57 3.39 1.25 4.04 0.39 1.90 4.42 2.13
5.75 3.79 3.27 3.66 4.09 3.35 3.05 4.14 3.99 4.15 3.58 5.64 3.93 5.42 5.84 4.24
66.28 15.82 18.93 19.18 28.51 16.82 15.60 13.13 19.40 22.74 15.43 12.35 16.96 48.51 37.69 24.49
19.94 64.23 49.66 51.61 39.04 34.86 61.87 43.58 54.21 47.59 58.13 44.54 47.50 3.47 21.17 42.76
13.77 19.95 31.41 29.21 32.45 48.32 22.53 43.29 26.39 29.67 26.45 43.11 35.55 48.02 41.14 32.75
S-2 C-2 M C-3 M M-3 C-2 M-3 C-3 M-3 C-3 M-3 M-3 M-2 M-2 /
3.1. TOC content and mineralogy
3.2. Porosity
Total organic carbon (TOC, wt.%) was measured on 45 powdered samples with particle size < 75 μm by a CS230 analyzer following (GB/ T) 19145-2003. Prior to the TOC experiment, HCl treatment was used to remove inorganic carbon for 72 h with 80 � C, and we cleaned ash cru cible timely then residual organic matters were combusted at approxi mately 930 � C with gas flow rate and pressure at approximately 800 mL/ min (�25) and 1050–1150 mbar to keep the leak test passed. 3 blank, 2 random and 2 soil (real TOC ¼ 1.55 wt%) samples are needed before testing to consume the oxygen and calibrate the TOC content. Then Xray diffraction analysis was also performed on 45 powdered samples with particle size <75 μm using a X’ Pert PRO DY2198 X-ray diffrac tometer following (SY/T)5163-2010. Powdered samples were tiled on a glass panel and then X-Ray scanned with 1 min step time and 0.001� 2θ step size at room temperature (25 � C) and a relative humidity (RH) of 65% to obtain the spectra.
The total porosity of the shale samples was determined at the China Petroleum and Chemical Corporation in Yangzhou city, China using a technique based on Boyle’s Law, by measuring the grain density (ρg) under ambient conditions and bulk density (ρb) by Archimedes’ princi ple with helium pycnometry (Manger, 1963). Samples were oven dried at 115 � C to a constant weight (�1 mg). The porosity (Φ) was calculated using bulk density (ρb) and grain density (ρg): � � ρb Φ¼ 1 � 100%
ρg
3.3. CO2 and N2 adsorption experiments We conducted CO2 and N2 adsorption with the same particle size (from 180 μm to 250 μm) on the same Quantachrome Autosorb iQ surface area analyzer with different detectors (P0 cell for N2 adsorption and another detector for CO2 adsorption for different temperatures). 4
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Firstly, 1–2 g of sample was degassed in a vacuum tube wrapped by Heating mantle at 110 � C for 480 min in outgas stations, then 2 samples were installed in sample stations one for the analysis. CO2 adsorption was employed to characterize nanopores (ranging from 0.3 nm to 1.48 nm size), based on non-local density functional (NLDFT) method with P/ P0 ranging from 0 to 0.032 under 0 � C working temperature. The N2 adsorption was employed to quantify pore-size distribution from 0.9 nm to approximately 320 nm at P/P0 ranging from 0.006 to 0.994. BET surface area (m2/g) was calculated with P/P0 ranging from 0.05 to 0.30, and the pore volume/surface areas and pore-size distributions (PSD) were calculated via the Barrett-Joyner-Halenda (BJH) method (Gregg and Sing, 1982) with large pore diameter ranges (ranging from 0.9 nm to 320 nm). Note that the total pore volume is determined when P/P0 is approximately at 1.0 at a working temperature of 196.15 � C. 3.4. Mercury injection capillary pressure (MICP) Mercury injection capillary pressure (MICP) experiments were con ducted on seven cubic samples (EYY1-02, EYY1-13, ZD1-50, ZD1-27, ZD2-79, ZD2-66 and ZD2-63; 1 � 1 � 1 cm3 volume) using a Micro meritics AutoPore IV 9510 Mercury Injection Porosimeter. This effective method can detect pore-throat diameters as small as 3 nm under a maximum pressure of 413 MPa (Gao and Hu, 2013). The forced intru sion of liquid mercury between particles and into pores as a function of applied pressure is routinely employed to characterize a wide range of particulate and solid materials. The pore-throat diameter was calculated by the Washburn equation (Washburn, 1921; Webb, 2001) with surface tension of 485 dyne/cm and contact angle of 130� , respectively (Gan et al., 1972; Gregg and Sing, 1982).
Fig. 3. Mineralogical ternary plot for all Sinian Doushantuo Shale samples selected from wells EYY1, ZD1 and ZD2. A detailed lithofacies classification can be found in articles written by Chen et al. (2016). All these primary types can be sorted into three main shale lithofacies groups, which include the calcareous shale lithofacies group (I-C), mixed shale lithofacies group (II-M) and siliceous shale lithofacies group (III-S). The red dotted lines represent unit boundary of lithofacies groups. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
respectively, ranging from 0.50 wt% to 9.20 wt% (EYY1), from 0.48 wt % to 10.73 wt% (ZD1) and from 0.35 wt% to 4.65 wt% (ZD2) in absolute terms (Table 1). TOC contents of Z1d1 and Z1d4 samples were much higher than samples from Z1d2 shale. The porosity, calculated by bulk density (ρb) and grain density (ρg), of shale samples from wells EYY1, ZD1 and ZD2 averaged 1.86%, 4.09% and 4.24%, respectively (Table 1). Porosity of samples from wells ZD1 and ZD2 were much larger than those of samples from well EYY1 due to the distinct burial depth of the Doushantuo Shale. Furthermore, the normalized siliceous mineral con tents of wells EYY1, ZD1 and ZD2 averaged 34.43%, 37.77% and 24.49%, respectively, with corresponding carbonate contents of 48.58%, 33.62% and 42.76%, respectively (Table 1). Finally, the normalized clay mineral contents of these three wells averaged 16.98%, 28.62% and 32.75%, respectively (Table 1). Inconsistent criteria and classification methods have been currently proposed for shale lithofacies in the Barnett, Marcellus, Horn River and Wufeng-Longmaxi Shales (Loucks and Ruppel, 2007; Wang and Carr, 2013; Diaz et al., 2013; Dong et al., 2015; Wu et al., 2018; Chen et al., 2016; Peng et al., 2019). However, the classification which is based on the three main mineral compositions (Wu et al., 2018; Chen et al., 2016), is an effective method to identify the favorable shale gas areas. Applying the ternary diagram minerals analysis to wells EYY1, ZD1 and ZD2 Doushantuo Shale samples, ten primary types can be identified, Fig. 3 and Table 1. All these primary types can be described as three main shale lithofacies groups as follows (Fig. 3); namely 1) calcareous shale lithofacies group (I-C), 2) mixed shale lithofacies group (II-M), and 3) siliceous shale lithofacies group (III-S). Most of shale samples are found in I-C and II-M areas.
3.5. Focused ion beam-scanning electron microscopy (FIB-SEM) 14 shale samples (marked with red arrows in Fig. 2) were selected due to their TOC content, and cut into cuboid samples (10 mm in length, 5 mm in width and 1.5 mm in height). Firstly, all 14 samples were mechanically polished by a Leica EM TXP instrument and then prepared by Ar-ion-milling using a Leica TIC 3X without gold coating to observe the nanopore morphology, and abundance of minerals using a highresolution Zeiss Merlin FIB-SEM. High resolution field emission scan ning electron microscope imaging and focused ion beam testing were used for mineral composition, OM and micro-nanometer pore network system analysis; these high precision rock imaging methods can achieve working distances ranging from 2.3 mm to 10.8 mm. FIB-SEM was also equipped with an In-lens secondary electron detector which can provide greatly increased details of nanoscale OM pores. Energy dispersive spectroscopy (EDS, X-Flash 6130 of Bruker) was used to identify the mineral composition by elemental analysis. All images were captured in SE2 model and InLens model. L and W in the SEM images mean length and width in Part 4.3. All pore parameters including Area, Perimeter, length (long axis) and width (short axis) were obtained from the Image-Pro Plus 6.0 software. The pores were assumed to have quasi circular shapes. The pore diam eter presents then the equivalent circle diameter D via: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi D ¼ 4S=3:14 where S is the pore area. All pore diameters data were calculated by this equation.
4.1.1. Calcareous shale lithofacies group (I-C) Fifteen samples fell into this group with an average TOC content of 1.38 wt%. Siliceous, carbonate and clay minerals content averaged 23.59%, 17.69% and 58.72%, respectively. I-C was thus divided into three sub-types (Fig. 3), namely siliceous-rich calcareous shale lith ofacies (C-1), argillaceous/siliceous mixed calcareous shale lithofacies (C-2) and argillaceous-rich calcareous shale lithofacies (C-3). The average siliceous minerals contents of C-1, C-2 and C-3 were 34.84%,
4. Results 4.1. TOC content, porosity and lithofacies classification TOC content and normalized mineral composition of all shale sam ples are displayed in Table 1. TOC content of the 45 Z1d shale samples in wells EYY1, ZD1 and ZD2 averaged 2.23 wt%, 3.05 wt% and 2.13 wt%, 5
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Fig. 4. CO2 adsorption isotherms measured for ten primary shale lithofacies.
Fig. 5. Typical N2 adsorption isotherms for ten primary shale lithofacies.
19.89% and 16.05%, respectively, with the corresponding average carbonate values of 58.25%, 62.29% and 55.62%. Clay mineral contents of these three sub-types averaged 6.91%, 17.82% and 28.33%, respectively. 4.1.2. Mixed shale lithofacies group (II-M) Twenty-five samples fell into the II-M subgroup, with an average TOC content of 2.27 wt%. The siliceous, carbonate and clay mineral contents averaged 29.21%, 38.05% and 32.72%, respectively. Four lithofacies sub-types, mixed shale lithofacies (M), carbonate/siliceous mixed shale lithofacies (M-1), argillaceous/siliceous mixed shale lith ofacies (M-2) and argillaceous/carbonate mixed shale lithofacies (M-3), were identified, Fig. 3. The average siliceous mineral contents of these four sub-types were 27.42%, 36.45%, 43.34% and 17.23%, respectively, with the corresponding carbonate averages of 40.79%, 43.00%, 15.66% and 42.86%, respectively. The clay mineral contents of these four subtypes averaged 31.79%, 20.55%, 41.00% and 39.91%, respectively. 4.1.3. Siliceous shale lithofacies group (III-S) Only five samples fell into this group with an average TOC content of 8.04 wt%. Siliceous, carbonate and clay mineral contents averaged 72.43%, 11.48% and 16.09%, respectively. III-S was further divided into three sub-types including siliceous shale lithofacies (S), argillaceous/ carbonate mixed siliceous shale lithofacies (S-2) and argillaceous-rich siliceous shale lithofacies (S-3), Fig. 3.
Fig. 6. Cumulative intruded Hg volume versus mercury pressure for the seven Doushantuo shale samples with four primary lithofacies tested.
adsorbent-adsorbate interactions (Thommes et al., 2015). Then at a relatively higher P/P0 range, isotherm shapes are of type II indicating molecular clustering and pore filling. Yet the isotherm shape of sample EYY1-36 (S, TOC ¼ 9.20 wt%) is clearly different and indicates rela tively more mesopores in this sample (Fig. 5). In addition, N2 adsorption volumes of samples ZD1-58 and EYY1-36 are much smaller than those for sample ZD2-72 (M-3, TOC ¼ 4.04 wt%), in contrast to CO2 adsorp tion data in Fig. 4. Such hysteresis loops as for sample EYY1-36 resemble H4 and H4 loops which are often found in micro-mesoporous carbons (Thommes et al., 2015). The more pronounced uptake at low P/P0 is related to the filling of micropores, yet hysteresis loops of the other shale samples resemble H2(b) and H3 mixture types. The H2(b) loop is asso ciated with pore blocking and observed in certain mesoporous structures (Thommes et al., 2015), but the pore-throat size distribution is much broader, while the H3 loop is observed in some non-rigid aggregates of plate-like clay particles but also if the pore network system consists of macropore, which are not completely filled with pore condensate. MICP experiments are also an effective and practical technique to describe pore-throat size distributions as this technique can provide quantitative information on pore characteristics. Fig. 6 shows the plots of the applied pressure versus cumulative Hg intrusion for the Doush antuo shale samples obtained from MICP experiments. The intrusion-
4.2. Pore characteristics for different lithofacies 4.2.1. Adsorption isotherm characteristics Adsorption of CO2 has become an acceptable technique for describing carbonaceous materials with very narrow micropore network (Thommes et al., 2015). Fig. 4 reveals similar physisorption isotherms on different lithofacies; these are now defined as type I(b), indicative of a relatively broader pore-size range including wider micropores and possibly narrower mesopores in the carbonaceous shales (Thommes et al., 2015). Sample ZD1-58 (S-2, TOC ¼ 9.95 wt%) displays the highest CO2 adsorption volume, followed by sample EYY1-36 (S, TOC ¼ 9.20 wt %); whereas sample EYY1-33 (C-1, TOC ¼ 1.27 wt%) has the lowest adsorption volume, indicating that III-S samples generally have higher adsorption volumes, indeed much higher than I-C samples. Types II and V isotherms have been measured (using N2 adsorption; Thommes et al., 2015) on different lithofacies indicating that pores in shale are more complicated than often expected (Fig. 5). In the low P/P0 range, isotherm shapes are of type V which indicates relatively weak 6
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Table 2 Lithofacies, basic properties and pore parameters for all Doushantuo shale samples. Sample ID
Litho-facies
TOC wt.%
Porosity (%)
CO2 adsorption Vmic 10
EYY1-01 EYY1-10 EYY1-15 EYY1-25 EYY1-33 ZD2-35 ZD2-56 EYY1-05 EYY1-24 EYY1-31 ZD1-21 ZD1-12 Z2-30 ZD2-63 ZD2-66 ZD1-55 ZD1-30 ZD2-39 ZD2-47 EYY1-08 EYY1-13 ZD1-50 ZD1-49 ZD1-15 EYY1-02 EYY1-17 EYY1-22 EYY1-29 ZD1-08 ZD1-06 ZD2-82 Z2-35 ZD1-43 ZD1-27 Z2-31 ZD2-59 ZD2-86 ZD2-72 ZD2-79 EYY1-27 ZD1-60 EYY1-36 ZD1-58 Z2-27 ZD1-39
C-1
0.90 1.20 1.64 2.18 1.27 1.02 0.35 0.50 1.50 1.08 2.37 0.78 1.29 1.57 1.25 1.89 0.69 1.81 1.58 4.45 2.33 2.62 1.63 0.48 3.40 0.60 2.22 2.42 1.78 1.76 1.90 4.42 0.85 1.54 2.50 1.74 3.39 4.04 0.39 0.82 10.73 9.20 9.95 4.65 5.67
C-2
C-3
M
M-1
M-2
M-3
S S-2 S-3
0.36 0.73 1.79 1.79 2.19 1.42 1.42 2.16 3.05 3.79 2.91 3.58 3.66 3.91 3.99 2.22 2.93 3.20 3.27 3.96 4.09 1.10 1.79 2.52 3.20 4.07 4.73 6.45 3.58 5.42 5.84 6.36 0.36 3.25 3.35 3.93 4.14 4.15 4.51 5.64 1.57 3.20 4.03 5.75 5.36
3
3
cm /g
2.09 2.34 2.85 4.10 3.06 3.46 2.29 1.93 2.96 2.88 4.47 3.08 4.17 4.27 3.85 4.93 2.78 4.96 4.55 8.89 4.25 5.97 4.57 2.65 6.19 3.05 4.38 4.85 4.98 5.14 4.90 7.07 3.39 4.96 5.51 4.75 6.89 6.37 2.90 2.98 13.70 11.51 12.49 7.54 8.21
N2 adsorption 10
3
cm3/g
Smic
SBET
2
m /g
2
m /g
Vmic
Vmes
Vmac
VBJH
6.96 7.69 9.17 12.48 9.33 11.16 7.72 6.22 9.35 9.66 14.72 10.18 12.53 13.70 12.11 16.67 8.45 16.24 15.09 26.36 13.53 18.61 14.47 8.84 19.91 9.49 11.66 15.11 16.96 17.60 15.80 24.09 10.70 15.90 16.04 16.32 19.48 21.06 9.79 9.28 44.32 36.62 40.95 24.10 26.55
5.13 6.52 7.88 11.40 10.38 5.82 4.91 3.69 5.37 10.76 10.32 5.13 7.66 7.99 6.6 8.19 7.27 9.24 6.11 11.79 9.36 12.33 10.05 4.37 10.37 8.50 4.80 10.01 11.00 11.45 13.29 23.11 7.42 7.43 6.48 8.79 10.29 11.70 7.48 5.17 4.78 25.93 1.19 26.07 5.32
13.94 16.27 19.48 14.02 11.35 30.89 11.37 11.33 17.75 12.72 19.41 23.31 24.89 24.16 24.09 23.08 18.32 24.99 24.99 22.13 12.09 24.99 20.93 14.16 17.79 15.71 16.59 12.55 23.25 26.01 28.97 67.83 21.35 30.00 28.21 15.67 50.9 48.34 21.86 22.63 13.22 23.17 8.64 20.94 16.87
5.64 7.58 6.91 6.12 5.42 10.58 7.09 5.47 8.81 6.83 8.46 9.81 11.58 11.98 9.99 11.91 9.30 11.77 10.75 11.86 5.84 11.92 10.23 6.43 10.33 8.27 8.10 5.03 10.93 12.77 17.39 28.10 10.88 11.28 11.22 9.98 15.38 17.10 9.94 10.09 6.07 10.15 2.28 8.73 6.31
7.22 7.34 10.79 4.57 3.01 19.53 3.44 5.26 8.26 3.02 8.33 12.9 12.06 10.74 12.87 9.83 7.52 11.39 13.41 7.73 3.75 10.25 8.36 7.05 5.19 5.42 7.93 4.62 9.83 10.79 8.98 35.24 9.30 17.54 15.34 3.98 33.78 29.25 10.54 12.09 6.30 5.30 6.38 3.96 9.69
13.90 16.41 19.54 13.52 11.31 30.86 11.39 11.39 18.06 12.64 19.00 23.58 24.81 24.13 24.00 23.31 18.47 24.82 25.05 22.08 11.80 24.61 20.60 14.36 17.56 15.47 16.92 11.63 23.00 25.83 29.28 68.14 21.59 30.00 27.51 15.34 50.78 48.20 22.15 22.99 13.25 19.80 8.99 18.12 17.14
Note: Vmic and Smic are micropore volumes and surface areas calculated by CO2 adsorption; SBET represents surface area determined by Multi-Point BET; Vtotal is the total pore volume; Vmes, Vmac and VBJH are mesopore, macropore and pore volumes obtained from N2 adsorption data using the Barrette Joynere Halenda (BJH) method.
Table 3 Average pore structure parameters of ten primary lithofacies (calculated from Table 2) for the Doushantuo shales. Lithofacies I-C
II-M
III-S
TOC C-1 C-2 C-3 average M M-1 M-2 M-3 average S S-2 S-3 average
Porosity
Vmic 3
Smic
(wt.%)
(%)
10
1.76 0.93 1.45 1.38 2.50 2.14 2.47 1.96 2.29 9.97 7.30 5.67 8.04
1.37 2.37 3.61 2.45 3.28 3.41 5.30 3.67 3.28 5.36 4.89 2.39 3.41
2.89 2.70 3.97 3.19 5.06 4.52 5.52 4.72 5.06 12.61 10.02 8.21 10.69
3
SBET 2
cm /g 9.13 8.82 12.65 10.20 16.06 14.01 18.61 14.82 16.20 40.47 32.53 26.55 34.51
7
Vtotal 3
m /g
10
8.26 6.11 7.54 7.30 8.66 8.63 14.71 8.10 10.09 15.36 13.63 5.32 12.66
15.01 16.81 23.17 18.33 20.93 17.53 36.52 29.87 26.91 18.20 14.79 16.87 16.57
Vmeo
Vmac
VBJH
6.33 7.76 10.36 8.15 10.24 8.62 17.30 11.98 12.32 8.11 5.51 6.31 6.71
6.59 7.90 11.38 8.62 8.94 6.97 16.21 16.48 12.57 5.80 5.17 9.69 6.33
14.94 16.87 23.10 18.30 20.92 17.31 36.56 29.82 26.85 16.53 13.56 17.14 15.46
3
cm /g
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Table 4 Pore structure parameters obtained from MICP for the Doushantuo Shale samples. Sample ID
Lf
ρb
ρs
Φ-Hg
Φ
Vtotal
Stotal
Dv
Ds
Da
ZD2-79 ZD2-66 ZD1-27 ZD2-63 EYY1-13 ZD1-50 EYY1-02
M-3 C-3 M-3 C-3 M M-1 M-1
2.6003 2.6358 2.6869 2.6026 2.6914 2.5292 2.6391
2.6847 2.7096 2.7980 2.7017 2.7125 2.6201 2.6959
3.15 2.72 3.97 3.67 0.78 3.47 2.11
3.93 3.58 4.51 3.99 2.93 6.45 4.07
1.21 1.03 1.48 1.41 0.29 1.37 0.80
5.75 3.96 4.87 6.02 1.24 7.61 3.61
8.73 11.08 14.72 9.45 10.21 7.31 10.20
5.64 7.52 8.65 7.43 4.54 4.99 4.53
8.41 10.45 12.14 9.37 9.33 7.21 8.84
Note: Lf indicates the lithofacies, ρb and ρs are bulk and skeletal density, g/mL; Φ-Hg is the Hg porosity, %; Vtoatl and Stotal are the total pore volume and area, cm3/100 g and m2/g; Dv and Ds are median pore diameter calculated by volume and area, nm; Da is the average pore diameter calculated by 4 V/A, nm. Φ is the total porosity in Table 1, (in contrast to Φ-Hg).
areas averaged 7.30 m2/g, 10.03 m2/g and 11.44 m2/g, respectively (Table 3), calculated from N2 adsorption data. Table 4 shows the main pore structure parameters obtained from the MICP method for seven Doushantuo Shale samples. The bulk density and skeletal density range from 2.5292 g/mL to 2.6914 g/mL and from 2.6201 g/mL to 2.7980 g/mL, respectively. The Hg-porosity averaged 2.84%, ranging from 0.78% to 3.97%, which is smaller than the total porosity (Φ) due to the detection limit of MICP method for nanoscale pores < 3 nm. The total pore volumes averaged 1.08 mL/100 g, ranging from 0.29 mL/100 g to 1.48 mL/100 g. Compared with the pore volumes and surface areas results from gas adsorption (Tables 2 and 3), total pore volumes and areas from MICP are smaller, due to the larger range detection limit of MICP method for nanoscale pores, as well as wider measurement range to approximately 36 μm. Median pore diameters calculated by volume are larger than the corresponding areal pore di ameters, similar value to average pore diameter (4 V/A) in MICP method.
extrusion curves show different hysteresis between the two branches at applied pressures ranging from 0.034 to 413 MPa for all shale samples (Fig. 6). This indicates that a significant amount of mercury remains inside parts of the mesopores after extrusion, indicating large pore space, ink-bottle type connected, with narrow pore-throat size distri bution and more complex pore networks. Samples ZD1-27 (M-3, TOC ¼ 1.54 wt%), ZD2-63 (C-3, TOC ¼ 1.57 wt%) and ZD1-50 (M-1, TOC ¼ 1.54 wt%) have relatively larger hysteresis, followed by samples ZD2-79 (M-3, TOC ¼ 0.39 wt%), ZD2-66 (C-3, TOC ¼ 1.25 wt%) and EYY1-02 (M-1, TOC ¼ 3.40 wt%), yet sample EYY1-13 (M, TOC ¼ 2.33 wt%) has the smallest hysteresis (Fig. 6). A significant volume of mercury intrudes into the connected pores in the Doushantuo Shale samples with increasing applied mercury pressure, suggesting that the pore-throat size of some large pores are in the meso-macropore range, even in the micrometer range. 4.2.2. Pore volume and surface area Pore structure parameters of ten primary shale types (45 shale samples in total) are displayed in Table 2; the average value of each parameter for each primary lithofacies is shown in Table 3. Different shale lithofacies had significantly varying pore volumes and surface areas. Micropore volumes of I-C, II-M and III-S samples averaged 3.19 � 10 3 cm3/g, 5.06 � 10 3 cm3/g and 10.69 � 10 3 cm3/g, respectively, with the corresponding average micropore surface areas of 10.20 m2/g, 16.20 m2/g and 34.51 m2/g, respectively, calculated from CO2 adsorption. Mesopore volumes of I-C, II-M and III-S samples average 8.15 � 10 3 cm3/g, 12.32 � 10 3 cm3/g and 6.71 � 10 3 cm3/g, respectively, with the corresponding average macropore volumes of 8.62 � 10 3 cm3/g, 12.57 � 10 3 cm3/g and 6.33 � 10 3 cm3/g, respectively, calculated from N2 adsorption data (BJH method). The total pore volumes of I-C, II-M and III-S samples averaged 18.33 � 10 3 cm3/g, 26.91 � 10 3 cm3/g and 16.57 � 10 3 cm3/g, respectively, similar to the BJH pore volumes. Finally, the corresponding BET surface
4.2.3. Pore-size distribution characteristics Multimodal pore-size distributions of ten primary shale lithofacies obtained from gas adsorption data are displayed in Fig. 7 with several volumetric maxima, which indicate similar micro-macro pore-size dis tribution characteristics for different lithofacies. CO2 adsorption data can precisely reflect micropore size distribution ranging from 0.3 nm to 0.9 nm and N2 adsorption data can also reflect wider pore-size distri butions ranging from 0.9 nm to 320 nm precisely (Fig. 7). MICP method can characterize a wider pore-size distribution from circa 3 nm to 36 μm and unimodal pore-throat size distributions are clearly observed within matrix pore-throat diameters <45 nm (Fig. 8a); while Fig. 8b also shows multimodal pore-throat size distributions from 50 nm to 36 μm for the Doushantuo samples. Fig. 8 indicates that the total pore volumes are dominated by mesopores (3–50 nm). Several volumetric maxima in the pore-throat size distribution (e.g. Figs. 7 and 8) can represent different
Fig. 7. Pore-size distributions for ten primary shale lithofacies. CO2 and N2 adsorption data can accurately measure the distributions from 0.3 nm to 0.9 nm and from 0.9 nm to 320 nm, respectively. 8
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
pore types, this is discussed below. 4.3. Pore types and morphologies identified in the main lithofacies The observations of nanoscale pores under SEM are key to interpret the occurrence of gas in shales, and the pore types and morphologies in different lithofacies can vary vastly. In this paper, based on SEM anal ysis, pore types of Z1d shale can be first divided into two types; namely organic matter (OM) pores and inorganic pores, where inorganic pores are dominated by intraparticle (intraP) pores. Pore types and morphologies at nm-μm scale and mineral composi tion of I-C samples can be directly observed by FIB-SEM analysis (Fig. 9), and OM particles are relatively rare in these shale samples. Few OM particles can be observed between pyrites and dolomite or are sur rounded by pyrites (in Fig. 9a with 1.46 μm length and 0.86 μm width, but they even can reach to about 3.65 μm length and 0.98 μm width in sample EYY-33 (C-1)). Pore sizes of OM pores in larger particles (3.65 μm length and 0.98 μm width) are much smaller than pores in small particles (Fig. 9a and b), which indicate that large scale OM particles can be compacted easily. Dissolution intraparticle (intraP) pores developed well in pyrites and some intraparticle (intraP) pores were filled with bitumen, such as the pores in the top left corner in Fig. 9a and b. The small OM particle (0.19 μm length and 0.79 μm) in Fig. 9a and b con tained more alveolate OM pores with relatively larger pore diameter,
Fig. 8. Pore-throat size distributions ranging from (a) 3 nm to 36 μm, and (b) diameters > 50 nm; for seven Doushantuo shale samples including four primary lithofacies.
Fig. 9. OM pore and inorganic pore characteristics identified in I-C samples. a, b and c represent sample EYY-33 (C-1) with a TOC content of 1.27 wt%; d, e and f represent sample EYY-31 (C-2) with a TOC content of 1.08 wt%; g, h and i represent sample ZD1-21 (C-3) with a TOC content of 2.37 wt%. a): Large OM particles and pyrites are surrounded by dolomite, in which OM pores and intraparticle (intraP) pores developed. b): Partial amplification of a) contains a lot of OM pores and intraparticle (IntraP) pores, and OM can be found in intraparticle (IntraP) pores. c): Intraparticle (IntraP) pores are commonly observed in dolomite minerals. d): Large OM particle shows no organic matter pores. e): A small OM particle contains abundant pores with maximum pore diameters of 104 nm. f): Intraparticle (IntraP) pores can reach 439 nm in diameter in dolomite. g): a lot of OM particles dispersed sponge-like in abundant OM pores. h): OM inside the pyrite contains a lot of pores and the largest long axis can reach c. 668 nm. i): Lots of intraparticle (intraP) pores with large pore size can be observed between clays. 9
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Fig. 10. Typical FIB-SEM images about pore characteristics on II-M samples. a, b and c represent sample Z1-30 (M) with a TOC content of 0.69 wt%; d, e and f represent sample EYY-17 (M-1) with a TOC content of 0.60 wt%; g, h and i represent sample ZD2-86 (M-3) with a TOC content of 3.39 wt%. a): A lot of pyrites and long striped OM particle (31.47 μm in length and 1.54 μm in width) were displayed; b): Partial amplification of a) contains lots of sponge-like OM pores with maxpore diameter of 46 nm. c): Small OM particle protected by brittle minerals contains lots of large OM pores. d): Large spaces between brittle minerals were filled with OM particles. e): Partial amplification of d) contains lots of sponge-like OM pores, which can reach about 57 nm. f): Intraparticle (IntraP) pores can be easily observed in dolomite minerals with large pore size. g): Large OM particle with 36.76 μm in length was compacted by minerals. h): Partial amplification of g) shows no OM pores but a fracture; i): Lots of large intraparticle (intraP) pores developed well in clays.
which reached 116 nm. The pore diameter of intraparticle (intraP) pores in pyrites approximately ranged from 10 s nm to 270 nm (Fig. 9b). In addition, irregularly dissolution intraparticle (intraP) pores were commonly observed in dolomite (Fig. 9c) with pore diameters ranging from 23 nm to 413 nm and the minimum pore-size was smaller than 23 nm. No OM pores were identified in sample EYY-31 (C-2) with large OM particle (38.37 μm in length and 11.04 μm in width, Fig. 9d). Intra particle (IntraP) pores were also commonly observed in dolomite with maximum pore diameters of 439 nm (Fig. 9f); these were much larger than OM pores in small OM particles (Fig. 9e, ranging from several nm to 104 nm). Fig. 9g shows a lot of OM particles dispersed abundantly in sponge-like OM pores. Furthermore, the solid bitumen inside the pyrite framboid contained a lot of OM pores, characterized by rod-like and round shapes in sample ZD1-21 (C-3) (Fig. 9g), where the long OM pores axis were about 668 nm. Abundant intraparticle (intraP) pores were also observed between clays and dolomite minerals with relatively large pore sizes ranging from 10 s nm to 858 nm (Fig. 9i). Similarly, in I-C samples, large elongated OM particles (Fig. 10a, 31.47 μm in length and 1.54 μm in width) were present, while II-M samples contained smaller sponge-like OM pores (Fig. 10b; with maximal diameters of 46 nm in sample Z1-31 (M)). In addition, large pores (50 nm–274 nm) were observed in small solid bituminous particles (Fig. 10c), characterized by elliptical shapes. Several large OM particles
of different size existed between the mineral grains, such as the one with 5.08 μm length and 2.56 μm width (Fig. 10d) in sample EYY-17 (M-1), in which sponge-like OM pores were predominant with relatively homo geneous pore sizes ranging from 20 nm to 57 nm (Fig. 10e). Dissolution intraparticle (intraP) pores in dolomite were also easily observed with diameters ranging from 10 s nm to 543 nm (Fig. 10f) with rectangular shapes. Sample ZD2-86 (M-3) had only few OM pores, but more intra particle (intraP) pores. While large OM particles contained few or no pores (Fig. 10g and h), a fracture in OM was observed (5.48 μm in length with a 57 nm aperture). A lot of rod-like intraparticle (intraP) pores developed in clays (Fig. 10i); these were much larger than OM pores, with long axis diameters ranging from 95 nm to 344 nm. Overall, II-M samples tended to have more intraparticle (intraP) pores and rela tively small OM pores. III-S samples had the highest TOC content, smaller OM pores and more intraparticle (intraP) pores (Fig. 11). Few OM pores were observed in sample ZD1-60 (S) with the highest TOC content of 10.73 wt% (Fig. 11a and b). Spaces between quartz particles were charged with bitumen and these OM particles were strongly compacted, resulting in no OM pores and some internal cracks in quartz particles (Fig. 11b). Pyrites can protect small solid bitumen (Figs. 11c and 0.68 μm in length and 0.52 μm in width) from being collapsed by the effective stress. The intergrowth of pyrites and OM particles occurred in sample Z2-27 (S-2) with abundant intercrystalline and intraparticle (intraP) pores in the 10
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Fig. 11. Typical FIB-SEM images about pore development characteristics on III-S samples. a, b and c represent sample ZD1-60 (S) with TOC content of 10.73 wt%; d, e and f represent sample Z2-27 (S-2) with TOC content of 4.65 wt%; g, h and i represent sample ZD1-39 (S-3) with TOC content of 5.67 wt%. a): Quartz, clay, pyrites and OM distribute randomly in shale sample. b): OM particles between cracked quartz shows no OM pores. c): Small OM particle protected by pyrites still contains lots of OM pores with max-pore diameter of 26 nm. d): The intergrowth of pyrites and OM, and pyrite can provide a good shelter for OM pores. e): Partial amplification of d) contains lots of elliptical OM pores inside the pyrite, which can reach about 57 nm. f): Intraparticle (IntraP) pores can be easily observed in apatite minerals with large pore diameter of 69 nm. g): OM and filamentous clays were intergrowth, and lots of OM pores were observed with pore diameter ranging from 18 nm to 61 nm. h): Lots of OM pores developed well inside the band-like pyrite. i): Quartz, clay, rutile and OM distribute randomly in shale sample, and OM pores can be easily observed.
pyrite framboid (Fig. 11d), which were filled with solid bitumen, OM pores can be directly seen (Fig. 11e). These OM pores had elliptical, bubble-like or alveolate shapes with pore diameters ranging from 7 nm to 57 nm. Large apatite particle contained a lot of elliptical intraparticle (intraP) pores with pore-sizes ranging from 18 nm to 69 nm (Fig. 11f). Finally, sponge-like OM pores developed between filamentous clays with diameters <61 nm in sample ZD1-39 (S-3) (Fig. 11g). Band-like pyrite can protect organic matters from being compacted (Fig. 11h) and OM pores can be preserve relatively large size. OM particles occurred with clays, rutile and quartz, and tended to have sponge-like pores (Fig. 11i). 5. Discussion 5.1. Effect of TOC content on porosity characteristics A moderately positive relationship is identified (Pearson correlation coefficient of 0.66) between TOC and quartz content (Fig. 12), indicative of a biogenic origin of quartz in the Doushantuo shale, and consistent with previous analyses of marine shales including the O3w-S1l Shales in the Jiaoshiba block (Guo et al., 2017; Yang et al., 2016), the є1n Shale in the Cen-gong block (Wang et al., 2016; Yang et al., 2019) and Devonian Horn River Shale in Horn River Basin (Chalmers et al., 2012). In
Fig. 12. Positive relationship between TOC content and quartz content on three main lithofacies groups, suggesting high III-S samples usually have rela tively higher quartz content.
11
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Fig. 13. Relationships between TOC content and a) porosity, b) micropore volumes obtained from CO2 adsorption, c) and d) mesopore and macropore volume obtained from N2 adsorption for these three main lithofacies groups.
addition, III-S samples had a higher TOC and quartz content than cor responding II-M and I-C samples. Relationships between TOC value and pore parameters reveal more complex pore characteristics in shales (Fig. 13). The porosity shows a
non-monotonic trend with a maximum porosity at a TOC of approxi mately 4.5–5.5 wt% (Fig. 13a), consistent with data reported for different shales (Li et al., 2016; Milliken et al., 2013). This indicates that TOC >4.5–5.5 wt% shales have relatively low porosity and these
Fig. 14. Examples of pore-size distribution curves and histograms calculated by N2 adsorption and FIB-SEM images analysis. The histograms data were calculated by Image Pro software. a) Pore-size distribution from 1 nm to 320 nm of sample EYY1-33 (C-1) (measured by N2 adsorption and BJH method). b) OM pore distribution histogram of the C-1 samples. c) Dissolution intraparticle pore distribution histogram of the C-1 samples. d): Intercrystalline pores within pyrite framboid distribution histogram of the C-1 samples. 12
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
samples are mostly plotted in III-S area, and especially in the S area. This relationship can be explained by two aspects; namely firstly, the bitumen and pyrobitumen generated by kerogen can occlude or occupy pore space or fill the fractures. Secondly, large pores tend to collapse or be compacted in response to effective stress. Yet a strong positive cor relation between TOC content and micropore volumes obtained from CO2 adsorption is evident (Fig. 13b, R2 ¼ 0.93), which was also observed in Permian Shales (Pan et al., 2013; Cao et al., 2016), O3w-S1l Shales (Yang et al., 2016) and Lower Cambrian Shale (Yang et al., 2019). The mesopore and macropore volumes obtained from N2 adsorption show similar trends with TOC (Fig. 13c and d), indicating that TOC >4.5–5.5 wt% samples have relatively low mesopore and macropore volumes. These high-TOC-content samples are mostly part of the III-S area, indi cating that III-S samples, and especially S samples, may have relatively worse porosity characteristics. This phenomenon can be explained that large OM particles can be easily compacted without effective protection when TOC content is up to 10.73%, resulting in relatively less mesopore and macropore (Fig. 11b and c). In terms of the pore-size distribution (PSD) characteristics of these three main lithofacies groups, the increments of pore volume in the III-S samples were relatively smaller than those for the II-M and I-C samples (Fig. 7). Sample EYY1-33 (C-1) for instance had a pore-size distribution ranging from 1 nm to 320 nm (calculated by N2 adsorption, Fig. 14a). The pore-size distribution histograms of OM pores, dissolution intra particle (intraP) pores and intercrystalline pores within pyrite framboid by SEM images analysis are illustrated in Fig. 14. The pore-size distri bution of OM pores (Fig. 14b) fits very well with the overall PSD from 2 nm to 30 nm, especially in the range from 2 nm to 20 nm, suggesting that these pores are dominated by OM pores. The pore-size distributions of dissolution intraparticle (intraP) pores and intercrystalline pores over lap from 20 nm to 50 nm, suggesting that these pores are dominated by intraparticle and intercrystalline pores. Furthermore, the pore-size dis tribution of intercrystalline pores also fits well with N2 adsorption pore size distribution (Fig. 14a) from 50 nm to 60 nm, indicating that these pores are partly dominated by intercrystalline pores. Finally, the poresize distributions of dissolution intraparticle (intraP) pores and inter crystalline pores overlap from 90 nm to 200 nm, suggesting that these pores are dominated by intraparticle pores and intercrystalline pores (Fig. 14c~4d). When pore size >200 nm, intercrystalline pores occupied part of the large pores (Fig. 14d). Moreover, pore-throat size distribu tions from 3 nm to 50 nm measured via MICP (Fig. 8a) also fit well with OM pores, and part of dissolution intraparticle (intraP) and intercrys talline pores range from 20 nm to 50 nm (Fig. 14b~14d).
sample (Curtis et al., 2012a, b) and Cambrian Niutitang Shale samples (Yang et al., 2019) in South China. We conclude that favorable OM type has a huge potential for the development of an interconnected organic network and thus hydrocarbon production.
5.2. Effect of organic matter type on porosity development
Author contribution
The effect of organic matter type on porosity development is signif icant, as different organic matter types have distinctly different pore development characteristics. Evidence shows that the smaller OM par ticles (Fig. 9b, e, 9h, 10c and 11e) were mobilized into the smaller pore space and subsequently converted into porous solid bitumen; this is supported by the fact that more mature OM has more pores. Clearly, pore diameters of these OM pores are relatively larger than other OM pores. Note that solid bitumen has a great mobility and hydrocarbon generation ability (Cardott et al., 2015). Larger OM particles can be divided into two types. Firstly, OM particles in Figs. 9d and 10g are most likely non-porous inert macerals, which are similar to the humic, type III kerogen which contains only few OM pores (Hou et al., 2015); these may stem from the hydrogen-lacking part of phytoplankton. Secondly, OM particles in Fig. 10a and d are most likely amorphous kerogen, which were compacted easily and still contained a few smaller and sponge-like OM pores in the magnified images of Fig. 10b and e. The trapped solid bitumen in the pyrite framboid (Fig. 9b, h, 11e and 11h) contained more OM pores, which indicates that pyrites induce more pore development and preservation (of OM pores). Such an effect of organic matter type on OM pores development has also been reported for a Woodford Shale
Wei Yang: Conceived and designed the analysis, Collected the data, Contributed data or analysis tools, Performed the analysis, Wrote the paper; Sheng He: Conceived and designed the analysis, Collected the data, Contributed data or analysis tools; Stefan Iglauer: Contributed data or analysis tools, Other contribution; Xiaowen Guo: Conceived and designed the analysis; Gangyi Zhai: Collected the data; Zhi Zhou: Collected the data; Tian Dong: Contributed data or analysis tools; Ze Tao: Contributed data or analysis tools; Sile Wei: Contributed data or analysis tools.
6. Conclusions We present here a detailed study of the Sinian Doushantuo shale (stemming from the Yichang area of western Hubei). This includes geochemical and mineralogical analysis, low-pressure gas adsorption, mercury injection capillary pressure (MICP) and high-resolution FIBSEM analysis. Our results thus provide critical new insights into the reservoir characteristics of the shale gas layer. We conclude that: (1) TOC content of the Sinian Doushantuo Shale averages 2.59 wt%, ranging from 0.39 wt% to 10.73 wt%. The porosity of shale samples from wells EYY1, ZD1 and ZD2 averaged 1.86%, 4.09% and 4.24%, respectively. Mineralogical composition is dominated by dolomite, quartz and clay, and calcareous shale lithofacies group (I-C), mixed shale lithofacies group (II-M) and siliceous shale lithofacies group (III-S) were identified. (2) Different lithofacies have varying porosity, pore volume, surface area and pore-size distribution (ranging from 0.3 nm to 36 μm). Porosity shows a non-monotonic trend with TOC value and has a maximum at 4.5–5.5 wt% TOC. Samples from the siliceous shale lithofacies group (III-S), and especially in the S area with high TOC value, have relatively low porosity probably caused by the effective stress and consequent less effective mineral framework support, resulting in pore collapse or be compaction. FIB-SEM images also revealed that different organic matter types have distinct pore development characteristics. (3) The examined OM particles in shales can be divided into solid bitumen and two types of kerogen. The smaller mobilized solid bitumen has more abundant and larger OM pores, and larger OM particles were most likely non-porous inert maceral and spongelike porous amorphous kerogen. (4) TOC content and organic types are the key controlling factors to the nanoscale porosity development in the Doushantuo Shale. Overall we thus conclude that the Sinian Doushantuo shale (in the Yichang area) has great potential for shale gas exploration and exploi tation in South China and can significantly contribute to the develop ment and prosperity of the western Hubei economy.
Acknowledgments The authors would like to thank the National Natural Science Foundation of China (Nos. 41690134 and 41672139), China National Science and Technology Major Projects (No. 2016ZX05034002-003), and China Geological Survey Project Grant (No. DD20190561-1) for financial assistance to this research. We also express our appreciation to Oil & Gas Survey Center of China Geological Survey for providing shale samples.
13
W. Yang et al.
Journal of Petroleum Science and Engineering 187 (2020) 106856
Appendix A. Supplementary data
Liu, Y., Yin, C., Gao, L., Wang, Z., 2003. Advances in the study of sedimentary facies of the Sinian candidate stratotype in the eastern areas of the Three Gorges, Hubei. Geol. Rev. 49 (2), 187–194. Loucks, R.G., Reed, R.M., Ruppel, S.C., Hammes, U., 2012. Spectrum of pore types and networks in mudrocks and a descriptive classification for matrix-related mudrock pores. AAPG (Am. Assoc. Pet. Geol.) Bull. 96, 1071–1098. Loucks, R.G., Reed, R.M., Ruppel, S.C., Jarvie, D.M., 2009. Morphology, genesis, and distribution of nanometer-scale pores in siliceous mudstones of the Mississippian Barnett Shale. J. Sediment. Res. 79, 848–861. Loucks, R.G., Ruppel, S.C., 2007. Mississippian Barnett shale: lithofacies and depositional setting of a deepwater shale-gas succession in the Fort Worth Basin, Texas. AAPG (Am. Assoc. Pet. Geol.) Bull. 91 (4), 579–601. Manger, G.E., 1963. Porosity and Bulk Density of Sedimentary Rocks. Milliken, K.L., Curtis, M.E., 2016. Imaging pores in sedimentary rocks: Foundation of porosity prediction. Mar. Pet. Geol. 73, 590–608. Milliken, K.L., Reed, R.M., 2010. Multiple causes of digenetic fabric anisotropy in weakly consolidated mud, Nankai accretionary prism, IOPD expedition 316. J. Struct. Geol. 32 (12), 1887–1898. Milliken, K.L., Rudnicki, M., Awwiller, D.N., Zhang, T.W., 2013. Organic matter-hosted pore system, Marcellus formation (Devonian), Pennsylvania. AAPG (Am. Assoc. Pet. Geol.) Bull. 97, 177–200. Pan, L., Chen, G., Xu, J., Xiao, X., 2013. Pore structure characteristics of Permian organic-rich shale in lower Yangtze area. J. China Coal Soc. 38 (38), 787–793. Peng, N., He, S., Hu, Q., Zhang, B., He, X., Zhai, G., He, C., Yang, R., 2019. Organic nanopore structure and fractal characteristics of Wufeng and lower member of Longmaxi shales in southeastern Sichuan, China. Mar. Pet. Geol. 103, 456–472. Slatt, R.M., O’Brien, N.R., 2011. Pore types in the Barnett and Woodford gas shales: contribution to understanding gas storage and migration pathways in finegrained rocks. AAPG (Am. Assoc. Pet. Geol.) Bull. 95, 2017–2030. Thommes, M., Kaneko, K., Neimark, A.V., Olivier, J.P., Rodriguez-Reinoso, F., Rouquerol, J., Sing, K.S.W., 2015. Physisorption of gases, with special reference to the E-valuation of surface area and pore size distribution (IUPAC technical report). Pure Appl. Chem. 87 (9–10), 1051–1069. Wang, G.C., Carr, T.R., 2013. Organic-rich Marcellus Shale lithofacies modeling and distribution pattern analysis in the Appalachian basin. AAPG Bull. 97 (12), 2173–2205. Wang, R., Ding, W., Zhang, Y., Wang, Z., Wang, X., He, J., Zeng, W., Dai, P., 2016. Analysis of developmental characteristics and dominant factors of fractures in Lower Cambrian marine shale reservoirs: a case study of Niutitang Formation in Cen-gong Block, Southern China. J. Pet. Sci. Eng. 138, 31–49. Wang, Y., Zhai, G., Bao, S., Zhou, Z., Song, T., Li, H., 2017. Evaluation of Sinian Doushantuo formation shale gas content and fracturing property of Eyangye1 well in Hubei province. China Min. Mag. 26 (6), 166–172. Washburn, E.W., 1921. The dynamics of capillary flow. Phys. Rev. 17 (3), 273–283. Webb, P.A., 2001. An introduction to the physical characterization of materials by mercury intrusion porosimetry with emphasis on reduction and presentation of experimental data. Micromeritics Instrument Corp, Norcross, Georgia. Wu, L., Lu, Y., Jiang, S., Lu, Y., Liu, X., Hu, H., 2018. Pore structure characterization of different lithofacies in marine shale: a case study of the Upper Ordovician WufengLower Silurian Longmaxi formation in the Sichuan Basin, SW China. J. Nat. Gas Sci. Eng. 57, 203–215. Xu, D., Peng, L., Liu, H., Wei, Y., 2013. Meso-Cenozoic tectono-sedimentary response of multiphased uplifts of Huangling Anticline, Central China. Geol. Miner. Resour. South China 29 (2), 90–99. Yang, P., Xie, Y., Li, X., Bai, D., Liu, Z., Chen, H., 2012. Hydrocarbon-generating potential of the source rocks of the Sinian Doushantuo formation on the western side of the Xuefeng Mountain. Chin. Geol. 39 (5), 1299–1310. Yang, R., He, S., Yi, J., Hu, Q., 2016. Nano-scale pore structure and fractal dimension of organic-rich Wufeng-Longmaxi shale from Jiaoshiba area, Sichuan Basin: investigations using FE-SEM, gas adsorption and helium pycnometry. Mar. Pet. Geol. 70, 27–45. Yang, W., He, S., Zhai, G., Dong, T., Gong, D., Yuan, X., Wei, S., 2019. Shale-gas accumulation and pore structure characteristics in the lower Cambrian Niutitang shales, Cen-gong Block, South China. Aust. J. Earth Sci. 66 (2), 289–303. Zhai, G., Bao, S., Wang, Y., Chen, K., Wang, S., Zhou, Z., Song, T., Li, H., 2017. Reservoir accumulation model at the edge of Palaeohigh and significant discovery of shale gas in Yichang area, Hubei Province. Acta Geosci. Sin. 38 (4), 441–447. Zhang, M., Peng, S., Zhang, L., Fang, J., Zhang, X., Han, Q., 2016. New recognition of carbonate nodules genesis in Sinian Doushantuo formation in Zigui area and its geological implication. Earth Sci. 41 (12), 1977–1994. Zhao, P., Ma, H., Rasouli, V., Liu, W., Cai, J., Huang, Z., 2017. An improved model for estimating the TOC in shale formations. Marine Petrol. Geol. 83, 174–183. Zhao, P., Mao, Z., Huang, Z., Zhang, C., 2016. A new method for estimating total organic carbon content from well logs. AAPG Bull. 100 (8), 1311–1327.
Supplementary data to this article can be found online at https://doi. org/10.1016/j.petrol.2019.106856. References Bernard, S., Wirth, R., Schreiber, A., Schulz, H.M., Horsfield, B., 2012. Formation of nanoporous pyrobitumen residues during maturation of the Barnett shale (Fort Worth Basin). Int. J. Coal Geol. 103 (23), 3–11. Bustin, R.M., Bustin, A.M.M., Cui, A., Ross, D., Pathi, V.M., 2008. Impact of Shale Properties on Pore Structure and Storage Characteristics. Society of Petroleum Engineers, Fort Worth, TX. Cao, T., Song, Z., Liu, G., Yin, Q., Luo, H., 2016. Characteristics of shale pores, fractal dimension and their controlling factors determined by nitrogen adsorption and mercury injection methods. Pet. Geol. Recovery Effic. 23 (2), 1–8. Cardott, B.J., Landis, C.R., Curtis, M.E., 2015. Post-oil solid bitumen network in the Woodford Shale, USA - potential primary migration pathway. Int. J. Coal Geol. 139, 106–114. Chalmers, G.R., Bustin, R.M., Power, I.M., 2012. Characterization of gas shale pore systems by porosimetry, pycnometry, surface area, and field emission scanning electron microscopy/transmission electron microscopy image analyses: examples from the Barnett, Woodford, Haynesville, Marcellus, and Doig unit. AAPG (Am. Assoc. Pet. Geol.) Bull. 96 (6), 1099–1119. Chen, L., Jiang, Z., Liu, K., Wang, P., Ji, W., Gao, F., Li, P., Hu, T., Zhang, B., Huang, H., 2016. Effect of lithofacies on gas storage capacity of marine and continental shales in the Sichuan Basin, China. J. Nat. Gas Sci. Eng. 36, 773–785. Chen, X., Wei, K., Zhang, B., Li, P., Li, H., Liu, A., Luo, S., 2018. Main geological factors controlling shale gas reservoir in the Cambrian Shuijingtuo Formation in Yichang of Hubei Province as well as its and enrichment patterns. Chin. Geol. 45 (2), 207–226. Curtis, J.B., 2002. Fractured shale gas systems. AAPG Bull. 86 (11), 1921–1938. Curtis, M.E., Cardott, B.J., Sondergeld, C.H., Rai, C.S., 2012. Development of organic porosity in the Woodford Shale with increasing thermal maturity. Int. J. Coal Geol. 103, 26–31. Curtis, M.E., Sondergeld, C.H., Ambrose, R.J., Rai, C.S., 2012. Microstructural investigation of gas shales in two- and three-dimensions using nanometer-scale resolution imaging. AAPG (Am. Assoc. Pet. Geol.) Bull. 96, 665–677. Diaz, H.G., Miller, C., Lewis, R., Fuentes, C.C., 2013. Evaluating the Impact of Mineralogy on Reservoir Quality and Completion Quality of Organic Shale Plays. AAPG Rocky Mountain Section Meeting, Salt Lake City, Utah. Dong, T., Harris, N.B., Ayranci, K., Twemlow, C.E., Nassichuk, B.R., 2015. Porosity characteristics of the Devonian Horn River shale, Canada: insights from lithofacies classification and shale composition. Int. J. Coal Geol. 141–142, 74–90. Dong, T., Harris, N.B., Ayranci, K., Twemlow, C.E., Nassichuk, B.R., 2017. The impact of composition on pore throat size and permeability in high maturity shales: middle and upper Devonian Horn River Group, northeastern British Columbia, Canada. Mar. Pet. Geol. 81, 220–236. Gan, H., Nandi, S.P., Walker, P.L., 1972. Nature of the porosity in American coals. Fuel 51, 272–277. Gao, Z., Hu, Q., 2013. Estimating permeability using median pore-throat radius obtained from mercury intrusion porosimetry. J. Geophys. Eng. 10 (2), 025014. Online at stacks, .iop.org/JGE/10/025014. Gregg, S., Sing, K., 1982. Adsorption, Surface Area and Porosity, second ed. Academic Press, London. Guo, X., Hu, D., Li, Y., Wei, Z., Wei, X., Liu, Z., 2017. Geological factors controlling shale gas enrichment and high production in Fuling shale gas field. Pet. Explor. Dev. 44 (4), 481–491. Hou, Y., He, S., Wang, J., Harris, N.B., Cheng, C., Li, Y., 2015. Preliminary study on the pore characterization of lacustrine shale reservoirs using low pressure nitrogen adsorption and field emission scanning electron microscopy methods: a case study of the Upper Jurassic Emuerhe Formation, Mohe basin, northeastern China. Can. J. Earth Sci. 62, 294–306. Jarvie, D.M., Hill, R.J., Ruble, T.E., Pollastro, R.M., 2007. Unconventional shale-gas systems: the Mississippian Barnett Shale of north-central Texas as one model for thermogenic shale-gas assessment. AAPG (Am. Assoc. Pet. Geol.) Bull. 91 (4), 475–499. Li, H., Song, T., Chen, K., Lin, T., Jin, C., Meng, F., Wang, P., Zhang, Y., 2017. The discovery of shale gas from Sinian Formation at ZD-2 well in western Hubei. Chin. Geol. 44 (4), 812–813. Li, X., Chen, G., Chen, Z., Wang, L., Wang, Y., Dong, D., Lv, Z., Lv, W., Wang, S., Huang, J., Zhang, C., 2016. An insight into the mechanism and evolution of shale reservoir characteristics with over-high maturity. J. Nat. Gas Geosci. 1 (5), 373–382.
14