Figure 4 - from "Assessing the utility of FIB-SEM images for shale digital rock physics"

rable'4. Overview of Analyzed 2D BIB-SEM Cross-Section Images

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Fig. 1. (A) Low magnification SEM image of an example of a mechanically milled/smoothed shale sample surface (Sample D1) and the selection of a FIB-SEM site (within the circled region). (B) High magnification SEM image of the FIB-SEM site. Platinum is deposited onto the FIB-SEM site with the FIB-SEM instrument’s (FEI Company) material deposition system. The platinum helps reduce the accumulation offimpinging electrons or “charging”. Images were provided by Sandia National Labs (Albuquerque, NM).

$Table 1. Overview of Analyzed FIB-SEM Images Standard core analysis data were acquired from each core associated with a group, of FIB-SEM images. [he relevant core data available for Groups A-D include Gas Research Institute(GRD,,X-ray diffraction (XRD) data, LECO TOC, and Rock Eval Pyrolysis. GRI data includes: “‘asf#eceived’bulk density, matrix permeability, gas-filled porosity, and gas saturation; and dry and Dean Stark (extracted conditions) grain density, matrix permeability, porosity, oil saturation, and water saturations=XRD identifies and quantifies he crystalline components of a (powdered) shale sample on a relative percentage basis and thus is not helpful for evaluating organic matter, which is non-crystalline.“The LECO TOC method, named after the LECO brand carbon analyzer, provides a reliable value of TOC by measuring the amount of carbon produced upon combustion of a powdered, dry sample.“Results “are delivered in weight percent as the weight of combusted carbon over the original weight,of the\dry sample. Values of organic matter (or kerogen) density and the mass fraction of carbon in kerogen are needed to convert to volume percent; a kerogen density of 1.3 g/cc and a carbon mass fraction of 0.80 (a typical value for mature kerogen) are assumed throughout this analysis. Rock Eval Pyrolysis is used to measure the maturity of the organic matter and yields the normalized amounts of free.hydrocarbons in the sample (S;), hydrocarbons formed by pyrolysis of kerogen (thermal cracking) (S2), and CO, released during breakdown of kerogen ($3), as well as the temperature at whichAhe maximum thermal cracking occurs (Tiny). In addition to these standard core measurements, ultraviolet (UV) and fluorescent microscopy mapping of a thin section impregnated with Rhodamine\B dyéd.époxy were performed for the Group C sample. The core data is summarized in tabular formin Tables 2 and 3; permeability is presented in the unit of milliDarcy (mD), 1s is commonly used fof porous media applications. One mD is equal to 9.87x10° m’.$

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Fig. 2. Examples of single images from each analyzed FIB-SEM stack. Scale bars for each group are as follows (the scale is appftoximately the same from stack to stack within a group): A = 3.3 um, B = 2.9 um, C = 5.9 um, and D = 4.8 um),Note that images from Groups A and B are of superior quality, but that images from Groups C and D offer a larger field of view. Group C and D image stacks were provided by Sandia National Labs (Albuquerque, NM).

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Shaina Kelly

Assessing the utility of FIB-SEM images for shale digital rock physics

Abstract: Highlights  We investigate if 3D models from FIB-SEM image stacks are a suitable REV for shale.  Data includes groups of local 2D and 3D images and corresponding core measurements.  Image analysis of image stacks and LBM fluid flow simulations are performed.  We conclude that FIB-SEMs are not suitable domains for permeability simulations.  Averaging local 2D and 3D images can provide representative volumetric properties.

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Abstract: Focused ion beam scanning electron microscopy (FIB-SEM) is a commonly used three-dimensional (3D) pore-network reconstruction method for shales due to its unique capability in imaging nano-scale pores. However, it has been found that for pore space of lacustrine shales with strongly heterogeneous pore structures, the conventional FIB-SEM 3D models usually with dimensions of 10 μm × 10 μm × 10 μm cannot adequately characterize the pore structures as the representative element volume required is much larger than the FIB models. Here, we propose to utilize large volume FIB-SEM (LV-FIB-SEM) 3D models to resolve this challenge. The LV-FIB-SEM model has a significant enhancement in the model size compared with the commonly used conventional FIB-SEM models and a much higher spatial resolution than non-synchrotron nano X-ray CT models for similar imaging sample sizes. With 75 μm × 65 μm × 60 μm as predesigned reconsruction size, after image processing two LV-FIB-SEM 3D models with sizes of 73.56 μm × 38.13 μm × 52.59 μm and 74.01 μm × 43.05 μm × 42.00 μm and model resolution of 30 nm were reconstructed and quantitatively analyzed. When use the conventional FIB-SEM models of 10 μm × 10 μm × 10 μm, the relative deviations between the porosities derived from 100 stochastic models and the average porosity for the two samples studied are −41.13% ∼ +87.31% and −51.66% ∼ +56.05%, respectively, indicating that such small models are not representative of the actual pore structure of the shales investigated. When the model sizes have been increased by 96 times volumetrically, the probabilities of matching average porosities for the two samples increase from 13% to 86% and from 12% to 100%, respectively. This research demonstrates that the upsizing of the FIB-SEM models enables an effective improvement on the representativeness of shale pore structures characterized. It is recommended that LV-FIB-SEM 3D reconstruction be employed to study pore space of lacustrine shales with strongly heterogeneous pore structures, which would enable a more accurate characterization and evaluation of reservoirs for shale oil exploration and development.

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Using BIB-SEM Imaging for Permeability Prediction in Heterogeneous Shales

Abstract: Organic-rich shale samples from a lacustrine sedimentary sequence of the Newark Basin (New Jersey, USA) are investigated by combining Broad Ion Beam polishing with Scanning Electron Microscopy (BIB-SEM). We model permeability from this 2D data and compare our results with measured petrophysical properties. Three samples with total organic carbon (TOC) contents ranging from 0.7% to 2.9% and permeabilities ranging from 4 to 160 nD are selected. Pore space is imaged at high resolution (at 20,000x magnification) and segmented from representative BIB-SEM maps. Modeled permeabilities, derived using the capillary tube model (CTM) on segmented pores, range from 2.3 nD to 310 nD and are relatively close to measured intrinsic permeabilities. SEM-visible porosities range from 0.1% to 1.8% increasing with TOC, in agreement with our measurements. The CTM predicts permeability correctly within one order of magnitude. The results of this work demonstrate the potential of 2D BIB-SEM for calculating transport properties of heterogeneous shales.

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Farzam Javadpour

Multiscale and multiresolution modeling of shales and their flow and morphological properties

Abstract: The need for more accessible energy resources makes shale formations increasingly important. Characterization of such low-permeability formations is complicated, due to the presence of multiscale features, and defies conventional methods. High-quality 3D imaging may be an ultimate solution for revealing the complexities of such porous media, but acquiring them is costly and time consuming. High-quality 2D images, on the other hand, are widely available. A novel three-step, multiscale, multiresolution reconstruction method is presented that directly uses 2D images in order to develop 3D models of shales. It uses a high-resolution 2D image representing the small-scale features to reproduce the nanopores and their network, a large scale, low-resolution 2D image to create the larger-scale characteristics, and generates stochastic realizations of the porous formation. The method is used to develop a model for a shale system for which the full 3D image is available and its properties can be computed. The predictions of the reconstructed models are in excellent agreement with the data. The method is, however, quite general and can be used for reconstructing models of other important heterogeneous materials and media. Two biological examples and from materials science are also reconstructed to demonstrate the generality of the method. Due to the substantial amount of natural gas that they store, shales have emerged as one of the most promising current and future energy resources. Shale gas can be dissolved in kerogen, be adsorbed on clay and kerogen, and/or appear as free gas in pores and natural fractures 1,2. Most of the gas production in such porous formations occurs in the source area because, owing to the low permeability of the formations 3 , the gas does not migrate significantly. Unlike conventional large-scale reservoirs that have been characterized through countless methodologies 4 , shale formations have not yet undergone advanced characterization, and much remains to be done. A most important obstacle to characterization and modeling of shales, which up to now has seemed insurmountable, is their multiscale structure that defies the conventional techniques of characterization. Pores of all sizes, ranging from nano to micro exist in shales 5,6 , which play a vital role in fluid flow 7,8. The intrinsic small scale and spatial variability of such pores require a very large number of samples for direct characterization of shales that, if available, will make it possible to better understand the depositional environment, mineralogy, maturation, thermal condition, total organic carbon, strain/stress properties, porosity, and permeability of shales. But, shales are also large-scale porous media and, therefore, the spatial variations of their properties also occur not only at very large scales. Thus, there are several distinct and disparate length scales that are relevant to modeling and characterization of shales. Two-or three-dimensional (3D) imaging of shale samples have become an important tool for their characterization, because they reveal very useful information about their pore network and their spatial connectivity, and provide a reliable platform for their characterization. In addition, some of the aforementioned key morphological and flow properties of shales, such as the TOC, mineralogy, porosity,

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