Pore Microstructure and Multifractal Characterization of Lacustrine Oil-Prone Shale Using High-Resolution SEM: A Case Sample from Natural Qingshankou Shale

Abstract

Pore structure is one of the important parameters for evaluating reservoirs, critical in controlling the storage capacity and transportation properties of hydrocarbons. The conventional pore characterization method cannot fully reflect the pore network morphology. The edge-threshold automatic processing method is applied to extract and quantify pore structures in shale scanning electron microscope (SEM) images. In this manuscript, a natural lacustrine oil-prone shale in the Qingshankou Formation of Songliao Basin is used as the research object. Based on FE-SEM, a high-resolution cross-section of shale was obtained to analyze the microstructure of pores and characterize the heterogeneity of pores by multifractal theory. The stringent representative elementary area (REA) of the SEM cross-section was determined to be 35 × 35. Four pore types were found and analyzed in the stringent REA: organic pores, organic cracks, inorganic pores, inorganic cracks. The results showed that inorganic pores and cracks were the main pore types and accounted for 87.8% of the total pore area, and organic cracks were of the least importance in the Qingshankou shale. Inorganic pores were characterized as the simplest pore morphologies, with the largest average MinFeret diameter, and the least heterogeneity. Moreover, the inorganic cracks had a long extension distance and stronger homogeneity, which could effectively connect the inorganic pores. Organic pores were found to be the most complex for pore structure, with the least average MinFeret diameter, but the largest heterogeneity. In addition, the extension distance of the organic cracks was short and could not effectively connect the organic pore. We concluded that inorganic pores and cracks are a key factor in the storage and seepage capacity of the Qingshankou shale. Organic pores and cracks provide limited storage space.

Quantifying the Pore Heterogeneity of Alkaline Lake Shale during Hydrous Pyrolysis by Using the Multifractal Method

“…The Hurst index, calculated as H = (D 2 + 1)/2, typically falls within the range of 0.5 to 1. A higher H value indicates increased autocorrelation within a size-dependent porosity distribution [8,82,83]. Moreover, the Hurst index serves as a measure of pore connectivity, with higher values suggesting improved pore connectivity.…”

“…The heterogeneity of pore structure, as represented by ∆D, is primarily influenced by volume changes among different types of pores. A higher ∆D value indicates a greater deviation in the dispersion of pore dimensions [8,82,84]. The ∆D values for samples at various pyrolysis temperatures range from 1.1883 to 1.3.…”

Pore-Type-Dependent Fractal Features of Shales and Implications on Permeability

“…Three cubic samples (2.5 × 2.5 × 2 mm 3 ) were characterized by a ZEISS Crossbeam 540 instrument and electron-beam-scanned at a vacuum of <5 × 10 −6 mbar for reconstruction. FIB milling and SEM imaging of shale samples layer by layer enable the reconstruction of 2D sections into 3D volumes with a voxel size of 10 nm 3 . In particular, regions of interest were selected with a size of 11.3 × 10.3 × 8.2 µm 3 , 11.1 × 10.7 × 5.8 µm 3 and 10.1 × 9.7 × 4.3 µm 3 , respectively, for covering the different pore types concerned in this paper.…”

“…Shale reservoirs, as self-contained source-reservoir systems, have become a high research priority because of their potential to provide long-term gas and oil resources [1][2][3]. Unlike conventional reservoirs, pores in shale are mainly on the nanoscale, and their properties vary in size, shape and connectivity depending on the mineral composition [4].…”

A node graph using SEM images for material properties prediction with a case study of composite material

“…In the process of material property prediction using SEM images, the size of a single SEM image is small and the microstructural information it reflects is limited, which can lead to a decrease in model prediction accuracy and robustness. In order to utilize richer and more adequate microstructural information, Rani G Elizabeth et al [25] used high-resolution images for material strength prediction of eggshell powder with 20 mm diameter, Tian et al [26] used high-resolution sections of shale and analyzed the microstructure of pores using multiple fractal theory to characterize the non-homogeneity of pores. Smith et al [27]developed a new technique combining high-resolution aberration-corrected SEM imaging and high-resolution X-ray energy dispersive spectroscopy, which can accurately and independently measure the size, volume fraction, and number density of these three precipitated phases.…”