Structural Characterization of Gas Shales on the Micro- and Nano-Scales

Abstract

A reliable petrophysical rock classification can significantly improve production form organic-rich source rocks, which have been recognized as a major energy resource in the recent years. Complex lithology and rapid vertical variation of petrophysical and compositional properties in these reservoirs result in challenging formation evaluation and production planning. Well logs are good candidates for formation evaluation of shale-gas reservoirs as they provide measurements with a relatively high vertical resolution. However, it has been challenging for the petroleum industry to estimate petrophysical, elastic, and compositional properties and to classify rock types in organic-shale formations using only conventional well-log interpretation techniques. Conventional rock typing techniques are also significantly dependent on core measurements and not reliable in shale-gas reservoirs. In this paper, we introduce a rock typing method based on the estimated petrophysical, compositional, and elastic properties obtained from combined interpretation of well logs and core measurements.We first jointly interpret photoelectric factor, density, neutron porosity, compressional-and shear-wave slowness, and elemental capture spectroscopy logs to estimate depth-by-depth petrophysical and compositional properties of the organic-rich source formation. We then apply the self-consistent approximation model to estimate depth-bydepth elastic properties of the rock using the estimates of petrophysical and compositional properties. Finally, we use the depth-by-depth estimates of porosity, Total Organic Content (TOC), fluid saturations, volumetric concentrations of mineral constituents, and elastic properties to classify rock types in the reservoir using unsupervised artificial neural network.We successfully applied the introduced method in the Haynesville shale-gas formation for rock classification. The estimates of porosity, TOC, bulk modulus, shear modulus, and volumetric concentrations of minerals are in agreement with core measurements. We verified the identified rock types using thin-section images. We also showed that well logs can directly be used for rock classification instead of petrophysical/compositional properties estimated from well logs. Direct application of well logs can reduce uncertainty associated with physical models used for well-log interpretation. Implementing an efficient rock classification technique using well logs can potentially improve production from these reservoirs.

Deformation in cemented mudrock (Callovo–Oxfordian Clay) by microcracking, granular flow and phyllosilicate plasticity: insights from triaxial deformation, broad ion beam polishing and scanning electron microscopy

“…FIB and BIB), which allow imaging of mineral fabrics and porosity down to the nanometre scale in very high-quality cross sections with SEM and TEM (Lee et al, 2003;Desbois et al, 2009Desbois et al, , 2011Desbois et al, , 2013Desbois et al, , 2016Loucks et al, 2009;Curtis et al, 2010;Heath et al, 2011;Klaver et al, 2012;Keller et al, 2011Keller et al, , 2013Houben et al, 2013Houben et al, , 2014Hemes et al, 2013Hemes et al, , 2015Laurich et al, 2014;Warr et al, 2014;Song et al, 2016). Serial sectioning allows the reconstruction of microstructure in 3-D (Keller et al, 2011(Keller et al, , 2013Milliken et al, 2013;Hemes et al, 2015), and cryogenic techniques can image the pore fluid in the samples and avoid artefacts produced by drying Schmatz et al, 2015).…”

Flow and Transport in Tight and Shale Formations: A Review

“…A recent review on these imaging techniques can be found in Blunt et al [51]. On the other hand, other techniques including focused ion beams (FIB) and scanning electron microscopy (SEM) are essentially destructive (Tomutsa et al [52], Curtis et al [53], and Lemmens et al [54]). SEM can be used to extract two-dimensional planar images of the microstructures.…”

Using BIB-SEM Imaging for Permeability Prediction in Heterogeneous Shales

“…By using these imaging methods, it is possible to receive a solid idea of a shale's internal (micro) structure and pore geometry (e.g., [6][7][8][9][10][11]). BIB milling in combination with SEM imaging delivers direct information about pore sizes, their morphology and spatial distribution, rock microstructure, and potential anisotropy as well as core damage due to sample handling (e.g., [3,[12][13][14][15][16][17][18][19][20][21][22][23][24][25]). Additionally, in combination with energy dispersive X-ray spectroscopy (EDX), BIB-SEM enables the identification of individual grains [26,27].…”