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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.

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Shale reservoirs represent a significant and increasing portion of natural gas production within North America. In order to better understand gas deliverability of these reservoirs, the flow paths and their connectivity within shales need to be understood at a fundamental level. The pores within gas shales are on the nanoscale and below optical resolution. This necessitates methods such as electron microscopy to image them. Recently, the introduction of Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) technology to the petroleum industry has enabled the imaging of these nanoscale pore structures in three dimensions (3D) for the first time. Imaging of 125 μm3 shale volumes has been performed on different gas shales and the pore systems within these volumes have been reconstructed. Throughout our investigations it has been difficult to obtain pore connectivity across the reconstructed shale volumes. High pressure mercury injection capillary pressure (MICP) measurements on gas shales suggest that the connections between many of these pores are less than 2 nm. Because the size of the smallest pores and pore throats detected within gas shales approaches the resolution limits of SEM, higher resolution techniques must be employed. Transmission electron microscopy (TEM) can image the internal structure of thin shales specimens and enables much higher resolution imaging than SEM. Utilizing this imaging technique in conjunction with other methods we discuss our investigations of pores and their connections that are below the SEM imaging resolution used in previous studies. Initial imaging suggests that isolated pores previously imaged with the SEM exhibit connectivity. It is the connections of pore spaces that control the gas flow in shales and such insights will have far reaching implications in better understanding this flow.