The Rock–Eval (RE) technique provides a rapid and reliable way to geochemical screening of organic-rich shales. It assesses the amount of organic carbon present, and provides indications of the type of organic matter present (oil-prone and/or gas-prone kerogen) from the hydrogen index (HI), and indirect measures of thermal maturity from the temperature profiles of its S2 peak. Recent studies have revealed that additional compositional and thermal maturity data can be extracted from RE’s S3 and S4 peaks, respectively. It has been observed that shale samples need to be carefully prepared applying limits to their sample weight and particle size to obtain consistent/repeatable results. Using pyrolysis analysis at three heating rates enables the reaction kinetics to be determined for shales and/or their component kerogens. To achieve this effectively, the Arrhenius equation is used to fit the S2 peaks of the three heating rates in parallel with an optimizer allowing both activation energy (E) and pre-exponential factor (A) to vary for a set of approximately eleven first-order reactions. Each reactions E and A values used to achieve the S2 peak fit with low error are weighted in accordance to their fractional contribution. The weighted average and weighted standard deviations of the E and A values of the fitted reactions tend to closely follow an empirically observed E versus lnA trend. The E and A values can also be used to quantify the hydrocarbon generation fraction versus temperature, a key PSM requirement. A case study of shale samples from the Damodar Valley Permian basins of northeast India illustrates how shale reaction kinetic can be derived from the RE S2 peaks and interpreted. X-ray diffraction (XRD), X-ray fluorescence (XRF), and inductively coupled plasma mass spectrometry (ICP-MS) provide details of the mineralogical and elemental compositions of shales. XRF scanning of core samples with hand-held devices provide rapid and detailed geochemical characterization. Machine learning models including cluster analysis can assist in the interpretation of large XRF datasets. ICP-MS analysis provides fine-scale elemental details, which are useful for defining geochemical pathways through a shales pore network, mineralogical transformations and environmental risks of chemical pollution from fracture-stimulation fluid flow back.