To demonstrate the potential of modern pyrolysis/mass spectrometry (Py-MS) for rapid characterization of oil shale kerogens and alginites of different geological and depositional origin, four key world oil shale kerogens and three alginites were analyzed by Curie-point Py-MS in combination with multivariate analysis methods such as factor and discriminant analysis.
The pyrolysis mass spectra of the algal coal torbanite and the two recent alginites coorongite and botryococcus rubber reveal similar structural characteristics as observed in previous pyrolysis gas chromatographic/mass spectrometric (GC/MS) studies, including signatures of specific aliphatic hydrocarbon moieties produced by Botryococcus braunii. Moreover, application of a new time-resolved Py-MS technique reveals the rather heterogeneous nature of the two recent alginites coorongite and botryococcus rubber which contain several types of oxygen functionalities and exhibit complex thermal decomposition reactions, as opposed to the much more homogeneous nature of torbanite which shows a rather simple thermal decomposition behavior. The thermal decomposition patterns of the four kerogens derived from Green River, kukersite, tasmanite, and messel oil shales show an overall resemblance with torbanite. Factor and discriminant analysis of the pyrolysis mass spectra, however, reveal the existence of relatively minor but highly characteristic differences. For instance, the messel shale kerogen shows a significant hydroxyaromatic component, presumably reflecting contributions of terrestrial plant materials to the depositional environment. In contrast, the high sulfur signals of the tasmanite shale kerogen reveal a strong marine influence.
The relationship between the Py-MS patterns in the discriminant analysis plot shows an overall resemblance with the relative positions of the kerogens in a Van Krevelen diagram. Consequently, it appears feasible to classify oil shale kerogens into the proper Tissot types (I–IV) on the basis of direct Py-MS results.
It is concluded that computerized Py-MS techniques offer a highly promising approach to rapid structural characterization and classification of a potentially broad range of oil shale kerogens and alginites.