Digital outcrop models (DOMs) have facilitated quantitative and qualitative studies in digital and virtual environments of source and reservoir rock analogs important to the oil industry. The use of immersive virtual reality (iVR) to extend field experiences has motivated several research groups to develop software integrating immersive virtual reality techniques with tools to interpret and derive geological information from DOMs. This virtual approach can also contribute to the development of geological and spatial thinking skills taught in the classroom and during field trips. The immersive virtual field trips (iVFTs) can provide students access to outcrops and additional data restricted to field learning activities while allowing additional interactions impossible in the field. iVFTs have been developed recently however the structuring of iVFTs for geology classes has not been presented in a way that inexperienced iVR users can make use of such systems. In this scenario, our work proposes a method to structure an iVFT using georeferenced data containers (GDCs) and the virtual reality software Mosis LAB while evaluating users' perceptions during an iVFT study case. The evaluation using technology acceptance model (TAM) questionnaires, showed that users were positively impacted by the observational iVFT experience, effectively supporting e-learning, and class field learning activities and preparations. This approach allows field trip experiences in less accessible study sites, especially in less favorable conditions like the ones during the Coronavirus 2019 (COVID-19) pandemic where many geoscience departments had their field trips hampered.

This study applied the Pearson correlation coefficient and principal component analysis as tools for unsupervised qualitative petroleum system evaluation techniques. A total of 252 oil samples (32 features per sample) representative of two Brazilian sedimentary basins (Recôncavo and Potiguar) were used to classify them according to their respective degrees of maturation and origin. The large initial set of variables comprises data on δ¹³C composition, saturate, aromatic, polar compound fractions, and the techniques reduced biomarkers to the most important variables, maintaining the global pattern of variance. The results were efficient in discriminating different petroleum systems from lacustrine, marine, and mixing sources, as observed in the studied accumulations from the Lower Cretaceous sediments of the Recôncavo and Potiguar basins. The methodology proved to be very useful to vene better characterize the petroleum systems. This methodology can be applied to analyze a large amount of oil samples, using simple software and spending relatively less time.