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.

Fracture modeling plays a valuable role to understand the fluid flow in carbonate reservoirs. For this, the fracture characterization to generate Discrete Fracture Networks (DFNs) can take advantage of analogue outcrops through Virtual Outcrop Models (VOMs), acquired by Unmanned Aerial Vehicles (UAV) and digital photogrammetry. The stochastic DFN generation is an important step in reservoir modeling as it brings more representative data to the process and has long been studied. However, optimizations concerning automatizing some of the steps necessary to its generation like data clustering are still open to advancements. In this sense, this work aims the fracture data clustering and the definition of the number of clusters when gathering data for the stochastic process, developing an Elbow method for spherical data and a balanced K-means, both based on Fisher statistics. For this, we interpreted fracture planes in a VOM that recreates a carbonate reservoir analogue from the Jandaíra Formation, in the Northeast, Brazil. As result, we show a workflow for immersive fracture interpretation alongside a 3D stochastic DFN model with fracture intensity of 22.57m ⁻¹ for cell sizes of 1m ³ . Regarding the clustering balance, our method achieved a lower standard deviation between sets while maintaining the Fisher values greater to obtain fracture sets with lower dispersion. Additionally, the Elbow method implementation proved a beneficial step to the workflow as it reduced the interpretation bias of family clusters. These results alongside the proposed workflow bring a better understanding of the outcrop geometry while offering data scalability for reservoir modeling.