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.

The identification of fractures and discontinuities has great importance on the fluid flow estimation in hydrocarbon reservoirs since they influence the properties of porosity and permeability. Due to the inaccessibility and sparsity of reservoir data, the fracture characterization is generally assessed through the study of outcrop analogues using remote sensing or in situ observations by a specialist. Considering the remote sensing methods, the unmanned Aerial Vehicle (UAV) acquisition combined with Structure from Motion (SfM) photogrammetry is a low-cost way to generate products like orthorectified images, allowing manual and automated methods of fracture trace detection. Automatic approaches, commonly used to address this problem, present some known limitations and disadvantages due to the nature of the outcrops and weather conditions during UAV acquisitions. In this work, we focus on fracture detection over karstic regions that are highly fractured. For this, we evaluated a series of adaptive segmentation methods based on thresholding. The Sauvola local adaptive segmentation presented the best result when compared to a manually annotated ground truth. The segmentation results were further improved by the use of the binary denoising method Non-Local means. We also carried an evaluation of the influence of the sun position in the fracture detection, and to reduce this inherent bias we combined three UAV acquisitions done over the karstic carbonate outcrop, namely Rosário pavement in the Jandaíra formation northeast Brazil. With the proposed methodology we acquired more accurate fracture data over the study area, which follows the directional statistics of previous works carried out in the region.

Lithological characterization plays a crucial role in geological studies, and outcrops serve as the primary source of geological information. Automatic identification of lithologies enhances geological mapping and reduces costs and risks associated with mapping less accessible outcrops. These outcrops are typically imaged using remote sensing techniques, enabling the identification of geological structures and lithologies through computer vision and machine learning approaches. In this context, Convolutional Neural Networks (CNNs) have significantly contributed to lithological characterization in outcrop images. Recent advancements include novel architectures based on residual and attention blocks, which improve upon base CNN models. Additionally, transformer-based architectures have surpassed CNNs in various tasks. Taking a step further, we propose a novel architecture that incorporates Fourier operators. Our proposed architecture builds upon the Transformer model, utilizing a sequential combination of Fourier Neural Operators and Channel-Wise self-attention layers. To train our model, we adopt a transfer learning strategy, initially training it on a texture dataset with 47 classes. Subsequently, we fine-tune the same model to classify five specific lithologies in our custom dataset. These lithologies include sandstone, gray and brownish-gray shale, limestone, and laminated limestone images from the Tres Irmãos quarry within the Araripe Basin—an outcrop analogous to oil exploration reservoirs. The proposed architecture achieved an F1-score of up to 98%, performing better than reference CNN and ResNet models. This advancement holds promise for accurate lithological characterization, benefiting geological research and exploration efforts.

The photogrammetry techniques are known to be accessible due to its low cost, while the geometric accuracy is a key point to ensure that models obtained from photogrammetry are a feasible solution. This work evaluated the discrepancies in 3D (DSM) and 2D (orthomosaic) models obtained from photogrammetry using control points (GCPs) near a reflective/refractive area (water body), where the objective was to evaluate these points, analysing the independence, normality and randomness and other basic statistic. The images were obtained with a 16 MP Canon PowerShot ELPH 110S with a modified NiR band and a multispectral sensor Parrot Sequoia, both embedded in a hex-rotor UAV in flight over the Unisinos University’s artificial lake in the city of São Leopoldo, Rio Grande do Sul, Brazil. Due the distribution of the data found to be not normal, we applied non-parametric tests Chebyshev’s Theorem and the Mann–Whitney’s U test, where it showed that the values obtained from Sequoia DSM presented significant similarities with the values obtained from the GCP’s considering the confidence level of 95%; however, this was not confirmed for the model generated from a Canon camera, showing that we found better results using the multispectral Parrot Sequoia.

The accurate characterization of carbonate rocks is crucial for determining the potential for exploring outcrops in the oil industry. Modern remote sensing instruments have played an important role in estimating mineral characteristics in the most varied scales. However, mathematical expressions suggested for converting radiance values to mineral information are often designed to work in only one particular territory. At the same time, the few general solutions existing tend to achieve overall poor results. In this paper, we propose an adaptive approach to estimate subpixel amounts of carbonate rocks using OLI-Landsat 8 image data. The main formulation is derived from exhaustive analysis of the spectral behavior of several carbonate materials. The formulation is subsequently adapted through the use of a genetic algorithm to work on diverse situations. Experiments have demonstrated the adaptability of the method for different environments, as well as its higher performance when compared with existing carbonate indices.