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.

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.

Traffic accidents could often be avoided with more in-depth studies of traffic and the geometric layout, using, for example, driving simulators to simulate traffic conditions. The objective of this study is to qualitatively evaluate three types of visualization techniques for examining a road project (one in a 2D printed project and the others using a driving simulator in a virtual immersion system and screen visualization). The results were evaluated by the Analytic Hierarchy Process-AHP method, used to establish different weights for the analyzed variables. For this, a questionnaire was applied to undergraduate students in Civil Engineering to compare the techniques. The results show that the immersive simulation visualization has sufficient quality and can contribute to the validation of geometric designs.

Considering all the elements related to the occurrence of accidents, an important aspect is to identify the drivers most prone to accidents. This study aims to evaluate the behavior of drivers concerning gender and age. For this, a stretch of highway with a dangerous curve was modeled in a simulator using an immersive virtual environment and 59 people experienced the simulation. The car speed was registered along the route and from a segmented regression model, the behavior for both groups was evaluated regarding average speed. The results showed that older drivers (26 to 35 years old) of the female gender slowed the car before the other profile groups. Also, younger drivers (18 to 25 years old) took longer to slow down and had more accidents in the simulations. These results show that there are differences in driver’s behavior concerning gender and age.