Detecção e análise de redes de fraturas em afloramentos por métodos de visão computacional adaptativos

Abstract

The identification of fractures and discontinuities is of great importance in the estimation of fluid flow in hydrocarbon reservoirs, as they influence the porosity and permeability properties.Due to the inaccessibility and scarcity of reservoir data, fracture characterization is usually evaluated by studying outcrop analogues by remote sensing or in-situ observations by an expert. Considering the remote sensing methods, the acquisition of Unmanned Aerial Vehicles (UAV) combined with Structure from Motion photogrammetry (SfM) is a low-cost way to generate products such as orthorectified images, allowing manual and automated methods of detection of fracture designs and discontinuities to obtain discrete fracture network models(Discrete Fracture Networks - DFN). Computer vision and image processing approaches with the objective of segmenting the areas of interest by semantic segmentation or edge and valley detection, commonly used to detect and characterize the fracture network, have been used in the literature, but they have peculiarities or are optimized for each outcrop type and its peculiarities. The outcrops that have undergone a karstification process, mainly, show a high level of fracturing due to the dissolution caused by weathering and the subsequent breakage and erosion of the rocky medium. This scenario, together with the presence of vegetation and areas with irregular lighting or shade, contribute to the challenge of automatic fracture detection in outcrop images. The segmentation techniques by thresholding or binarization employed by previous works in fracture segmentation, bring the difficulty of establishing a global threshold applicable to the entire image without generating a large number of false positives and negatives in the detection. An alternative already used in biomedicine and character recognition is the use of local threshold adaptive segmentation techniques, which are the focus of this work. To optimize the detection of fractures in highly fractured karst regions, we propose the use and evaluation of these adaptive methods. In preliminary tests, the Sauvola local adaptive segmentation presented the best result when compared to the manually annotated ground truth. This work also proposes the use of binary noise reduction techniques to create the fracture segmentation method presented, which is complemented by a fracture segment detection method that identifies topological fracture data such as nodes and terminations. The results presented also bring the combination of UAV acquisitions at different times of the day to evaluate the influence of the position of the sun in the detection of fractures and the interpretation bias. This analysis is carried out on orthophotos of the outcrop of karstified carbonate rocks from Lajedo do Rosário, belonging to the Jandaíra formation, in Rio Grande do Norte. With the proposed methodology, we acquired more accurate fracture data over the study area, following directional statistics from previous works carried out in the region. In addition to the directional analysis, the DFN model and its length and opening statistics follow the expected distributions for this type of outcrop, while the fracture network connectivity is also analyzed. From the proposed methodology, it was possible to generate DFN models more faithful to the field truth, reducing the impact of external agents to the rocky environment such as the solar position and the presence of vegetation, providing more quality data for stochastic modeling and reservoir modeling.