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Ademir Marques
Vizlab - X-Reality and GeoInformatics Lab, UNISINOS, São Leopoldo, RS, Brazil

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Review
Published: 28 June 2020 in Earth-Science Reviews
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The study of outcrop analogues of petroleum reservoirs is well established in the petroleum industry through the use of digital outcrop models (DOMs). These models, which are also known as virtual outcrop models (VOMs) or 3D outcrops, are of great importance for understanding the behavior of actual reservoirs. This topic has been reviewed by many authors, and the studies vary in detail according to the technologies involved. The present study applies systematic review methodology traversing a number of articles to find the trends in studies utilizing DOMs. The articles included in this review indicate that the technologies used to generate DOMs are still predominantly classified as Light Detection and Ranging (LiDAR) and digital photogrammetry, with the first being present in most of the works, and the latter attracting attention owing to the popularity of unmanned aerial vehicles (UAVs). These studies have attracted a significant amount of attention to outcrop analysis, and the information acquired can be used to better fit reservoir simulations. Furthermore, a trend is identified with a focus on outcrop geometry and structural data. This work also discusses some of the available opportunities related to the generation of DOMs as well as emerging technologies that can improve the quality of the outcrop models in order to provide better reservoir simulations. Finally, this work discusses the findings and highlights of the articles answering the initially raised research questions.

ACS Style

Ademir Marques; Rafael Kenji Horota; Eniuce Menezes de Souza; Lucas Kupssinskü; Pedro Rossa; Alysson Soares Aires; Leonardo Bachi; Mauricio Roberto Veronez; Luiz Gonzaga; Caroline Lessio Cazarin. Virtual and digital outcrops in the petroleum industry: A systematic review. Earth-Science Reviews 2020, 208, 103260 .

AMA Style

Ademir Marques, Rafael Kenji Horota, Eniuce Menezes de Souza, Lucas Kupssinskü, Pedro Rossa, Alysson Soares Aires, Leonardo Bachi, Mauricio Roberto Veronez, Luiz Gonzaga, Caroline Lessio Cazarin. Virtual and digital outcrops in the petroleum industry: A systematic review. Earth-Science Reviews. 2020; 208 ():103260.

Chicago/Turabian Style

Ademir Marques; Rafael Kenji Horota; Eniuce Menezes de Souza; Lucas Kupssinskü; Pedro Rossa; Alysson Soares Aires; Leonardo Bachi; Mauricio Roberto Veronez; Luiz Gonzaga; Caroline Lessio Cazarin. 2020. "Virtual and digital outcrops in the petroleum industry: A systematic review." Earth-Science Reviews 208, no. : 103260.

Journal article
Published: 23 June 2020 in Sensors
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Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually lack the spatial resolution that suborbital sensors have. Many techniques have been developed to improve spatial resolution through data fusion. However, most of them have serious limitations regarding application and scale. Recently Super-Resolution (SR) convolution neural networks have been tested with encouraging results. However, they require large datasets, more time and computational power for training. To overcome these limitations, this work aims to increase the spatial resolution of multispectral bands from the Landsat satellite database using a modified artificial neural network that uses pixel kernels of a single spatial high-resolution RGB image from Google Earth as input. The methodology was validated with a common dataset of indoor images as well as a specific area of Landsat 8. Different downsized scale inputs were used for training where the validation used the ground truth of the original size images, obtaining comparable results to the recent works. With the method validated, we generated high spatial resolution spectral bands based on RGB images from Google Earth on a carbonated outcrop area, which were then properly classified according to the soil spectral responses making use of the advantage of a higher spatial resolution dataset.

ACS Style

Ademir Marques Junior; Eniuce Menezes De Souza; Marianne Müller; Diego Brum; Daniel Capella Zanotta; Rafael Kenji Horota; Lucas Silveira Kupssinskü; Maurício Roberto Veronez; Jr. Luiz Gonzaga; Caroline Lessio Cazarin. Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks. Sensors 2020, 20, 3559 .

AMA Style

Ademir Marques Junior, Eniuce Menezes De Souza, Marianne Müller, Diego Brum, Daniel Capella Zanotta, Rafael Kenji Horota, Lucas Silveira Kupssinskü, Maurício Roberto Veronez, Jr. Luiz Gonzaga, Caroline Lessio Cazarin. Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks. Sensors. 2020; 20 (12):3559.

Chicago/Turabian Style

Ademir Marques Junior; Eniuce Menezes De Souza; Marianne Müller; Diego Brum; Daniel Capella Zanotta; Rafael Kenji Horota; Lucas Silveira Kupssinskü; Maurício Roberto Veronez; Jr. Luiz Gonzaga; Caroline Lessio Cazarin. 2020. "Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks." Sensors 20, no. 12: 3559.

Journal article
Published: 28 May 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This paper proposes a technique named Printgrammetry, a structured workflow that allows the extraction of 3D models from Google Earth platform through the combination of image captures from the screen monitor with Structure from Motion algorithms. This technique was develop to help geologists and other geoscientists in acquiring 3D photo-realistic models of outcrops and natural landscapes of big proportions without the need of field mapping and expensive equipment. The methodology is detailed aiming to permit easy reproducibility and focused on achieving the highest resolution possible by working with the best images that the platform can provide. The results have shown that it is possible to obtain visually high quality models from natural landscapes from Google Earth by acquiring images at high Level of Detail regions of the software, using a 4K monitor, multi-directional screenshots and by marking homogeneously spaced targets for georeferencing and scaling. The geometric quality assessment performed using Light Detection and Ranging ground truth data as comparison shows that the Printgrammetry dense point clouds have reached 98.1\% of the total covered area under 5 meters of distance for the Half Dome case study and 96.7\% for the Raplee Ridge case study. The generated 3D models were then visualized and interacted through an immersive virtual reality software that allowed geologists to manipulate this virtual field environment in different scales. This technique is considered by the authors to have a promising potential for research, industrial and educational projects that doesn't requires highly precision models.

ACS Style

Rafael Kenji Horota; Alysson Soares Aires; Ademir Marques; Pedro Rossa; Eniuce Menezes De Souza; Luiz Gonzaga; Mauricio Roberto Veronez. Printgrammetry—3-D Model Acquisition Methodology From Google Earth Imagery Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 2819 -2830.

AMA Style

Rafael Kenji Horota, Alysson Soares Aires, Ademir Marques, Pedro Rossa, Eniuce Menezes De Souza, Luiz Gonzaga, Mauricio Roberto Veronez. Printgrammetry—3-D Model Acquisition Methodology From Google Earth Imagery Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):2819-2830.

Chicago/Turabian Style

Rafael Kenji Horota; Alysson Soares Aires; Ademir Marques; Pedro Rossa; Eniuce Menezes De Souza; Luiz Gonzaga; Mauricio Roberto Veronez. 2020. "Printgrammetry—3-D Model Acquisition Methodology From Google Earth Imagery Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 2819-2830.

Articles
Published: 01 January 2020 in European Journal of Remote Sensing
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Geometric accuracy is an important attribute of cartographic products and UAV photogrammetry has been gaining market in topographic mapping thanks to high spatial and temporal resolution, however, they need proper evaluation following accuracy standards and protocols. Regarding this, this work evaluates products from digital photogrammetry from images acquired with a fixed-wing UAV (18Mpixel camera) in a 300-380m height flight over a Hydroelectric Power Plant (HPP) in Brazil. A dataset of 23 ground control points assessed with an RTK-GNSS (using natural targets) was validated with its homologous in the Digital Surface Model (DSM) and the orthomosaic, following a workflow in which the appropriate statistics were applied. Following parametric tests like the Students t-test and the Chi-square, we compared the results with the Brazilian Cartographic Standard for digital cartography, achieving minimum scale of 1: 20,000 (RMSE of 1.04 m) for the orthomosaic, and minimum scale of 1: 10,000 (RMSE of 1.31 m) for the elevation in the DSM, although, no special targets were used. As the 3D mapping generated using the photogrammetry still needs a protocol to evaluate the accuracy, this work applied a proposed workflow respecting the quality of the data to meet the requirements of the cartographic standard.

ACS Style

Ademir Marques Junior; Dalva Maria De Castro; Taina Thomassin Guimarães; Leonardo Campos Inocencio; Maurício Roberto Veronez; Frederico Fábio Mauad; Luis Gonzaga Jr. Statistical assessment of cartographic product from photogrammetry and fixed-wing UAV acquisition. European Journal of Remote Sensing 2020, 53, 27 -39.

AMA Style

Ademir Marques Junior, Dalva Maria De Castro, Taina Thomassin Guimarães, Leonardo Campos Inocencio, Maurício Roberto Veronez, Frederico Fábio Mauad, Luis Gonzaga Jr. Statistical assessment of cartographic product from photogrammetry and fixed-wing UAV acquisition. European Journal of Remote Sensing. 2020; 53 (1):27-39.

Chicago/Turabian Style

Ademir Marques Junior; Dalva Maria De Castro; Taina Thomassin Guimarães; Leonardo Campos Inocencio; Maurício Roberto Veronez; Frederico Fábio Mauad; Luis Gonzaga Jr. 2020. "Statistical assessment of cartographic product from photogrammetry and fixed-wing UAV acquisition." European Journal of Remote Sensing 53, no. 1: 27-39.

Journal article
Published: 26 November 2018 in European Journal of Remote Sensing
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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.

ACS Style

Dalva Maria De Castro Vitti; Ademir Marques Junior; Taina Thomassin Guimarães; Emilie Caroline Koste; Leonardo Campos Inocencio; Maurício Roberto Veronez; Frederico Fábio Mauad. Geometry accuracy of DSM in water body margin obtained from an RGB camera with NIR band and a multispectral sensor embedded in UAV. European Journal of Remote Sensing 2018, 52, 160 -173.

AMA Style

Dalva Maria De Castro Vitti, Ademir Marques Junior, Taina Thomassin Guimarães, Emilie Caroline Koste, Leonardo Campos Inocencio, Maurício Roberto Veronez, Frederico Fábio Mauad. Geometry accuracy of DSM in water body margin obtained from an RGB camera with NIR band and a multispectral sensor embedded in UAV. European Journal of Remote Sensing. 2018; 52 (sup1):160-173.

Chicago/Turabian Style

Dalva Maria De Castro Vitti; Ademir Marques Junior; Taina Thomassin Guimarães; Emilie Caroline Koste; Leonardo Campos Inocencio; Maurício Roberto Veronez; Frederico Fábio Mauad. 2018. "Geometry accuracy of DSM in water body margin obtained from an RGB camera with NIR band and a multispectral sensor embedded in UAV." European Journal of Remote Sensing 52, no. sup1: 160-173.