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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.
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 StyleAdemir 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 StyleAdemir 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.
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.
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 StyleAdemir 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 StyleAdemir 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.
Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8.
Lucas Silveira Kupssinskü; Tainá Thomassim Guimarães; Eniuce Menezes De Souza; Daniel C. Zanotta; Mauricio Roberto Veronez; Jr. Luiz Gonzaga; Frederico Fábio Mauad. A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning. Sensors 2020, 20, 2125 .
AMA StyleLucas Silveira Kupssinskü, Tainá Thomassim Guimarães, Eniuce Menezes De Souza, Daniel C. Zanotta, Mauricio Roberto Veronez, Jr. Luiz Gonzaga, Frederico Fábio Mauad. A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning. Sensors. 2020; 20 (7):2125.
Chicago/Turabian StyleLucas Silveira Kupssinskü; Tainá Thomassim Guimarães; Eniuce Menezes De Souza; Daniel C. Zanotta; Mauricio Roberto Veronez; Jr. Luiz Gonzaga; Frederico Fábio Mauad. 2020. "A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning." Sensors 20, no. 7: 2125.
Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies. These sensors are embedded in aircrafts and satellites like the Landsat, which has more data freely available but lack the spatial resolution that suborbital sensors have. To increase the spatial resolution, a series of techniques have been developed like pansharpenning data fusion and more advanced convolutional neural networks for super-resolution, however, the later requires large datasets. To overcome this requirement, this work aims to increase the spatial resolution of Landsat spectral bands using artificial neural networks that uses pixel kernels of a single high-resolution image from Google Earth. Using this method, the high-resolution spectral bands were generated with pixel size of 1m in contrast to the 15m of pansharpenned Landsat bands. The evaluate the predicted spectral bands the validation measures Universal Quality Index (UQI) and Spectral Angle Mapper (SAM) were used, showing values of 0.98 and 0.16 respectively, presenting good results.
Ademir Marques; Pedro Rossa; Rafael Kenji Horota; Diego Brum; Eniuce Menezes De Souza; Alyson Soares Aires; Lucas Kupssinsku; Mauricio Roberto Veronez; Luis Gonzaga; Caroline Lessio Cazarin. Improving spatial resolution of LANDSAT spectral bands from a single RGB image using artificial neural network. 2019 13th International Conference on Sensing Technology (ICST) 2019, 1 -6.
AMA StyleAdemir Marques, Pedro Rossa, Rafael Kenji Horota, Diego Brum, Eniuce Menezes De Souza, Alyson Soares Aires, Lucas Kupssinsku, Mauricio Roberto Veronez, Luis Gonzaga, Caroline Lessio Cazarin. Improving spatial resolution of LANDSAT spectral bands from a single RGB image using artificial neural network. 2019 13th International Conference on Sensing Technology (ICST). 2019; ():1-6.
Chicago/Turabian StyleAdemir Marques; Pedro Rossa; Rafael Kenji Horota; Diego Brum; Eniuce Menezes De Souza; Alyson Soares Aires; Lucas Kupssinsku; Mauricio Roberto Veronez; Luis Gonzaga; Caroline Lessio Cazarin. 2019. "Improving spatial resolution of LANDSAT spectral bands from a single RGB image using artificial neural network." 2019 13th International Conference on Sensing Technology (ICST) , no. : 1-6.
Total suspended solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, the technique proposed in this paper takes another approach. TSS and chlorophyll-a are optically active components therefore enable measures through remote sensing. Using data from both Sentinel-2 spectral images and laboratory analysis, an artificial neural network was trained to predict the concentration of TSS and chlorophyll-a. The predictions were evaluated using the R2 coefficient, where TSS and chlorophyll-a achieved values of 0.7 and 0.72, respectively.
Lucas Kupssinskü; Taina Thomassim Guimaraes; Rafael De Freitas; Eniuce Menezes De Souza; Pedro Rossa; Ademir Marques; Mauricio Roberto Veronez; Luiz Gonzaga; Caroline Lessio Cazarin; Frederico Fabio Mauad. Prediction of chlorophyll-a and suspended solids through remote sensing and artificial neural networks. 2019 13th International Conference on Sensing Technology (ICST) 2019, 1 -6.
AMA StyleLucas Kupssinskü, Taina Thomassim Guimaraes, Rafael De Freitas, Eniuce Menezes De Souza, Pedro Rossa, Ademir Marques, Mauricio Roberto Veronez, Luiz Gonzaga, Caroline Lessio Cazarin, Frederico Fabio Mauad. Prediction of chlorophyll-a and suspended solids through remote sensing and artificial neural networks. 2019 13th International Conference on Sensing Technology (ICST). 2019; ():1-6.
Chicago/Turabian StyleLucas Kupssinskü; Taina Thomassim Guimaraes; Rafael De Freitas; Eniuce Menezes De Souza; Pedro Rossa; Ademir Marques; Mauricio Roberto Veronez; Luiz Gonzaga; Caroline Lessio Cazarin; Frederico Fabio Mauad. 2019. "Prediction of chlorophyll-a and suspended solids through remote sensing and artificial neural networks." 2019 13th International Conference on Sensing Technology (ICST) , no. : 1-6.
Pedro Rossa; Rafael Kenji Horota; Alysson Soares Aires; Lucas Kupssinskü; Carolina Jung Kremer; Eniuce Menezes De Souza; Ademir Marques; Luiz Gonzaga Jr; Mauricio Roberto Veronez; Caroline Lessio Cazarin. VROffice. Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2019, 1 .
AMA StylePedro Rossa, Rafael Kenji Horota, Alysson Soares Aires, Lucas Kupssinskü, Carolina Jung Kremer, Eniuce Menezes De Souza, Ademir Marques, Luiz Gonzaga Jr, Mauricio Roberto Veronez, Caroline Lessio Cazarin. VROffice. Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2019; ():1.
Chicago/Turabian StylePedro Rossa; Rafael Kenji Horota; Alysson Soares Aires; Lucas Kupssinskü; Carolina Jung Kremer; Eniuce Menezes De Souza; Ademir Marques; Luiz Gonzaga Jr; Mauricio Roberto Veronez; Caroline Lessio Cazarin. 2019. "VROffice." Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems , no. : 1.
In a geological study, an important step is to determine the type of sedimentary rock or its grain size. Such a determination requires accurate analysis in the field or in a laboratory. As the size of the study area grows, this activity can be time consuming and error prone because the number of specialists working under rigid criteria also increases. This paper proposes a novel methodology to classify grain size using unique wavelength reflectance data and artificial neural networks. The results indicate that the proposed method can be reliably used in the field.
Rodrigo Marques Figueiredo; Mauricio Roberto Veronez; Francisco Manoel Wohnrath; Marcio Rosa Da Silva; Luiz Gonzaga; Lucas Kupssinskü; Fabiane Bordin; Diego Brum; Caroline Lessio Cazarin. Artificial neural network–based method to classify sedimentary rocks. 2018 12th International Conference on Sensing Technology (ICST) 2018, 282 -286.
AMA StyleRodrigo Marques Figueiredo, Mauricio Roberto Veronez, Francisco Manoel Wohnrath, Marcio Rosa Da Silva, Luiz Gonzaga, Lucas Kupssinskü, Fabiane Bordin, Diego Brum, Caroline Lessio Cazarin. Artificial neural network–based method to classify sedimentary rocks. 2018 12th International Conference on Sensing Technology (ICST). 2018; ():282-286.
Chicago/Turabian StyleRodrigo Marques Figueiredo; Mauricio Roberto Veronez; Francisco Manoel Wohnrath; Marcio Rosa Da Silva; Luiz Gonzaga; Lucas Kupssinskü; Fabiane Bordin; Diego Brum; Caroline Lessio Cazarin. 2018. "Artificial neural network–based method to classify sedimentary rocks." 2018 12th International Conference on Sensing Technology (ICST) , no. : 282-286.
The main goal of this paper was to evaluate the use of a low cost immersive driving simulator to improve the teaching learning process of the Transport Infrastructure undergraduate course. The driving simulator that was developed in a virtual reality environment to assist both the teaching of engineering and the research on road safety. An experiment was conducted in Transport Infrastructure 1 course for Civil Engineering students in a Brazilian university. The students developed a geometric design of a road that was posteriorly modeled in 3D and provided in simulator. Students piloted a vehicle in the immersive simulator in the same road that they designed. Subsequently the usability of the system was assessed by the SUS metric (System Usability Scale). We performed an evaluation with 52 users and the SUS metric that we found was of 73% assuring a degree of usability above average and demonstrating that the immersive system is good to be used as a complementary tool in the learning of transport infrastructure.
Mauricio R. Veronez; Luiz Gonzaga; Fabiane Bordin; Lucas Kupssinsku; Gabriel Lanzer Kannenberg; Tiago Duarte; Leonardo G. Santana; Lean Luca De Fraga; Demetrius Nunes Alves; Fernando Pinho Marson. RIDERS: Road Inspection & Driver Simulation. 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) 2018, 715 -716.
AMA StyleMauricio R. Veronez, Luiz Gonzaga, Fabiane Bordin, Lucas Kupssinsku, Gabriel Lanzer Kannenberg, Tiago Duarte, Leonardo G. Santana, Lean Luca De Fraga, Demetrius Nunes Alves, Fernando Pinho Marson. RIDERS: Road Inspection & Driver Simulation. 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). 2018; ():715-716.
Chicago/Turabian StyleMauricio R. Veronez; Luiz Gonzaga; Fabiane Bordin; Lucas Kupssinsku; Gabriel Lanzer Kannenberg; Tiago Duarte; Leonardo G. Santana; Lean Luca De Fraga; Demetrius Nunes Alves; Fernando Pinho Marson. 2018. "RIDERS: Road Inspection & Driver Simulation." 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) , no. : 715-716.
Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R2 values of greater than 0.60, consistent with literature values.
Maurício R. Veronez; Lucas S. Kupssinskü; Tainá T. Guimarães; Emilie C. Koste; Juarez M. Da Silva; Laís V. De Souza; William F. M. Oliverio; Rogélio S. Jardim; Ismael É. Koch; Jonas G. De Souza; Jr. Luiz Gonzaga; Frederico F. Mauad; Leonardo C. Inocencio; Fabiane Bordin. Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks. Sensors 2018, 18, 159 .
AMA StyleMaurício R. Veronez, Lucas S. Kupssinskü, Tainá T. Guimarães, Emilie C. Koste, Juarez M. Da Silva, Laís V. De Souza, William F. M. Oliverio, Rogélio S. Jardim, Ismael É. Koch, Jonas G. De Souza, Jr. Luiz Gonzaga, Frederico F. Mauad, Leonardo C. Inocencio, Fabiane Bordin. Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks. Sensors. 2018; 18 (1):159.
Chicago/Turabian StyleMaurício R. Veronez; Lucas S. Kupssinskü; Tainá T. Guimarães; Emilie C. Koste; Juarez M. Da Silva; Laís V. De Souza; William F. M. Oliverio; Rogélio S. Jardim; Ismael É. Koch; Jonas G. De Souza; Jr. Luiz Gonzaga; Frederico F. Mauad; Leonardo C. Inocencio; Fabiane Bordin. 2018. "Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks." Sensors 18, no. 1: 159.