Petroleum Geochemistry of South Atlantic Sedimentary Basins

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geochemistry".

Deadline for manuscript submissions: 10 July 2024 | Viewed by 3949

Special Issue Editors


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Center of Geosciences, University of Coimbra, Coimbra, Portugal
Interests: geochemistry; petroleum geology; gas seepage; stable and radioactive isotopes; biomarkers; medical geology
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Departamento de Geologia e Geofísica, Universidade Federal Fluminense, Niteroi, Brazil
Interests: paleoenvironmental reconstruction; oceanic anoxic events; organic facies modelling

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Programa de Geociências (Geoquímica), Universidade Federal Fluminense, Niterói, Brazil
Interests: paleoclimate; geochemistry

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Departament of Earth Sciences, Faculty of Sciences and Technology (Polo II), University of Coimbra, Rua Sílvio Lima, 3039-790 Coimbra, Portugal
Interests: sedimentary geology; basin analysis; petroleum geology; geoheritage
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Institute of Geosciences, University of São Paulo, São Paulo, Brazil
Interests: hydrothermal mineral systems; petroleum system; base-metal; fluid inclusion; Raman spectroscopy

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Guest Editor
Petrobras Research Center (CENPES), Rio de Janeiro, Brazil
Interests: biomarkers; petroleum system; gas geochemistry; geochemistry

Special Issue Information

Dear Colleagues,

The South America and Africa Atlantic conjugate margins contain more than 30 important sedimentary basins. The structural and tectonic evolution of these basins favoured the generation, migration and accumulation of giant petroleum reserves, which, since the 1950s have been the focus of several exploratory campaigns, and consequently, academic studies.

The deepening of geochemistry studies is relevant not only from a geological point of view, but also for environmental sciences, biochemistry, oceanography, climatology, hydrology, geophysics, etc. Thus, this Special Issue intends to increase and help to disseminate the knowledge on sedimentary basins in the South Atlantic, focusing on petroleum geochemistry information and data, through traditional and innovative methods, reviews, and any other relevant contributions from scholars around the world. We especially encourage the submission of articles on the following topics:

  • Advances in petroleum biomarkers studies of the South Atlantic basins;
  • Advances in hydrocarbons and associated gases characterization in the South Atlantic basins;
  • Characterization and simulation of oil and gas seeps across the South Atlantic conjugate margin;
  • Petroleum system characterization and simulation case studies;
  • Geochemical and basin modelling case studies;
  • Reservoir geochemistry case studies;
  • Sources of reduced sulphur (H2S) across the South Atlantic conjugate margin: bacterial versus thermochemical sulphate reduction;
  • Advances in the characterization and prediction of carbon dioxide (CO2) accumulation across the South Atlantic basins;
  • Fluid inclusions applications to reconstructing the history of the South Atlantic petroleum systems;
  • Advances in the understanding of hydrothermal and magmatic events linked to the evolution of the South Atlantic petroleum systems;
  • Paleoenvironmental reconstruction: implications for the South Atlantic petroleum systems;
  • Oceanic Anoxic Events (OAE) in the context of the South Atlantic conjugate margin;
  • Chronology of the petroleum systems of the South Atlantic basins.

Dr. Gabriel Barberes
Dr. Ana Luiza Spadano Albuquerque
Dr. Igor Martins Martins Venancio Padilha de Oliveira
Prof. Dr. Rui Pena Dos Reis
Dr. André Luiz Silva Pestilho
Dr. André Durante Spigolon
Guest Editors

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Keywords

  • petroleum system
  • geochemistry
  • South Atlantic Basins
  • sedimentary basins
  • conjugate margins

Published Papers (2 papers)

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12 pages, 2834 KiB  
Technical Note
Geochemical Biodegraded Oil Classification Using a Machine Learning Approach
by Sizenando Bispo-Silva, Cleverson J. Ferreira de Oliveira and Gabriel de Alemar Barberes
Geosciences 2023, 13(11), 321; https://doi.org/10.3390/geosciences13110321 - 24 Oct 2023
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Abstract
Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve [...] Read more.
Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of Convolutional Neural Networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one chromatographic oil images from different worldwide basins (Brazil, the USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations. Subsequently, the recurring features are grouped into common feature groups. The training result obtained an accuracy (CA) of 96.7% and an area under the ROC (Receiver Operating Characteristic) curve (AUC) of 99.7%. In turn, the test result obtained a 97.6% CA and a 99.7% AUC. This work suggests that the processing of petroleum chromatographic images through CNN can become a new tool for the study of petroleum geochemistry since the chromatograms can be loaded, read, grouped, and classified more efficiently and quickly than the evaluations applied in classical methods. Full article
(This article belongs to the Special Issue Petroleum Geochemistry of South Atlantic Sedimentary Basins)
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12 pages, 2713 KiB  
Technical Note
Pearson Correlation Coefficient Applied to Petroleum System Characterization: The Case Study of Potiguar and Reconcavo Basins, Brazil
by Érica T. Morais, Gabriel A. Barberes, Igor Viegas A. F. Souza, Fabiano G. Leal, Jarbas V. P. Guzzo and André L. D. Spigolon
Geosciences 2023, 13(9), 282; https://doi.org/10.3390/geosciences13090282 - 18 Sep 2023
Cited by 1 | Viewed by 1635
Abstract
This study applied the Pearson correlation coefficient and principal component analysis as tools for unsupervised qualitative petroleum system evaluation techniques. A total of 252 oil samples (32 features per sample) representative of two Brazilian sedimentary basins (Recôncavo and Potiguar) were used to classify [...] Read more.
This study applied the Pearson correlation coefficient and principal component analysis as tools for unsupervised qualitative petroleum system evaluation techniques. A total of 252 oil samples (32 features per sample) representative of two Brazilian sedimentary basins (Recôncavo and Potiguar) were used to classify them according to their respective degrees of maturation and origin. The large initial set of variables comprises data on δ13C composition, saturate, aromatic, polar compound fractions, and the techniques reduced biomarkers to the most important variables, maintaining the global pattern of variance. The results were efficient in discriminating different petroleum systems from lacustrine, marine, and mixing sources, as observed in the studied accumulations from the Lower Cretaceous sediments of the Recôncavo and Potiguar basins. The methodology proved to be very useful to vene better characterize the petroleum systems. This methodology can be applied to analyze a large amount of oil samples, using simple software and spending relatively less time. Full article
(This article belongs to the Special Issue Petroleum Geochemistry of South Atlantic Sedimentary Basins)
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