Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin
Abstract Identifying oil spills in offshore production areas presents a critical challenge, requiring reliable and efficient methodologies to minimize environmental and economic impacts. Traditional approaches are often time-consuming, subjective, and limited in their ability to provide accurate pre...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-00084-5 |
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| author | Gil Marcio Avelino Silva Fernando Pellon de Miranda Jarbas Vicente Poley Guzzo Wagner Leonel Bastos Ygor Rocha Igor Viegas Alves Fernandes de Souza Italo Oliveira Matias Sarah Barron Torres Francisco Fabio de Araujo Ponte |
| author_facet | Gil Marcio Avelino Silva Fernando Pellon de Miranda Jarbas Vicente Poley Guzzo Wagner Leonel Bastos Ygor Rocha Igor Viegas Alves Fernandes de Souza Italo Oliveira Matias Sarah Barron Torres Francisco Fabio de Araujo Ponte |
| author_sort | Gil Marcio Avelino Silva |
| collection | DOAJ |
| description | Abstract Identifying oil spills in offshore production areas presents a critical challenge, requiring reliable and efficient methodologies to minimize environmental and economic impacts. Traditional approaches are often time-consuming, subjective, and limited in their ability to provide accurate predictions. This study introduces a novel methodology that integrates geochemical data analysis with machine learning techniques to enhance the identification of oil spill origins. A dataset comprising 2200 presalt oil samples and 75 attributes from the Santos Basin underwent preprocessing and exploratory analysis, resulting in 2137 samples and 62 predictive attributes. Seven machine learning algorithms were evaluated, with the random forest model achieving the highest classification accuracy of 91%. The methodology was validated using three independent oil samples (spill events and one natural seep), demonstrating its robustness in accurately predicting field origins with high confidence. The integration of machine learning techniques and geochemical analysis reduced the subjectivity of human interpretation, significantly accelerated diagnostic workflows, and provided reliable results in minutes. This approach represents a scalable and innovative solution for both exploratory and forensic geochemistry, particularly in complex production areas along the Brazilian coast. The proposed methodology has the potential to enhance decision-making processes in environmental monitoring and oil exploration. |
| format | Article |
| id | doaj-art-fe689754258141d79f4c86fe9a3bf819 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fe689754258141d79f4c86fe9a3bf8192025-08-20T01:47:33ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-00084-5Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basinGil Marcio Avelino Silva0Fernando Pellon de Miranda1Jarbas Vicente Poley Guzzo2Wagner Leonel Bastos3Ygor Rocha4Igor Viegas Alves Fernandes de Souza5Italo Oliveira Matias6Sarah Barron Torres7Francisco Fabio de Araujo Ponte8Petroleo Brasileiro S.A.Petroleo Brasileiro S.A.Petroleo Brasileiro S.A.Petroleo Brasileiro S.A.Petroleo Brasileiro S.A.Petroleo Brasileiro S.A.PUC-RioPUC-RioPUC-RioAbstract Identifying oil spills in offshore production areas presents a critical challenge, requiring reliable and efficient methodologies to minimize environmental and economic impacts. Traditional approaches are often time-consuming, subjective, and limited in their ability to provide accurate predictions. This study introduces a novel methodology that integrates geochemical data analysis with machine learning techniques to enhance the identification of oil spill origins. A dataset comprising 2200 presalt oil samples and 75 attributes from the Santos Basin underwent preprocessing and exploratory analysis, resulting in 2137 samples and 62 predictive attributes. Seven machine learning algorithms were evaluated, with the random forest model achieving the highest classification accuracy of 91%. The methodology was validated using three independent oil samples (spill events and one natural seep), demonstrating its robustness in accurately predicting field origins with high confidence. The integration of machine learning techniques and geochemical analysis reduced the subjectivity of human interpretation, significantly accelerated diagnostic workflows, and provided reliable results in minutes. This approach represents a scalable and innovative solution for both exploratory and forensic geochemistry, particularly in complex production areas along the Brazilian coast. The proposed methodology has the potential to enhance decision-making processes in environmental monitoring and oil exploration.https://doi.org/10.1038/s41598-025-00084-5 |
| spellingShingle | Gil Marcio Avelino Silva Fernando Pellon de Miranda Jarbas Vicente Poley Guzzo Wagner Leonel Bastos Ygor Rocha Igor Viegas Alves Fernandes de Souza Italo Oliveira Matias Sarah Barron Torres Francisco Fabio de Araujo Ponte Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin Scientific Reports |
| title | Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin |
| title_full | Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin |
| title_fullStr | Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin |
| title_full_unstemmed | Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin |
| title_short | Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin |
| title_sort | application of machine learning in forensic geochemistry using presalt oil samples from the santos basin |
| url | https://doi.org/10.1038/s41598-025-00084-5 |
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