Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework
An accurate assessment of shale gas resources is highly important for the sustainable development of these energy resources. Total organic carbon (TOC) analysis thus becomes fundamental for understanding the distribution and quality of hydrocarbon source rocks within a shale gas reservoir. The eleva...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/45116/ http://umpir.ump.edu.my/id/eprint/45116/1/Data-driven%20total%20organic%20carbon%20prediction%20using%20feature%20selection.pdf |
| _version_ | 1848827255216668672 |
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| author | Macêdo, Bruno da Silva Wayo, Dennis Delali Kwesi Campos, Deivid De Santis, Rodrigo Barbosa Martinho, Alfeu Dias Yaseen, Zaher Mundher Saporetti, Camila M. Goliatt, Leonardo |
| author_facet | Macêdo, Bruno da Silva Wayo, Dennis Delali Kwesi Campos, Deivid De Santis, Rodrigo Barbosa Martinho, Alfeu Dias Yaseen, Zaher Mundher Saporetti, Camila M. Goliatt, Leonardo |
| author_sort | Macêdo, Bruno da Silva |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | An accurate assessment of shale gas resources is highly important for the sustainable development of these energy resources. Total organic carbon (TOC) analysis thus becomes fundamental for understanding the distribution and quality of hydrocarbon source rocks within a shale gas reservoir. The elevation of the TOC is often associated with the presence of source rocks, indicating the potential for oil and gas production. TOC assessment is performed using laboratory methods, which can be time-consuming and costly. Data-driven models have been successfully applied to model the relationship between TOC and other constituents and to predict the TOC content. However, these methods depend on extensive parameter adjustments that must be carefully conducted in different sedimentary environments. In this context, Automated Machine Learning (AutoML) is an alternative for accurately predicting TOCs, saving time-consuming fine-tuning steps in model development. This study aims to develop an AutoML strategy for estimating TOC using well log data. This procedure automatically preprocesses the search for the best method parameters, reducing the execution time. Among the methods evaluated, Extremely Randomized Trees (XT) performed best (R = 0.8632, MSE = 0.1806) in the test set. The proposed strategy provides a powerful data-driven method, which allows real-world use of the well to assist in data analysis and subsequent decision-making. |
| first_indexed | 2025-11-15T03:57:48Z |
| format | Article |
| id | ump-45116 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:57:48Z |
| publishDate | 2025 |
| publisher | Nature Publishing Group |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-451162025-07-18T07:05:20Z http://umpir.ump.edu.my/id/eprint/45116/ Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework Macêdo, Bruno da Silva Wayo, Dennis Delali Kwesi Campos, Deivid De Santis, Rodrigo Barbosa Martinho, Alfeu Dias Yaseen, Zaher Mundher Saporetti, Camila M. Goliatt, Leonardo QA75 Electronic computers. Computer science TD Environmental technology. Sanitary engineering TP Chemical technology An accurate assessment of shale gas resources is highly important for the sustainable development of these energy resources. Total organic carbon (TOC) analysis thus becomes fundamental for understanding the distribution and quality of hydrocarbon source rocks within a shale gas reservoir. The elevation of the TOC is often associated with the presence of source rocks, indicating the potential for oil and gas production. TOC assessment is performed using laboratory methods, which can be time-consuming and costly. Data-driven models have been successfully applied to model the relationship between TOC and other constituents and to predict the TOC content. However, these methods depend on extensive parameter adjustments that must be carefully conducted in different sedimentary environments. In this context, Automated Machine Learning (AutoML) is an alternative for accurately predicting TOCs, saving time-consuming fine-tuning steps in model development. This study aims to develop an AutoML strategy for estimating TOC using well log data. This procedure automatically preprocesses the search for the best method parameters, reducing the execution time. Among the methods evaluated, Extremely Randomized Trees (XT) performed best (R = 0.8632, MSE = 0.1806) in the test set. The proposed strategy provides a powerful data-driven method, which allows real-world use of the well to assist in data analysis and subsequent decision-making. Nature Publishing Group 2025 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/45116/1/Data-driven%20total%20organic%20carbon%20prediction%20using%20feature%20selection.pdf Macêdo, Bruno da Silva and Wayo, Dennis Delali Kwesi and Campos, Deivid and De Santis, Rodrigo Barbosa and Martinho, Alfeu Dias and Yaseen, Zaher Mundher and Saporetti, Camila M. and Goliatt, Leonardo (2025) Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework. Scientific Reports, 15 (1). pp. 1-19. ISSN 2045-2322. (Published) https://doi.org/10.1038/s41598-025-91224-4 https://doi.org/10.1038/s41598-025-91224-4 |
| spellingShingle | QA75 Electronic computers. Computer science TD Environmental technology. Sanitary engineering TP Chemical technology Macêdo, Bruno da Silva Wayo, Dennis Delali Kwesi Campos, Deivid De Santis, Rodrigo Barbosa Martinho, Alfeu Dias Yaseen, Zaher Mundher Saporetti, Camila M. Goliatt, Leonardo Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework |
| title | Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework |
| title_full | Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework |
| title_fullStr | Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework |
| title_full_unstemmed | Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework |
| title_short | Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework |
| title_sort | data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework |
| topic | QA75 Electronic computers. Computer science TD Environmental technology. Sanitary engineering TP Chemical technology |
| url | http://umpir.ump.edu.my/id/eprint/45116/ http://umpir.ump.edu.my/id/eprint/45116/ http://umpir.ump.edu.my/id/eprint/45116/ http://umpir.ump.edu.my/id/eprint/45116/1/Data-driven%20total%20organic%20carbon%20prediction%20using%20feature%20selection.pdf |