The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data
Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational powe...
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| Format: | Article |
| Language: | English |
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Syiah Kuala University
2021-04-01
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| Series: | Aceh International Journal of Science and Technology |
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| Online Access: | https://jurnal.usk.ac.id/AIJST/article/view/18749 |
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| author | Sudarmaji Saroji* Ekrar Winata Putra Pratama Wahyu Hidayat Suryo Prakoso Firman Herdiansyah |
| author_facet | Sudarmaji Saroji* Ekrar Winata Putra Pratama Wahyu Hidayat Suryo Prakoso Firman Herdiansyah |
| author_sort | Sudarmaji Saroji* |
| collection | DOAJ |
| description | Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns. |
| format | Article |
| id | doaj-art-815cddb18bd642498bbbd62962d5f49c |
| institution | DOAJ |
| issn | 2088-9860 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | Syiah Kuala University |
| record_format | Article |
| series | Aceh International Journal of Science and Technology |
| spelling | doaj-art-815cddb18bd642498bbbd62962d5f49c2025-08-20T02:58:29ZengSyiah Kuala UniversityAceh International Journal of Science and Technology2088-98602021-04-0110191710.13170/aijst.10.1.1874912687The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs DataSudarmaji Saroji*0Ekrar Winata1Putra Pratama Wahyu Hidayat2Suryo Prakoso3Firman Herdiansyah4Universitas Gadjah MadaUniversitas Gadjah MadaUniversitas Gadjah MadaUniversitas TrisaktiUniversitas TrisaktiLithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns.https://jurnal.usk.ac.id/AIJST/article/view/18749machine learning, lithofacies, well log, supervised learning |
| spellingShingle | Sudarmaji Saroji* Ekrar Winata Putra Pratama Wahyu Hidayat Suryo Prakoso Firman Herdiansyah The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data Aceh International Journal of Science and Technology machine learning, lithofacies, well log, supervised learning |
| title | The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data |
| title_full | The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data |
| title_fullStr | The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data |
| title_full_unstemmed | The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data |
| title_short | The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data |
| title_sort | implementation of machine learning in lithofacies classification using multi well logs data |
| topic | machine learning, lithofacies, well log, supervised learning |
| url | https://jurnal.usk.ac.id/AIJST/article/view/18749 |
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