Enhancing Facies Classification in Geological Studies Through Artificial Neural Networks: A Review
Geological studies rely heavily on facies classification since it offers vital information for reservoir characterization and hydrocarbon exploitation. Because facies are inherently complex and heterogeneous, traditional approaches frequently struggle to categorize them effectively. Artificial Neura...
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Shahid Beheshti University
2024-10-01
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| Series: | Sustainable Earth Trends |
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| Online Access: | https://sustainearth.sbu.ac.ir/article_104401_9a3e5eb33190cb6bbb075144fdec7cc3.pdf |
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| author | Ofoh Juliana Onyekuru Okechuwu Ikoro Diugo Opara Iheanyichukwu Njoku I.O Okereke Chikwendu Akakuru Chigozie |
| author_facet | Ofoh Juliana Onyekuru Okechuwu Ikoro Diugo Opara Iheanyichukwu Njoku I.O Okereke Chikwendu Akakuru Chigozie |
| author_sort | Ofoh Juliana |
| collection | DOAJ |
| description | Geological studies rely heavily on facies classification since it offers vital information for reservoir characterization and hydrocarbon exploitation. Because facies are inherently complex and heterogeneous, traditional approaches frequently struggle to categorize them effectively. Artificial Neural Networks (ANNs) have shown great promise in recent years for improving the efficiency and accuracy of facies classification. This review assesses ANN applications for facies classification in geological investigations critically and it begins by delineating the essential principles of facies classification and the constraints of traditional methodologies. Then ANNs' theoretical underpinnings and applicability to tasks involving the classification of facies was explored. The different architectures and configurations of ANNs used in geological research were also examined, as well as the benefits and difficulties of their use. The several ANNs architectures and configurations used in geological research are examined, as well as the benefits and difficulties of putting them into practice. In order to enhance the efficacy of ANNs in facies classification, the paper also addresses the integration of auxiliary data sources, such as well logs, seismic, and core data. Furthermore, the application of new developments in Deep Learning methods, including Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), to facies classification were discussed. To guarantee solid and trustworthy classification results, factors including feature selection, data preparation, and model assessment metrics were also taken into account. Lastly, the review highlights possible avenues for future research and breakthroughs in leveraging ANNs for enhanced facies classification, precision and effectiveness in geological studies. |
| format | Article |
| id | doaj-art-d097c9a5b0ea4722a38e6b11d428aff2 |
| institution | Kabale University |
| issn | 3060-6225 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Shahid Beheshti University |
| record_format | Article |
| series | Sustainable Earth Trends |
| spelling | doaj-art-d097c9a5b0ea4722a38e6b11d428aff22024-12-16T12:52:56ZengShahid Beheshti UniversitySustainable Earth Trends3060-62252024-10-0134314510.48308/ser.2024.234950.1041104401Enhancing Facies Classification in Geological Studies Through Artificial Neural Networks: A ReviewOfoh Juliana0Onyekuru Okechuwu1Ikoro Diugo2Opara Iheanyichukwu3Njoku I.O4Okereke Chikwendu5Akakuru Chigozie6Department of Geology, Federal University of Technology, Owerri, P.M.B. 1526, Imo State NigeriaDepartment of Geology, Federal University of Technology, Owerri, P.M.B. 1526, Imo State NigeriaDepartment of Geology, Federal University of Technology, Owerri, P.M.B. 1526, Imo State NigeriaDepartment of Geology, Federal University of Technology, Owerri, P.M.B. 1526, Imo State NigeriaDepartment of Geology, Federal University of Technology, Owerri, P.M.B. 1526, Imo State NigeriaDepartment of Geology, Federal University of Technology, Owerri, P.M.B. 1526, Imo State NigeriaDepartment of Geology, Federal University of Technology, Owerri, P.M.B. 1526, Imo State NigeriaGeological studies rely heavily on facies classification since it offers vital information for reservoir characterization and hydrocarbon exploitation. Because facies are inherently complex and heterogeneous, traditional approaches frequently struggle to categorize them effectively. Artificial Neural Networks (ANNs) have shown great promise in recent years for improving the efficiency and accuracy of facies classification. This review assesses ANN applications for facies classification in geological investigations critically and it begins by delineating the essential principles of facies classification and the constraints of traditional methodologies. Then ANNs' theoretical underpinnings and applicability to tasks involving the classification of facies was explored. The different architectures and configurations of ANNs used in geological research were also examined, as well as the benefits and difficulties of their use. The several ANNs architectures and configurations used in geological research are examined, as well as the benefits and difficulties of putting them into practice. In order to enhance the efficacy of ANNs in facies classification, the paper also addresses the integration of auxiliary data sources, such as well logs, seismic, and core data. Furthermore, the application of new developments in Deep Learning methods, including Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), to facies classification were discussed. To guarantee solid and trustworthy classification results, factors including feature selection, data preparation, and model assessment metrics were also taken into account. Lastly, the review highlights possible avenues for future research and breakthroughs in leveraging ANNs for enhanced facies classification, precision and effectiveness in geological studies.https://sustainearth.sbu.ac.ir/article_104401_9a3e5eb33190cb6bbb075144fdec7cc3.pdfartificial neural networkdeep learningfaciesmachine learningreservoir |
| spellingShingle | Ofoh Juliana Onyekuru Okechuwu Ikoro Diugo Opara Iheanyichukwu Njoku I.O Okereke Chikwendu Akakuru Chigozie Enhancing Facies Classification in Geological Studies Through Artificial Neural Networks: A Review Sustainable Earth Trends artificial neural network deep learning facies machine learning reservoir |
| title | Enhancing Facies Classification in Geological Studies Through Artificial Neural Networks: A Review |
| title_full | Enhancing Facies Classification in Geological Studies Through Artificial Neural Networks: A Review |
| title_fullStr | Enhancing Facies Classification in Geological Studies Through Artificial Neural Networks: A Review |
| title_full_unstemmed | Enhancing Facies Classification in Geological Studies Through Artificial Neural Networks: A Review |
| title_short | Enhancing Facies Classification in Geological Studies Through Artificial Neural Networks: A Review |
| title_sort | enhancing facies classification in geological studies through artificial neural networks a review |
| topic | artificial neural network deep learning facies machine learning reservoir |
| url | https://sustainearth.sbu.ac.ir/article_104401_9a3e5eb33190cb6bbb075144fdec7cc3.pdf |
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