Enhancing Facies Classification in Geological Studies Through Artificial Neural Networks: A Review

Geological studies rely heavily on facies categorization 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 categorization in geological investigations critically. The introductory section delineates the essential principles of facies classification and the constraints of traditional methodologies. The article then explores the ANNs' theoretical underpinnings and applicability to tasks involving the classification of facies. The different architectures and configurations of Artificial Neural Networks (ANNs) used in geological research are examined, as well as the benefits and difficulties of their use. The article then explores the theoretical underpinnings of ANNs and whether or not they are appropriate for tasks involving the classification of facies. The several ANN 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 characteristics, and core samples. In summary, this review highlights the importance of Artificial Neural Networks (ANNs) as potent instruments for enhancing the precision and effectiveness of facies classification, thereby promoting progress in geological exploration and reservoir characterization.

کلیدواژه ها:

Facies ، Reservoir ، Deep Learning ، Artificial Neural Network ، Machine Learning