Facie classification of X-field in the Niger Delta using machine learning

EVWERHAMRE GODSPOWER CALVIN *  and JULIET EMUDIANUGHE

Department of Earth Sciences, Federal University of Petroleum Resources, Effurun, 330102, Delta, Nigeria.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 3556-3568.
Article DOI: 10.30574/ijsra.2024.13.2.2516
Publication history: 
Received on 16 November 2024; revised on 22 December 2024; accepted on 24 December 2024
 
Abstract: 
The Niger Delta's complex geology presents challenges for accurate facies classification and reservoir characterization. This study applies machine learning techniques to well log data from four wells in the X-Field: Freeman-001, Freeman-003ST1, Freeman-004ST1, and Freeman-005. Key features analyzed include Gamma Ray (GR), Sonic Transient Time (DT), Bulk Density (RHOB), and Neutron Porosity (NPHI). Unsupervised learning methods (K-means, GMM, and Agglomerative Clustering) yielded moderate clustering performance, with K-means achieving the highest accuracy of 47.87%, followed by GMM (20.38%) and Agglomerative Clustering (13.94%). Supervised learning methods significantly outperformed unsupervised ones. Random Forest achieved 87.06% accuracy, 87.07% precision, and an F1-score of 87.06%. Gradient Boosting delivered the best results, with 87.53% accuracy, 87.55% precision, and an F1-score of 87.54%. SVM showed lower performance, with 77.86% accuracy and an F1-score of 77.82%. These findings highlight the superior performance of supervised learning, particularly tree-based models, in facies classification and underscore the potential of machine learning to enhance reservoir characterization in the Niger Delta.
 
Keywords: 
Supervised Learning; Unsupervised learning; Facie Classification; Niger Delta
 
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