Machine Learning-Based Rock Facies Classification for Improved Reservoir Characterization in Niger Delta.

The Niger Delta, a cornerstone of Nigeria's oil and gas sector, plays a significant role in the Nation's energy landscape. This research concentrates on enhancing reservoir characterization, specifically emphasizing advancing rock facies classification. Employing advanced machine-learning methodologies and a dataset from 12 wells containing crucial well log parameters, such as Gamma Ray, Resistivity Micro-Spherical, Volume of Shale, Resistivity Deep, Resistivity Medium, Density, and Porosity, we conducted a rigorous evaluation of various classification models. The Random Forest algorithm emerged as the optimal choice, achieving an impressive F1 score of 0.93 and an accuracy of 0.93 on the cross-validation set. A meticulous analysis of identified facies classes, including Shale, Lower Shoreface, Middle Shoreface, Upper Shoreface, Transition Shoreface, Over Bank, and Channel, through confusion matrices, offered profound insights into the Model's efficiency. Feature importance analysis underscored the critical role of variables such as volume of shale, gamma ray, porosity, and bulk density in driving accurate predictions. This research significantly advances subsurface exploration in the Niger Delta, highlighting the effectiveness of machine learning for geologic characterization within the region's intricate geological landscape. [ABSTRACT FROM AUTHOR]