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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]

Rapid industrialization and oil exploration activities is believed to have influence on groundwater quality globally, and Niger Delta Region of Nigeria is no exception. Hence, this research is conducted to evaluate factors that influence groundwater origin and its geochemistry. For the purpose of this study, 20 groundwater samples were collected (4 from borehole and 16 from hand-dug wells). The parameters used in the assessment include physical parameters; pH, total dissolved solid and electrical conductivity and chemical parameters such as; major cations and anions. From the findings it was observed that pH values fell within the slightly acidic range with the exception of sample location HG/08 with value of 7.01 which can consider to be neutral. The dominant factors that influence groundwater origin and geochemistry within the study area are mainly precipitation and weathering. From Gibb’s plot ninety percent (90 %) of groundwater chemistry is influenced by precipitation. While, Soltan classification showed that 98 % of groundwater belongs to (Na+ – SO42¯), hence it can be classified as deep meteoric (precipitation influence), while the remaining (2%) is of (Na+ – HCO3¯) its classified as shallow meteoric type. Lastly from relationship between Cl¯/HCO3¯ groundwater were slightly moderately affected by saline water intrusion.