Core-Calibrated Machine Learning–Based Permeability Estimation: A Western Niger Delta Case Study
Accurate permeability estimation is essential for optimum reservoir characterization, simulation, and development planning; however, its prediction remains challenging due to the limited availability of core-derived permeability measurements, often constrained by acquisition cost and data coverage. This study presents an integrated machine learning workflow for permeability prediction calibrated against core permeability data using wireline log information from wells located in western Niger Delta. The workflow involved systematic data preprocessing, depth-based alignment of core and well log data, and feature engineering to derive additional petrophysical attributes relevant to fluid flow behavior. Key input variables used in the model include Gamma Ray (GR), Bulk Density (RHOB), Resistivity, Neutron Porosity, and several derived parameters such as shale volume, resistivity index, effective porosity, neutron–density separation, and bulk volume water. Permeability values were transformed into logarithmic space to improve modeling stability and capture the wide range of permeability values typical of heterogeneous clastic reservoirs. Five (5) supervised machine learning algorithms; Random Forest (RF), Extreme Gradient Boosting (XGB), Extra Trees Model (ETM), AdaBoost (ADB) and Decision Tree (DT) were developed and evaluated using an 80–20% train–test split, with blind test well excluded for validation against measured core permeability. The Extra Trees model demonstrated the highest predictive performance, achieving an R² value of ~ 0.9164, Mean Absolute Error (MAE) of ~ 1.5392, and Root Mean Squared Error (RMSE) of ~ 4.1011, indicative of high correlation between predicted and core-measured permeability. The result visualized predicted versus actual permeability along the depth axis, providing a vertical reservoir-scale understanding of permeability distribution. The results indicate that machine learning models can effectively capture complex petrophysical relationships and provide reliable permeability estimates in intervals lacking core measurements.