Quantitative geophysical analysis and prediction of TOC content in marine source rocks of the Madingo Formation, Lower Congo Basin, West Africa

Total organic carbon (TOC) is a critical parameter for evaluating hydrocarbon source rocks, but its strong spatial heterogeneity challenges accurate regional prediction using limited well sample data. Therefore, this study presents an integrated geophysical workflow for the three-dimensional (3D) quantitative prediction of TOC in the post-salt marine source rocks of the Madingo Formation, Lower Congo Basin. Calibrated with 60 measured samples, three log-based TOC models (multiple regression, improved ΔLogR, and a Back-Propagation neural network [BPNN]) were evaluated, and the optimal log-derived TOC was integrated with seismic attributes to construct a 3D TOC volume via seismic impedance inversion. Comparative results demonstrate that the BPNN outperforms traditional empirical models, yielding a correlation coefficient of R = 0.9342. The 3D prediction reveals pronounced spatiotemporal heterogeneity in TOC distribution, which is strongly controlled by sedimentary facies, reaching maximum concentrations in the deep-water slope zone and decreasing towards shallow-water zones. This integrated data-driven approach effectively addresses the limitations of discrete sample analysis, providing a quantified predictive framework that helps constrain prediction uncertainty for deep-sea hydrocarbon exploration in heterogeneous marine source rocks.