Python-based seismic-to-well tie, horizon interpretation and structural mapping for reservoir characterization in the Niger Delta Basin

This study presents a Python-based workflow for seismic-to-well tying, reservoir correlation, and structural mapping using well-log and 3D seismic data from the Kolo Creek Field in the Niger Delta Basin. The study aimed to evaluate the applicability of open-source scientific computing tools for seismic interpretation and reservoir characterization traditionally performed using proprietary software. Gamma ray, resistivity, sonic, and density logs were integrated with 3D seismic data for reservoir identification, synthetic seismogram generation, seismic-to-well tying, horizon interpretation, and structural mapping. Acoustic impedance and reflection coefficient series were computed from the sonic and density logs, after which synthetic seismograms were generated using a 25Hz zero-phase Ricker wavelet. The results revealed laterally continuous reservoir intervals with depths ranging from 11,598ft to 12,248ft across the study area. Seismic-to-well tying identified the reservoir top at approximately 978.87ms two-way travel time and produced a calibrated average interval velocity of approximately 24,016ft/s for depth conversion. The generated time and depth structure maps revealed a regional structural dip with localized structural closures that may represent favorable hydrocarbon trapping configurations. Comparison between the Python-derived depth structure map and a previously published Petrel-derived interpretation showed strong agreement in structural trends and contour geometry. The study demonstrates that Python-based workflows provide a reliable, reproducible, and cost-effective framework for seismic interpretation, structural mapping, and reservoir characterization within academic and research environments.