The application of seismic attribute analyses for enhanced reserve estimation in “KOA” field, Niger Delta

The demand for energy increases daily since the world’s population is increasing, therefore there is a need to identify new or by-passed prospects in other to meet these needs. The “KOA” field is located onshore Niger Delta, Nigeria. This basin is made up of three formations; Akata Formation (source rock), the sand/shale units in the Agbada Formation (the reservoirs and seals), the faulting in the Agbada Formation provided pathways for petroleum migration and formed structural and stratigraphic traps, and Benin Formation (sand unit). In this work, seismic attributes were applied with the aim of enhancing the imaging of structures for improved structural interpretation and new prospects identification. Nine reservoir sands were encountered and found to be laterally continuous across the field. Petrophysical analysis of the identified reservoir sands showed that the average effective porosity ranged from 13.3 to 24.9%, average permeability ranged from 38.4 to 3340.6 md and average hydrocarbon saturation ranged from 61.8 to 93.0%. Seismic volume attributes were applied to the 3D seismic data to enhance the imaging of faults. Fault interpretation and horizon mapping were carried out to generate structural maps which were analyzed for closures capable of trapping hydrocarbon accumulations. Structural interpretation revealed synthetic faults, antithetic faults and a crestal fault. Horizons that corresponded to the well tops of the most viable reservoir sands in the field were mapped after well to seismic tie. Surface attribute maps were generated from the structure maps, this was done in order to identify areas with hydrocarbon accumulations which were conformable to structures. Three prospects (P1, P2 and P3) and a lead were identified. Volumetric analysis revealed that prospects P1, P2 and P3 contained 161.49, 271.79 and 128.79 thousand barrels of oil respectively. The study concluded that the application of seismic attribute analyses significantly improved the imaging and interpretation of structures which was useful in the identification of new prospects in the study area.