Quantitative Seismic Interpretation for Facies Discrimination in the Offshore Deepwater Turbidite Systems, Gulf of Guinea

Deepwater turbidite reservoirs in Nigeria's Gulf of Guinea sector are among the country's most prolific petroleum provinces, yet predicting reservoir quality away from well control in complex channel-lobe systems remains a significant challenge for appraisal and development geoscience. This study applies quantitative seismic interpretation techniques including model-based acoustic impedance inversion, lambda-mu-rho analysis, and geostatistical facies simulation to discriminate depositional facies in a deepwater turbidite field in OML 118 offshore Nigeria. Prestack simultaneous inversion was performed on a partial-angle-stack seismic dataset to derive acoustic impedance, Poisson ratio, and pseudo-density volumes. Crossplot analysis of the derived elastic properties in well control volumes defines distinct clusters corresponding to clean channel sands, amalgamated lobe sands, mudstone, and hybrid event beds. Bayesian classification applied to the full inversion volume produces a probabilistic facies cube from which the three-dimensional geometry of reservoir and non-reservoir facies is reconstructed. The channel sand body mapped from the facies cube has a mean width of 1.8 kilometres and a total mapped length of 22 kilometres, consistent with architectural element dimensions reported for similar West African deepwater systems. Uncertainty analysis using sequential Gaussian simulation shows that net sand thickness uncertainty of plus or minus 12 percent at the P10-P90 level propagates from the facies model to volumetric estimates. The study establishes a robust quantitative seismic workflow for reducing development well placement risk. Keywords: seismic inversion, turbidite, deepwater, facies discrimination, Gulf of Guinea