Characterisation of Hydrocarbon Reservoir Heterogeneity Using 3D Seismic Attributes in the Coastal Swamp Depobelt, Niger Delta

Reservoir heterogeneity in the Coastal Swamp Depobelt of the Niger Delta poses a significant challenge to accurate production forecasting and enhanced oil recovery planning, as conventional seismic interpretation methods fail to adequately resolve intraformational facies variability within individual sand bodies. This study characterises reservoir heterogeneity in a producing oil field in Bayelsa State using a comprehensive suite of 3D seismic attribute analysis techniques applied to a post-stack time-migrated seismic volume. Instantaneous amplitude, instantaneous frequency, coherence, and curvature attributes were extracted and co-rendered to map lateral facies changes within the primary Agbada Formation reservoir sands. Neural network-based multi-attribute analysis was applied to integrate eight seismic attributes into a single lithofacies probability volume, trained using well-log-derived facies descriptions at three penetrating wells. The resulting facies model identifies four distinct reservoir facies: amalgamated channel sands, thin-bedded turbidites, shale-prone heterolithic intervals, and clean sheet sands. The spatial distribution of these facies explains previously unresolved production anomalies between wells with initially similar log-derived porosity and saturation values. Permeability heterogeneity estimated from the facies model is incorporated into a revised static reservoir model showing 35 percent better history match compared to a facies-unconstrained base model. The study demonstrates that 3D seismic attribute analysis provides a quantitatively rigorous basis for improving the geological model of producing Niger Delta reservoirs. Keywords: seismic attributes, reservoir heterogeneity, Niger Delta, facies modelling, Agbada Formation