Seismic Attribute Analysis for Characterising Turbidite Reservoir Heterogeneity in Deepwater Nigeria

This study applies multi-attribute seismic analysis to characterise internal heterogeneity within turbidite reservoir systems in the Nigerian deepwater, with the aim of improving reservoir description for field development planning. Turbidite reservoirs in the Niger Delta deepwater are the country's most prolific petroleum reservoirs, but internal architecture variations including amalgamation degree, mudstone interbeds, and facies transitions significantly affect fluid flow behaviour and recovery efficiency. This study analyses a 3D seismic volume from a producing deepwater field in OML 130 area using Petrel seismic interpretation software. Attributes computed and analysed include instantaneous frequency, sweetness, similarity, curvature, and spectral decomposition. Unsupervised neural network facies classification combines multiple attributes to produce a seismic facies volume for each reservoir interval. Well-seismic calibration uses core description and log analysis from five penetrations to define the geological meaning of seismic facies classes. Time slices and stratigraphic slices from the facies volume image internal reservoir architecture. Findings reveal that spectral decomposition at tuning frequencies discriminates between amalgamated channel axis facies and thinner channel margin and lobe fringe facies with 72 percent correspondence to well-calibrated classifications. The neural network classification identifies seven reproducible facies classes that map coherent sedimentological patterns within the primary reservoir interval. Connectivity analysis of the facies volumes suggests that mud barriers exceeding 2 metres thickness compartmentalise the secondary reservoir interval, explaining observed differential pressure behaviour between production wells. The study provides an improved reservoir heterogeneity model with direct implications for infill well placement.

Keywords: seismic attributes, turbidite reservoir, deepwater Niger Delta, facies classification, reservoir heterogeneity.