Genetic Units Based Reservoir Characterization using a Normalized Pore Throat Radius for the Clastic System: Niger Delta as Field Case Study

Onuh, Haruna Monday (2016-12-12)


Globally, 30–50% of hydrocarbon volumes in silici-clastic reservoirs are contained within the thin-bedded pay. In the Niger Delta deep water assets, over 30% of in-place volumes are found within the complex turbidites. The presence of multi-pore architecture within such facies makes their description from petrophysics very complex. With the quest for hydrocarbon prospects in frontier deep water settings characterized by such complex rock fabric, detailed reservoir characterization is essential for accurate field management and production optimization. The focus of this work is to characterize complex reservoir pore systems at core scale based on genetic reservoir unit averages and to provide improved models for petrophysical evaluation using a normalized pore throat radius approach for clastic reservoirs. New methods are presented for modelling permeability in rocks with multimodal pore throat size distributions using Niger Delta field as case study. The statistical significance of the coefficients in the proposed relationships for various genetic reservoir units was verified using α-level of 0.05; and the results indicate that the proposed model is very unlikely to have occurred by chance. Two methodologies are presented for upscaling from core plug to log scale–genetic unit averages of pseudo normalized pore throat radius as input parameter to the proposed model. This study also presents improved methodology for generating capillary pressures from NMR T2 relaxation time using a genetic unit based averages of the kappa scaling parameter proposed by Volokitin et al. The improved methodology is also applicable to conventional geophysical logs for estimating capillary pressure in the absence of NMR T2 data. Comparative analyses indicate that the proposed methodology is an excellent improvement over existing methods (e.g., Reservoir Quality Index, Leverett J-Function, Stratigraphic Modified Lorenz Plot) for characterizing hydraulic flow units. Additionally, efficiency of the proposed methodology is demonstrated by comparison of estimated permeabilities versus core permeabilities from four depobelts in the Niger Delta. Permeabilities were derived from existing methodologies including Genetic Unit Averages of FZI’s, Neural Network Permeability, NMR based Schlumberger Doll Research (SDR) and Coates correlations. It is concluded that the proposed methodology is a superior and practical tool for reservoir characterization.