The Tertiary marginal marine clastics reservoirs offshore Nigeria have traditionally been treated as well-characterized for formation evaluation; however, reservoir distribution and quality could be quite variable due to the heterogeneities that went unnoticed. Reservoir sands in the study area are structurally controlled and depositionally complex due to various sub-environments architectural elements contributing to the pay zone. Different sands are analyzed with advanced wireline measurements of tri-axial induction resistivity, nuclear magnetic resonance and dielectric dispersion, in the background of geological understanding developed in nearby wells with borehole images. We also describe the new understanding of reservoir rock quality and producibility in a field where easterly dipping massive sand units are juxtaposed with thinly-bedded shaly sand units in a composite interplay of depositional processes.
The data are interpreted taking into account that both the sands and the intermingled shales are anisotropic. Thinly bedded reservoirs, which had been previously overlooked, are discovered and added to the reserves estimate. By reviewing magnetic resonance fluid maps recorded at different and deeper depths of investigation, the radial distribution of fluids is mapped in both massive sandstone and thinly laminated sections.
The data are further analyzed to characterize a set of innovative producibility indicators derived from the logging data. These include: invasion profile, high resolution volumetric analysis using sand and shale definition from image logs, irreducible water analysis and movable fractions from dielectric dispersion, and magnetic resonance T1, T2 and Diffusion measurements.
Different sand units are studied with these indicators, and heterogeneity is captured and addressed to resolve for more precise reservoir summation. Comparison with well test results validates the improvements in reservoir description from multiple high resolution logging measurements (at early stage of reservoir development) into reservoir models.
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