A cohesive approach at estimating water saturation in a low-resistivity pay carbonate reservoir and its validation

Abstract

Carbonate reservoir characterization and fluid quantification seem more challenging than those of sandstone reservoirs. The intricacy in the estimation of accurate hydrocarbon saturation is owed to their complex and heterogeneous pore structures, and mineralogy. Traditionally, resistivity-based logs are used to identify pay intervals based on the resistivity contrast between reservoir fluids. However, few pay intervals show reservoir fluids of similar resistivity which weaken reliance on the hydrocarbon saturation quantified from logs taken from such intervals. The potential of such intervals is sometimes neglected. In this case, the studied reservoir showed low resistivity. High water saturation was estimated, while downhole fluid analysis identified mobile oil, and the formation produced dry or nearly dry oil. Because of the complexity of Low-resitivity pay (LRP) reservoirs, its cause should be determined a prior to applying a solution. Several reasons were identified to be responsible for this phenomenon from the integration of thin section, nuclear magnetic resonance (NMR) and mercury injection capillary pressure (MICP) data—among which were the presence of microporosity, fractures, paramagnetic minerals, and deep conductive borehole mud invasion. In this paper, we integrated various information coming from geology (e.g., thin section, X-ray diffraction (XRD)), formation pressure and well production tests, NMR, MICP, and Dean–Stark data. We discussed the observed variations in quantifying water saturation in LRP interval and their related discrepancies. The nonresistivity-based methods, used in this study, are Sigma log, capillary pressure-based (MICP, centrifuge, and porous plate), and Dean–Stark measurements. The successful integration of these saturation estimation methods captured the uncertainty and improved our understanding of the reservoir properties. This enhanced our capability to develop a robust and reliable saturation model. This model was validated with data acquired from a newly drilled appraisal well, which affirmed a deeper free water level as compared to the previous prognosis, hence an oil pool extension. Further analysis confirmed that the major causes of LRP in the studied reservoir were the presence of microporosity and high saline mud invasion. The integration of data from these various sources added confidence to the estimation of water saturation in the studied reservoir and thus improved reserves estimation and generated reservoir simulation for accurate history matching, production forecasting, and optimized field development plan.