Enhanced Fluid Characterization for Improved Saturation & Connectivity

Abstract

New methods used to accurately determine water and hydrocarbon saturation profile, hydrocarbon typing & reservoir extent (connectivity) in real time by using acquired insitu water salinity measurement downhole and measured hydrocarbon optical density from formation testers. The uncertainties present from water salinity (Rw) values from nearby wells (due to vertical & aerial variations) may compute inaccurate hydrocarbon saturations when input in Archie's based fluid saturation model, which affects the hydrocarbon in place calculations. Also, relying only on pressure measurement for connectivity assessment & reservoir extent using Vertical Interference Testing (VIT) may sometimes be misleading & inconclusive due to supercharging in low permeability & low pressure contrast in high permeability which affects the field development plans. Therefore, the new methodology is introduced to overcome these challenges. Firstly, the downhole water salinity is measured in a water zone at the bottom of the reservoir while pumping out using formation tester after achieving clean-out (negligible contamination). The values were input into the initial petrophysical model to recompute the new fluid saturation which was confirmed by pump-out fluid fractions from other zones. Secondly, the optical density measurement from the spectroscopy was used to confirm the vertical connectivity though Reservoir Fluid Geodynamics & indicating a compositional gradient in real time in an oil column. This was done by constructing Flory-Huggins-Zuo (FHZ) Equation of State model and identifying asphaltene molecular size from optical density gradient. These two methods will bring accurate & reliable results compared to conventional methods which has some uncertainties impacting evaluation & reservoir understanding. Secondly, will help in optimizing sampling & downhole fluid analysis programs which eventually saves cost & time & CO2 emissions. Thirdly, Optical Density gradient analysis can be also used for flow assurance prediction.