Quality Assessment of Downhole Reservoir Fluid Sampling by Predicted Interfacial Tension

Abstract Acquiring a representative reservoir fluid sample is critical for reservoir engineering, production, and petroleum economics. Because a well may be drilled with different drilling fluids, reservoir oil sampled could be contaminated by drilling fluid used. Although some contamination can be characterized relatively easy, others may be difficult to identify and quantify, which could affect decision making. In this paper, we present a methodology to assess reservoir sampling quality with interfacial tension (IFT) prediction while sampling. In this study, live reservoir crude oils were sampled from different reservoirs, and their basic PVT properties were measured, which showed distinct properties in density, viscosity, and gas-oil ratio. IFT of the live crude oils was measured against that of the respective formation brines, at reservoir conditions, using a specially designed high-pressure/high-temperature cell, and the measured values range from 18.02 to 31.10 mN/m. Using the measured data of crude oil properties and IFT, an empirical model was developed to predict IFT from basic properties, including oil and water density, oil viscosity, and reservoir pressure and temperature, all can be measured downhole using a formation sampling tool. The model was validated using downhole measurements where IFT was predicted, and results indicate that the IFT prediction is very sensitive to drilling fluid contamination and this phenomenon is then used effectively to identify drilling contamination of the sampled reservoir oil. The findings of this study provide a new methodology to assess the quality of downhole fluid sampling and therefore reduce operating cost and provide quality reservoir fluid samples. The methodology can be integrated as an answer product of a formation tester to identify contamination and confirm the accuracy of the in-situ measured properties, in addition to minimize pumping-out time, saving rig cost and reduce operational CO2 emissions. In addition, the findings of this study provide a new correlation to drive IFT downhole and at reservoir conditions, through the developed correlation.