Comparison of Z-Factor Correlations Using a Large PVT Dataset with Emphasis on the GERG-2008 EOS

The gas compressibility factor is an important property in reservoir simulation studies. It is directly linked to the gas formation volume factor and the gas density thereby impacting wellhead injection pressure, reservoir voidage, injectivity, as well as the tendency for gas gravity override to occur in the reservoir. ADNOC's PVT database contains experiments on almost 2,000 samples, of which more than 100 have been subject to advanced gas injection experiments. Z-factor data have been compiled from the liberated gas during DV experiments as well as from CCE experiments on reservoir gases, injection gases, and swollen fluid mixtures. Several of these mixtures are very rich in H2S, whereas pressure and temperature are in the range of 14.7-14,500 psia and 80-365 °F, respectively. We test several different methods for predicting the Z-factor, such as the industry-standard Hall-Yarborough method, in combination with various models for pseudo-critical pressure and temperature and including correction for non-hydrocarbon components. Other methods tested include the GERG-2008 model, considered to be state-of-the-art for predicting physical properties for well-described gas mixtures, as well as the well-known Peng-Robinson cubic equation of state. Based on close to 10,000 data points in our database, the GERG-2008 model typically predicts the Z-factor to be within 2% of the measured value, which is on par with the experimental uncertainty. However, for some rich gas condensate mixtures, the model gives larger errors because its parameters are only tuned to compositions with components up to C10. This is to our knowledge the first time that the GERG-2008 EOS has been compared to standard Z-factor correlations for such a large number of data points. If compositional information is available, we recommend using either the GERG-2008 model or the Hall-Yarborough model with pseudo-critical properties provided by Kay (1936). When compositions are not available, we find that the Standing correlation is more accurate than the Sutton model, also for sour mixtures.

Differential Microstrip Sensor for Complex Permittivity Characterization of Organic Fluid Mixtures

A microstrip highly sensitive differential sensor for complex permittivity characterization of urine samples was designed, fabricated and tested. The sensing area contains two pairs of open-stub resonators, and the working frequency of the unloaded sensor is 1.25 GHz. The sensor is easily implemented on an affordable substrate FR-4 Epoxy with a thickness of 1.6 mm. A Teflon beaker is mounted on the sensor without affecting the measurements. Numerically, liquid mixtures of water and urine at different percentages were introduced to the proposed sensor to evaluate the frequency variation. The percentage of water content in the mixture varied from 0% (100% urine) to 100% (0% urine) with a step of 3.226%, thus giving 32 data groups of the simulated results. Experimentally, the mixtures of: 0% urine (100% water), 20% urine (80% water), 33% urine (66% water), 50% urine (50% water), 66% urine (33% water), and 100% urine (0% water) were considered for validation. The complex permittivity of the considered samples was evaluated using a nonlinear least square curve fitting in MATLAB in order to realize a sensing sensitivity of about 3%.

Dynamic Viscosity of Binary Fluid Mixtures: A Review Focusing on Asymmetric Mixtures

A literature review on experimental data for binary mixtures of methane and carbon dioxide with hydrocarbons up to n-hexadecane is presented. Based on these data, the extended corresponding states method, two entropy scaling approaches, and the friction theory are analyzed with respect to their capability of calculating viscosity values with increasing asymmetry of the binary mixture. It is shown that not only the viscosity model but also the underlying thermodynamic equation of state has a significant influence on the result of such calculations. Shortcomings are identified both in the experimental data and in the modeling approaches.