Agradecimentos: This work was conducted with the support of Libra Consortium (Petrobras, Shell, Total, CNOOC, CNPC and PPSA) within the ANP R&D tax as “commitment to research and development investments” and Energi Simulation. The authors are grateful for the support of the Center of Petroleum...

Agradecimentos: This work was conducted with the support of Libra Consortium (Petrobras, Shell, Total, CNOOC, CNPC and PPSA) within the ANP R&D tax as “commitment to research and development investments” and Energi Simulation. The authors are grateful for the support of the Center of Petroleum Studies (CEPETRO-UNICAMP/Brazil), the Department of Energy (DE-FEM-UNICAMP/Brazil) and Research Group in Reservoir Simulation and Management (UNISIM-UNICAMP/Brazil). In addition, special thanks to CMG and Schlumberger for software licenses. The authors would also like to thank Guilherme Fernandes Vasquez and Marcos Sebastião dos Santos for the technical discussions that contributed to the enrichment of this study

There is a growing interest in applying quantitative methods to adjust reservoir flow models using time-lapse seismic data. The most common approach relies on a petroelastic model to convert the flow simulator outputs into acoustic impedance. The comparison of this simulated data with the observed...

There is a growing interest in applying quantitative methods to adjust reservoir flow models using time-lapse seismic data. The most common approach relies on a petroelastic model to convert the flow simulator outputs into acoustic impedance. The comparison of this simulated data with the observed time-lapse seismic anomalies enables the computation of changes in the reservoir models' parameters, reducing the uncertainty, and improving the reservoir characterization. Among other properties, the petroelastic model requires fluid models capable of forecasting the speed of sound. This fact becomes more challenging when the oil is volatile and contains a significant amount of CO2, which is the case in some reservoirs in the Brazilian pre-salt region. In this situation, some classical models fail to predict the speed of sound in the oil phase within reasonable accuracy. Other models require testing for specific fluids or are not conveniently build to the integration with actual compositional reservoir simulators. Therefore, we propose the application of a calibrated cubic equation of state to represent the fluid behavior for both reservoir flow and petroelastic simulations. For this purpose, we describe a methodology in which the fluid model is progressively adjusted using reservoir engineering and speed of sound experimental data, depending on the available information. We applied this methodology using the well-known Peng-Robinson equation, but similar results could be obtained with other models of this class. We show that the match to the conventional pressure-volume-temperature data, a common practice in reservoir engineering, can be enough to generate fluid models capable of forecasting the speed of sound. Furthermore, the speed of sound experimental data can improve the fluid characterization without jeopardizing the previous fitted experiments. We tested our methodology with experimental data of a fluid of one reservoir in the Brazilian pre-salt region. Moreover, we compared the results obtained in the equation of state with other published correlations and simplified models. Synthetic reservoir models with different production strategies were applied in these comparisons

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