Unlocking Insights from Post-Stack 4D Seismic Inversion and Interpretation: A Case Study of the Tupi Field, Brazilian Pre-salt

Abstract Interpreting 4D seismic signals involves significant uncertainty, particularly in stiff and heterogeneous carbonate reservoirs such as the Brazilian pre-salt. This study aimed to leverage seismic inversion to estimate changes in the elastic properties of rocks due to production activities and to evaluate the benefits of inversion techniques for 4D seismic interpretation. The study focused on the productive Tupi Field in the Santos Basin, where high repeatability 4D seismic data were acquired in 2015 and 2017/2018 using Ocean Bottom Node technology. The constrained sparse-spike inversion method was employed to generate 3D post-stack inversions for both surveys. 4D information was obtained through a straightforward subtraction from the impedance for both 3D results. Consequently, the inverted volumes produced delta impedance maps and sections, which were compared with amplitude difference and integrated with well history data. The results demonstrated that the delta impedance maps effectively outlined the primary 4D anomalies associated with WAG and injection wells. Compared to the amplitude map, the delta impedance provided a more precise delineation of the key anomalies, which were also in greater agreement with the well history data and literature findings. These results suggest that integrating seismic inversion with well-history data significantly improved 4D seismic interpretability, offering valuable insights into reservoir dynamics. Additionally, the integration between the inversion outcomes and well history data with geological data helped us identify potential barriers for fluid flow related to microporous zones within the upper section of the Barra Velha Formation. In conclusion, integrating seismic inversion with well-history and other data sources enhances 4D seismic interpretation, providing valuable insights into reservoir dynamics and supporting more informed reservoir management decisions. This approach effectively addresses interpretational uncertainties, particularly in complex fields.