Bridging Data Gaps: Rock Physics-Driven vs Log Prediction for Reservoir Excellence

Summary Shear wave velocity (Vs) is crucial for reservoir characterization, AVO analysis, and seismic inversion, yet direct measurements are often unavailable. Traditional empirical models introduce uncertainty, making accurate predictions challenging. This study employs advanced rock physics modeling in the Peninsular Malay Basin, integrating the Xu-White model, selfconsistent modeling (SCM), and Gassmann fluid substitution to predict Vs with improved accuracy. Five wells from a clastic fluvial reservoir were analyzed using well-log data, mineralogy, and petrophysical properties. SCM-driven aspect ratio modeling enhances Vs prediction by capturing rock stiffness variations. Results show strong correlation between predicted and measured Vs, validating the model’s reliability. Gassmann fluid substitution highlights the seismic impact of coal stringers, affecting AVO responses. This methodology enables accurate Vs estimation in data-limited wells, improving seismic inversion and reservoir characterization across mature and frontier basins.