Enhancing Reservoir Characterization using Seismic Inversion and Geostatistical Modeling by Integrating Seismic Attributes with Well-Log Data for Improved Lithofacies and Reservoir Property Estimation.

Accurate reservoir characterization is critical for optimizing hydrocarbon exploration and production. This study explores the integration of seismic inversion and geostatistical modeling, leveraging seismic attributes and well-log data to enhance lithofacies estimation and reservoir property prediction. The research addresses the challenges of combining multiple data sources to improve the spatial resolution and accuracy of reservoir models. The workflow begins with the acquisition and preprocessing of seismic and well-log data, followed by seismic inversion to derive high-resolution subsurface properties. Geostatistical modeling is then employed to integrate seismic attributes with well-log data, providing a robust framework for predicting lithofacies distribution and reservoir properties. The study evaluates the effectiveness of this integrated approach through a detailed analysis of seismic attribute interpretation, lithofacies classification, and reservoir property distribution. Validation of the models against existing methods demonstrates significant improvements in accuracy and resolution, highlighting the potential of this approach for complex reservoir environments. Key findings reveal that the integration of seismic attributes with well-log data not only enhances the reliability of lithofacies models but also provides a more detailed understanding of reservoir heterogeneity. This research contributes to the advancement of reservoir characterization techniques by offering a practical and scalable solution for improved hydrocarbon recovery. The study concludes with recommendations for applying this approach to diverse geological settings and identifies avenues for future research in the integration of advanced geostatistical methods and machine learning techniques.