Predictive Seismic Velocity from Rock Characteristics: Automating AVO Analysis for Hydrocarbon Exploration

This study focuses on automating the creation of Amplitude Variation with Offset (AVO) sensitivity matrices using machine learning (ML) to improve seismic exploration. Traditional methods are labor-intensive and prone to inaccuracies due to manual processes and expert interpretation. We developed a Random Forest model to estimate Bulk Density (RHOB), Compressional Velocity (VP), and Shear Velocity (VS) in the Balingian area, Offshore Sarawak.

Our model integrated well log data and employed sequential prediction for enhanced accuracy. Data preprocessing involved removing outliers and bad data points. Key features included gamma ray, bulk density, and seismic velocities. Hyperparameter tuning via Grid Search with cross-validation ensured optimal performance.

Results showed the model performed well under simpler fluid conditions (INSITU and BRINE) but struggled with complex conditions (OIL and GAS), as indicated by higher error rates and negative R-squared (R²) values. Metrics such as Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) highlighted these challenges.

This study demonstrates ML’s potential to automate and improve seismic velocity predictions, aiding petrophysics in subsurface understanding and drilling target identification. Further refinement and additional data are necessary to enhance predictive accuracy for complex fluid scenarios, paving the way for more efficient and accurate seismic exploration processes.