Data Driven Shear-Sonic Log Synthesis for In-Situ Caprock and Carbonate Reservoirs in Malaysian Fields

The travel time of shear waves (DTS) in caprock and carbonate formations is critical for subsurface studies, including evaluating caprock sealing integrity, storage capacity, lithology, and geomechanical modeling. However, DTS logs are often unavailable in older wellbores due to data loss or lack of recording. This study develops a mathematical model to estimate DTS from compressional sonic logs (DTC) using a simple supervised machine learning algorithm, Random Forest, for calibration and verification.

The study utilizes geophysical logs such as gamma ray (GR), neutron porosity (NPhi), and density (RhoB) for predictions. Case studies are conducted on two carbonate fields in the Luconia Basin, offshore Sarawak, using offset well data from Field-A. Separate models correlating DTC and DTS are generated for caprock and carbonate reservoirs and applied to other wells in Field-A and Field-B to create synthetic DTS logs.

Machine learning independently calibrates the synthetic DTS using well log datasets, with consistent results observed between the mathematical model and machine learning outputs. Comparing linear regression with machine learning reveals strong agreement, with the model showing the highest R² value considered most reliable for detailed subsurface studies. This approach demonstrates the feasibility of DTS estimation using readily available log data.