Multiresolution Features for Sonic Log Classification

This study highlights the significance of precise DTC and DTS values for reservoir characterization and presents a unique method for sonic log prediction in the oil and gas sector. To improve prediction accuracy, the approach integrates signal processing-more especially, Wavelet transformation-with machine learning. Wavelet transformation on seismic signals, feature extraction, dataset preprocessing, and machine learning model training are all steps in the process. The accuracy of the results is an astonishing 87.5 percent, outperforming previous algorithms. The extensive and varied data set used guarantees the model's resilience in a range of geological situations. The suggested method improves reservoir characterization confidence by making a substantial contribution to precise wave velocity estimation. All things considered, the study provides a workable and efficient way to enhance sonic log forecasts, which will help with hydrocarbon exploration and petroleum industry decision-making.