Project title
4D seismic inversion for pressure and saturation using machine learning
Project abstract
This project focuses on developing machine learning–driven approaches for 4D seismic inversion to estimate reservoir pressure and fluid saturation changes. By integrating data-driven models with trace-, waveform-, and section-based inputs, it aims to enhance prediction accuracy, resolution, and computational efficiency. The work emphasizes leveraging modern ML techniques to better capture time-lapse reservoir dynamics and improve monitoring and decision-making.