COURSE OVERVIEW

 

This course is designed to provide participants with a detailed and up-to-date overview of Artificial Intelligence in Sub-Surface Oil & Gas. It covers the fundamentals of Al and machine learning in oil and gas and the key Al tools and platforms used in subsurface exploration; the Al for geological data analysis, subsurface mapping, seismic data interpretation, well log interpretation and formation evaluation; the Al in reservoir characterization and modeling covering Al-based reservoir property estimation, predicting subsurface heterogeneity using Al, Al-driven geostatistical modeling and Al-powered reservoir simulation integration; and using machine learning for rapid history matching, Al-driven optimization of simulation parameters and Al-based uncertainty quantification in history matching. 

 

Further, the course will also discuss the machine learning for reservoir performance prediction and Al for fluid flow modeling and pressure transient analysis; the Al for fracture characterization and well stimulation and the production logging and well performance analysis; the Al-driven wellbore stability and geomechanics analysis, real-time drilling optimization, well placement and trajectory optimization; the Al-powered formation damage detection and mitigation and downhole sensor data interpretation; and the Al for secondary and tertiary recovery optimization, reservoir surveillance, production optimization and CO₂ sequestration and carbon storage.

 

During this interactive course, participants will learn the digital twins in oil and gas and pipeline and well integrity monitoring, subsurface corrosion prediction, subsurface geohazards identification and predictive maintenance for wells; the Al trends and innovations in subsurface engineering and the Al for autonomous subsurface decision-making; and the Al and loT integration for smart subsurface operations, reducing operational costs and increasing efficiency.