My name is Ramin Soltanmohammadi. I am a Ph.D. student in the mechanical engineering department at UNICAMP. My primary research interest is in the use of machine learning to accelerate the simulation of fluid flow in complex-heterogeneous porous materials using micro-CT images of plug samples. Additionally, I am keenly interested in carbon capture and storage, renewable energy, and energy transition. Furthermore, using various machine learning techniques, I am predicting various facies, rock types, and characteristics of reservoir rock using well-log data.

After joining Schlumberger in 2010, Ahmed Adnan Aqrawi has published over 30 conference and journal papers combined, as well as having numerous patents. His background is a master’s degree from the Norwegian University of Science and Technology, specializing in high performance computing, image processing and artificial intelligence. The research over the last 12 years has mainly focused on topics of delineation and segmentation of structural geology in post-stack seismic data. In recent years, the focus on the use of unsupervised learning techniques has led to many interesting developments and will continue to be a focus area.

Jonathan Dietz is a project geoscientist within CGG's DataHub team and has been with CGG for just over 3 years. His role focuses on developing and pushing new methods for the interpretation, understanding and integration of sedimentological data with other disciplines. He has a PhD from the University of Glasgow which focused on volcanic and sedimentary systems in Iceland.