Speaker
Description
Micro-CT imaging and pore-scale modelling have developed rapidly over the last decade by bridging the disciplines of geology, reservoir engineering, image processing, and computational fluid dynamics. They have provided new pathways for understating complex transport phenomena in underground geological formations and other porous media. However, there are several steps in this framework that are time-consuming and of user bias. Machine learning and Convolutional Neural Networks (CNN) as a part of the broader field of Artificial Intelligence (AI) can be integrated into the framework of pore-scale modelling and imaging. The trade-off between sample size and image resolution as well as the expensive computational cost associated with numerical simulations of fluid flow in the pore spaces can be addressed by the use of CNNs. We will show how we can recreate porous media images at a super-resolution and use them for exploring porous media transport phenomena. We also demonstrate the reliability and accuracy of CNNs for the determination of rock properties on images of porous media. Challenges and opportunities for the development of machine learning approaches in porous media applications will be discussed.
| Participation | In person |
|---|---|
| Country | Australia |
| MDPI Energies Student Poster Award | No, do not submit my presenation for the student posters award. |
| Time Block Preference | Time Block B (14:00-17:00 CET) |
| Acceptance of the Terms & Conditions | Click here to agree |
