Speaker
Description
The trade-off between the field of view (FOV) and resolution of micro-computed tomography (micro-CT) is a hardware bottleneck limits capturing both heterogeneity and micro-structure detail. Efficient super resolution methods combine the upper limits of both FOV and voxel resolution, while efficient modelling permits analysis of the large domain. A low resolution image of a 1-inch sandstone core plug and an unregistered high resolution (1-micron) sample trains an efficient and world-first 3D un-paired super resolution convolutional neural network, Dual-CycleSR. The resulting 25,000 x 25,000 x 50,000 domain provides unprecedented geometric fidelity over a full-sized core plug and reveals spatial heterogeneity that is captured by pore-to-core upscaling, with which forward-modelling produces a close match with unsteady-state core flooding production curves.
| Participation | In-Person |
|---|---|
| Country | Australia |
| MDPI Energies Student Poster Award | No, do not submit my presenation for the student posters award. |
| Acceptance of the Terms & Conditions | Click here to agree |
