Plenty of efforts have been devoted to implementing machine learning (ML) to resolve geoscience challenges in the recent years, and significant progress has been achieved in multiple aspects such as well placement, well design, fault detection, and data reconstruction. The goal of this workshop is to showcase ML solutions in geoscience and brainstorm its potential for additional geoscience tasks. This will include but is not limited to the following topics:
1. Team dynamics for successful ML implementations
2. Multi-domain data integration (engineering, geology, geophysics, & petrophysics) for field optimization
3. Use of multivariate analyses for well log interpretation
4. Seismic interpretation such as facies classification and structure modeling
5. Signal processing modules such as frequency extrapolation, de-multiple, de-noise, de-ghost, deblending, etc.
6. Depth processing technologies such as velocity model building, acceleration of seismic wave propagation modeling and migration engine, advancement of FWI
7. Land data processing challenges and potential ML solutions, such as static correction, near surface characterization.
8. Non-seismic geophysical data processing and inversion
9. Energy transition implementations for emissions monitoring and CCUS
10. Data management