Session 16. Oral Presentation for: Reservoir simulation using multi-variate machine learning techniques
Presented on 20 May 2026: Session 16 Novel use of statistical and machine learning techniques in this study show that improved reservoir simulation results, history matched production and reserve estimates can be improved by integrating all available reservoir simulation, infill log and 4D seismic data. This can be set-up for real-time analysis without waiting for many iterations of the inputs (e.g. rigorous re-processing and inversion of the 4D seismic) with manual reservoir simulations. The key is these multi-variate techniques can integrate all data simultaneously in 3D to mimic the many steps of manual calibration. To access the Oral Presentation click ‘Supplementary data’ below. To read the full paper click here
Authors
- Jones K. Ebinesan
Institutions
- Curtin University (AU)
Publication Details
- Journal
- Australian Energy Producers journal.
- Published
- 2026-06-18
- DOI
- https://doi.org/10.1071/ep26469
- Primary Topic
- Reservoir Engineering and Simulation Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00