Reservoir modeling and optimization are vital tasks for ensuring that our future CO2 storage sites remain safe and intact while performing according to our expectations. How machine learning and powerful, fast, and automated modeling workflows can help us achieve this, will be the main topic in the Subsurface Workflows – Reservoir characterization session at the Dig X Subsurface 2024 conference in Oslo in March.
The launch of the flagship CCS project in the North Sea – Northern Lights – is just months away. Senior Reservoir Engineer Eirik Jenssen will give the talk Optimizing Uncertainty Management in CO2 Storage Projects and provide insight into a novel methodology in employing an ensemble-based workflow for uncertainty quantification to optimize storage operations.
The Northern Lights team has developed an improved workflow that integrates advanced modeling techniques to systematically evaluate uncertainties such as impacting pressurization, injectivity, migration, trapping mechanisms, and more.
Ammar Ahmad (SLB) will share some results from a CCS case study, where the objective was to effectively delineate tight versus non-tight sands. This highly useful task was done through seismic inversion and machine learning to ultimately choose an optimal area for CO2 injection across the entire project life.
While not addressing CCS issues specifically, the session will also include a talk by Odd Kolbjørnsen at Aker BP on reservoir characterization. We are looking forward to hearing him explain how ensemble technology and leveraging automation have been shown to improve efficiency and reduce turnaround time!
The program can be found on the conference website. The place is Oslo (Gardermoen) and the dates are March 5 – 6th 2024.