- E-postpeter.schultzendorff@uib.no
- BesøksadresseAllégaten 41Realfagbygget5007 Bergen
- PostadressePostboks 78035020 Bergen
I am a PhD student at the Centre for Sustainable Subsurface Resources, which researches technologies that enable the transition of the Norwegian continental shelf to a sustainable future.
My field of research is the coupling of classical physics-based models for reservoir simulations with new data-driven methods. The goal is to combine the best of both worlds to enable high fidelity to physics and data, as well as fast computation for reservoir simulations. My work focuses on the mathematical analysis of such coupled problem and the development of robust and stable solvers. This work is motivated by applications such as CO2 storage, H2 storage, and renewable-powered offshore operations. As part of my research, I contribute to the development of the open-source simulator PorePy and the pyopmnearwell framework for the simulator OPM Flow.
- (2023). Machine Learning for near well Models in CCS/CCUS/H2 Storage Simulations.
- (2023). Homotopy continuation for a two-phase flow model with machine-learned relative permeabilities.
- (2023). A machine learned near-well model in OPM.
- (2023). A machine learned near-well model in OPM.
- (2023). pyopmnearwell: A framework to simulate near well dynamics using OPM Flow.
- (2023). PorePy: A Simulation Tool for Fractured and Deformable Porous Media, version 1.8.1.
- (2024). A machine-learned near-well model in OPM Flow.