I am a PhD student at the Centre for Sustainable Subsurface Resources (CSSR), 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.

Lecture

• Tor Harald Sandve; Birane Kane; David Landa-Marbán et al. (2023). Machine Learning for near well Models in CCS/CCUS/H2 Storage Simulations​. (external link)

Conference poster

• Peter Moritz von Schultzendorff; Jakub Wiktor Both; Jan Martin Nordbotten et al. (2025). An adaptive homotopy continuation solver for incompressible two-phase flow in porous media. (external link)
• Peter Moritz von Schultzendorff; Jakub Wiktor Both; Jan Martin Nordbotten et al. (2023). A machine learned near-well model in OPM. (external link)
• Peter Moritz von Schultzendorff; Jakub Wiktor Both; Jan Martin Nordbotten et al. (2025). An adaptive homotopy continuation solver for incompressible two-phase flow in porous media. (external link)
• Peter Moritz von Schultzendorff; Jan Martin Nordbotten; Tor Harald Sandve et al. (2025). An adaptive homotopy continuation solver for incompressible two-phase flow in porous media. (external link)
• Peter Moritz von Schultzendorff; Tor Harald Sandve; Birane Kane et al. (2024). A machine-learned near-well model in OPM Flow. (external link)
• Birane Kane; Peter Moritz von Schultzendorff; Tor Harald Sandve (2025). Machine Learning integration into a reservoir simulatorMachine Learning integration into a reservoir simulator. (external link)

Conference lecture

• Peter Moritz von Schultzendorff; Martin Vohralík; Jakub Wiktor Both et al. (2025). Adaptive homotopy continuation for machine-learned relative permeability models in porous media flow. (external link)
• Peter Moritz von Schultzendorff; Jakub Wiktor Both; Jan Martin Nordbotten et al. (2025). An adaptive homotopy continuation solver for incompressible two-phase flow in porous media. (external link)
• Peter Moritz von Schultzendorff; Tor Harald Sandve; Birane Kane et al. (2024). A machine-learned near-well model in OPM Flow. (external link)
• Peter Moritz von Schultzendorff; Jakub Wiktor Both; Jan Martin Nordbotten et al. (2025). An adaptive homotopy continuation solver for incompressible two-phase flow in porous media. (external link)
• Peter Moritz von Schultzendorff; Jakub Wiktor Both; Jan Martin Nordbotten et al. (2023). A machine learned near-well model in OPM. (external link)
• Peter Moritz von Schultzendorff; Jakub Wiktor Both; Jan Martin Nordbotten et al. (2023). Homotopy continuation for a two-phase flow model with machine-learned relative permeabilities. (external link)
• Peter Moritz von Schultzendorff; Tor Harald Sandve; Birane Kane et al. (2024). A machine learned near-well model in OPM. (external link)

Academic book chapter

• Peter Moritz von Schultzendorff; Tor Harald Sandve; Birane Kane et al. (2024). A machine-learned near-well model in OPM Flow.. (external link)

Academic article

• Peter Moritz von Schultzendorff; Tor Harald Sandve; Birane Kane et al. (2024). A Machine-Learned Near-Well Model in OPM Flow. (external link)

See a complete overview of publications in Cristin.