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.

Poster

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

Academic lecture

• von Schultzendorff, Peter Moritz; Sandve, Tor Harald; Kane, Birane et al. (2024). A machine-learned near-well model in OPM Flow. (external link)
• von Schultzendorff, Peter Moritz; Sandve, Tor Harald; Kane, Birane et al. (2024). A machine learned near-well model in OPM. (external link)
• von Schultzendorff, Peter Moritz; Both, Jakub Wiktor; Nordbotten, Jan Martin et al. (2023). Homotopy continuation for a two-phase flow model with machine-learned relative permeabilities. (external link)
• von Schultzendorff, Peter Moritz; Both, Jakub Wiktor; Nordbotten, Jan Martin et al. (2023). A machine learned near-well model in OPM. (external link)

Software

• Keilegavlen, Eirik; Stefansson, Ivar; Fumagalli, Alessio et al. (2024). PorePy: A Simulation Tool for Fractured and Deformable Porous Media, version 1.10.0. (external link)
• Landa-Marbán, David; von Schultzendorff, Peter Moritz (2023). pyopmnearwell: A framework to simulate near well dynamics using OPM Flow. (external link)
• Keilegavlen, Eirik; Stefansson, Ivar; Duran, Omar et al. (2023). PorePy: A Simulation Tool for Fractured and Deformable Porous Media, version 1.8.1. (external link)

Lecture

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

See a complete overview of publications in Cristin.