About
David Landa Marban is a Researcher at NORCE Research AS, affiliated with the Energy and Technology division. His primary focus is on mathematical modeling and simulation of subsurface processes, including applications in CO2 storage (CCS), microbial enhanced oil recovery (MEOR), and microbially induced calcite precipitation (MICP). He has expertise in scientific computing, software development for multi-phase flow and reactive transport, and numerical simulations at pore, core, and field scales.
Education: PhD in Applied Mathematics from the University of Bergen (2019), followed by a postdoctoral position at NORCE (2019-2021). Current role as a Research Scientist since 2022.
Research interests emphasize computational geosciences, with projects such as MuPSI (multiscale pressure-stress impacts on CO2 storage) and CSSR (sustainable subsurface resources). He develops open-source tools like pyopmspe11 and contributes to frameworks like OPM Flow for reservoir simulation and history matching.
Key contributions include modeling pressure interference in CO2 storage, machine-learned near-well models, and data-driven predictions for CO2 EOR. His work bridges lab-scale experiments with field-scale applications, addressing challenges in subsurface energy systems and leakage remediation.
