Vemund Stenbekk Thorkildsen started as a PhD student at the University of Oslo in August 2019. During the next three years he will be working on his project “Use of Passive and Active Electromagnetics in the Barents Sea”, supervised by Professor Leiv Jacob Gelius (UiO), Professor Tor Arne Johansen (UiB) and Professor Alfred Hanssen (UiT).
I did both my bachelor and master’s degree at the University of Oslo. Throughout my education, I developed an interest for processing geophysical data in general, and enhanced data utilization in particular. The latter resulted in my master’s thesis, which focused on diffraction separation and imaging. A diffraction section can reveal information about small scale heterogeneities, inherently present in the input seismic data. However due to their weak energy, reflections tend to dominate the seismic image. Hence, it is important to employ a proper separation technique to utilize this important information.
My PhD research is focused on electromagnetic (EM) methods in the Barents Sea. This research will include studies employing magneto telluric and controlled source EM data, in addition to well logs. We hope that employing EM as supplementary data will improve the basic understanding of the underlying geology beyond what is possible by only employing seismic. While seismic data currently is, and always will be the most powerful tool for hydrocarbon subsurface imaging, the method can struggle to identify oil-water contacts, and is mostly sensitive at lower hydrocarbon saturation. On the contrary, EM is very sensitive to fluids as it provides an estimate of the subsurface resistivity, and can therefore indicate the presence of conductive brine. In the PhD project we will investigate the full potential of EM in the Barents Sea through regional mapping of the geology, case studies of well-known false positives (e.g. the Blåmann gas field), and well log analysis with special emphasis on resistivity logs. I look forward to cross disciplinary collaboration with the researchers and companies in the ARCEx project. The research group at the University of Oslo will also play a vital role in this project, as we aim to employ Machine Learning techniques for tackling the demanding challenges of my PhD project.