PhD-students:
| Name: | Håkon Per Gulestø Stavang |
| Affiliation: | University of Bergen |
| Supervisor: | Morten Jakobsen |
| Co-supervisor: | Nazanin Jahani, Koen van Dongen, Ketil Hokstad |
| Associated Epic: | Accurate real-time geophysical data imaging while drilling |
| Period: | 2025-2029 |
| Thesis: | Real-Time Joint Inversion of Borehole EM and Acoustic Data |
| Short about your background: | I graduated from UiB’s master program for theoretical physics. My research interest lies in efficient numerical modeling of physical systems. My thesis was in developing accurate approximation schemes for high energy atoms |
| Describe the objective of your thesis and how you plan to achieve this: | The objective of my thesis is to develop a real-time algorithm that jointly inverts borehole EM and Acoustic data for fast and accurate imaging of the surrounding geology. This will be achieved by first developing separate single inversion algorithms for borehole EM and acoustic data that are fast and reliable, then combining them into a joint inversion workflow. The main feature of the joint inversion will be cross-gradient and rock-physics constraints that will improve convergence and accuracy. Applications of machine learning, such as picking the parameterization effectively and for building low rank approximations are also considered for the project |
| Name: | Seyedeh Fatemeh Afaridoun |
| Affiliation: | University of Stavanger (UiS) |
| Supoervisor: | Kristian Gjerstad |
| Co-Supervisor: | Nazanin Jahani |
| Associated Epic: | Well Placement Optimization |
| Period: | 2026-2029 |
| Thesis: | Real-Time Multi-Objective Well Placement (Geosteering) in the North Sea |
| Short about your background: | Petroleum engineer specializing in multiphase-flow modelling, reservoir simulation and optimization. Academic and industry experience in subsurface engineering, intelligent well systems, and technical evaluation |
| Describe the objective of your thesis and how you plan to achieve this: | The objective of this PhD project is to develop multi-objective decision-making frameworks for well geosteering under geological uncertainty and drilling and operational constraints. The research will investigate how real-time subsurface imaging based on deep-sensing electromagnetic measurements can inform reservoir boundaries predictions and guide well trajectory optimization while accounting for drilling requirements. The optimization framework will combine physics-based models, machine learning techniques, and uncertainty quantification methods to balance multiple objectives. It will be evaluated using representative North Sea geosteering scenarios to demonstrate robust, transparent, and verifiable geosteering workflows |
| Name: | Simon Krane |
| Affiliation: | NTNU, Department of Engineering Cybernetics |
| Supervisor: | Ole Morten Aamo |
| Co-supervisor: | Rodica G. Mihai |
| Associated Epic: | |
| Period: | 2026 - 2029 |
| Thesis: | AI-Assisted Methods for Modelling, Estimation and Control |
| Short about your background: | MSc in Engineering Cybernetics from NTNU and certified process operator, with industrial experience in the treatment and recycling of oil-based waste |
| Describe the objective of your thesis and how you plan to achieve this: | The objective of my PhD is to investigate how AI and machine learning can be combined with methods from systems and control theory to develop models, observers and controllers with meaningful theoretical guarantees. The work will study how physical knowledge, mathematical structure and data can be integrated in learning-based methods, with emphasis on state estimation, fault detection and controller design. The main focus will be on fundamental methodological development and analysis, including properties such as stability and robustness. Relevant dynamical systems, including examples from drilling and well operations where appropriate, may be used to evaluate the developed methods |
| Name: | Tom Staab |
| Affiliation: | University of Stavanger |
| Supervisor: | Dan Sui |
| Co-Supervisor: | Eric Cayeux |
| Associated Epic: | Automated Drilling Operation Planning and Execution |
| Period: | 2026-2029 |
| Thesis: | Optimization of Measurement Strategies for Efficient and Safe Drilling Operations |
| Short about your background: | I obtained my master’s degree in physics from Goethe University Frankfurt in 2026. My studies focused on experimental solid-state physics and quantum materials. I also completed an Erasmus exchange year at the University of Bergen |
| Describe the objective of your thesis and how you plan to achieve this: | My PhD research focuses on developing methods for evaluating and optimizing measurement strategies in drilling operations. The objective is to assess the value of different measurement types and data qualities, and to identify when advanced measurement technologies provide sufficient benefits to justify their cost. To achieve this, I will investigate the trade-offs between measurement quality, uncertainty, and cost by combining mathematical modeling, drilling simulations, sensor fusion, and economic analyses. The resulting framework will support drilling engineers in selecting appropriate measurement strategies during the well planning and execution phases |
PostDoc:
| Name: | Abhinav Das |
| Affiliation: | NORCE Research AS |
| Supervisor: | Rodica Mihai |
| Co-Supervisor: | Benoit Daireaux |
| Work Program: | WP5: Drilling automation and autonomy, across different epics |
| Period: | 2026 - 2029 |
| Project: | Methods and Metrics for decision making in Reinforcement Learning for Drilling |
| Short about your background: | I received my PhD degree in Mathematics from Ulm University, Ulm, Germany, in 2026, where I worked on probabilistic and deep learning models with emphasis on uncertainty quantification for energy price forecastingI |
| Describe the objective of your thesis and how you plan to achieve this: | As a postdoctoral researcher at NORCE AS, Bergen, I will formulate drilling decisions through formal probabilistic state spaces to build rigorous decision-making metrics. My mathematical focus involves deriving state-transition boundaries to ensure dynamic stability, optimizing trade-off frontiers across competing physical constraints, and quantifying epistemic and aleatoric uncertainty under noisy measurements |
