Industrial PhD: Machine Learning techniques for slender structures in the offshore industry

My name is Halvor Snersrud Gustad and I am a first year industrial-Ph.D. student at TechnipFMC in cooperation with the Department of Mathematical Sciences of the Norwegian University of Science and Technology (NTNU) and the European training network THREAD. I am Ph.D. student in the Industrial Ph.D. Scheme of the Research Council of Norway.

Background for the PhD

TechnipFMC is a company delivering projects mostly within the offshore oil and gas industry. Fatigue of oil wells and risers (long slender beams) has been a major concern of the oil and gas industry during recent years. Wellheads were originally not designed for fatigue, and the cyclic loads that cause fatigue during operations have been increasing since equipment placed on top of the well is becoming heavier. Additionally, riser tensioning systems have more hysteresis. Fatigue may cause wells to be abandoned and riser systems to be scrapped at a very high cost to the owners.

The traditional methods for calculating fatigue is based on methods that utilizes a large safety margin to account for errors in the mathematical model and measurement data. This implies that the predicted fatigue will be significantly larger than the actual fatigue, forcing the owners to abandon the well before it is necessary.


We aim to develop numerical methods for slender structures such as risers or other slender offshore equipment. These methods and models will be developed using acquired data from ongoing offshore operations in cooperation with the ETN MSCA training programme THREAD project. We will complement our models with data using artificial neural network architectures with the goal of improving the newly developed models further.

Having developed improved models and methods for the offshore equipment we eventually aim to decrease the safety margin in the calculations due to an increased trust in the numbers.

Industrial Ph.D. Scheme – Doctoral Projects in Industry

The Research Council of Norway has established the Industrial Ph.D. Scheme to boost research efforts and long-term competence-building for Norwegian trade and industry through the recruitment of doctoral candidates.

The formal applicant and Project Owner must be a Norwegian company that carries out economic activity in Norway.

Doctoral candidates involved in four-year projects are to dedicate 75 per cent of a full time position to the project and 25 per cent to other tasks. The PhD fellowship is financed up to 50% from the RCN and 50% from the company.

Best regards,
Halvor Snersrud Gustad

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