- Compulsory courses
- Modeling for Decision Insight Year 1 / Semester 1
Study points: 10
Reidar Brumer Bratvold
- Modeling and Computational Engineering Year 1 / Semester 1
This course introduces numerical methods and modeling techniques used to solve practical problems. The course provides insights and skills in computational thinking and programming techniques
You will learn the most common numerical methods used to solve complex physical, biological, financial or geological phenomena. Examples of methods are numerically derivation, numerical integration, Monte Carlo and boot strapping methods, inverse methods, numerical solution of common differential equations, simulated annealing, and colony optimization, lattice Boltzmann models, random walk models, box (compartment) models.
The primary programming language is Python. Through assignments, you will learn how to set up mathematical models of a phenomenon, develop algorithms, implement them, and investigate the strength and limitations of the solution method and the mathematical model.
Read more about Modeling and Computational EngineeringStudy points: 10
- Mathematical and Numerical Modelling of Transport Processes Year 1 / Semester 2
A central part of the course is to consider 1D models relevant for simulating single- and two-phase flow in porous media. Analytical solutions of these transport equations are discussed as well as principles for use of numerical methods.
Read more about Mathematical and Numerical Modelling of Transport ProcessesStudy points: 10
Pål Østebø Andersen
- Automated Drilling and Modeling Year 1 / Semester 2
Automated drilling and modeling course provides a detailed knowledge of mathematical drilling models, drilling data, drilling control systems and automated drilling methods.
Read more about Automated Drilling and ModelingStudy points: 10
Jan Einar Gravdal
- Master Thesis in Computational Engineering Year 2 / Semester 3
The master thesis is an independent project in which you will apply the knowledge acquired during your studies on solving a given assignment. It is through this assignment that you will show your abilities and qualities as a coming engineer.
The assignment will normally be carried out during the last semester of your studies. At this stage you will have acquired the knowledge and know-how needed for accomplishing a relevant assignment in your studies.
Read more about Master Thesis in Computational EngineeringStudy points: 30
- Elective courses
- Geostatistical Modeling Year 1 / Semester 2
Study points: 10
Reidar Brumer Bratvold
- Computational Fluid Dynamics (CFD) Year 1 / Semester 2
The course deals with computational analysis of fluid flow. The first part of the course deals with theory and numerical methods. In the second part the students work in groups of 2-3 with a project where theory and computer software is applied on a practical problem.
Read more about Computational Fluid Dynamics (CFD)Study points: 5
Knut Erik Teigen Giljarhus
- Integrated Reservoir Management From Seismic Field Development Planning Year 1 / Semester 2
Students will learn, get familiar, and apply the following concepts:
- Closed Loop Reservoir Management
- Big Loop Model conditioning
- Multidisciplinary way of thinking
- Integrated workflows
The course will introduce an integrated reservoir management workflow that starts with seismic and ends with the field development planning.
Read more about Integrated Reservoir Management From Seismic Field Development PlanningStudy points: 10
Remus Gabriel Hanea
- Developing Research Skills Year 2 / Semester 3
This course is an elective which aims to help students improve their research, writing, and presentation skills in preparation for the thesis. The course is handled as a seminar and in close collaboration with the selected supervisor.
Read more about Developing Research SkillsStudy points: 10
Lisa Jean Watson
- Reservoir Simulation Year 2 / Semester 3
Advanced reservoir simulation, theory and practical work.
Read more about Reservoir SimulationStudy points: 10
Dag Chun Standnes
- Economics and Decision Analysis for Engineers Year 1 / Semester 1
This course teaches the skills required for a key component of an Engineer's job - creating value by making decisions that yield optimal returns on the allocation of human and financial resources. Engineers perform technical work to support the business objectives of the organization they work for (corporation, government). It is therefore important that they understand that business because it will influence the judgments they make.
Economic evaluations provide the main source of the organization's information by which investment and operational decisions are made regarding the most effective use of resources. There are many subtleties and assumptions that underlie the apparently straight-forward economic calculations that are often seen. Consequently, a fundamental understanding of the concepts behind economic evaluation and of techniques for performing them within a petroleum context, are essential skills.
The many uncertainties inherent to the offshore business create considerable uncertainty in the value that can be realized from resource-allocation decisions. Consequently, there will be a strong emphasis on evaluating the impacts of uncertainty, managing its resultant risks and planning to exploit its up-side potential.
Read more about Economics and Decision Analysis for EngineersStudy points: 10
Reidar Brumer Bratvold
- Statistical learning Year 2 / Semester 3
Introduction to statistical learning, multiple linear regression, classification, resampling methods, model selection/regularization, non-linearity, tree-based methods.
Read more about Statistical learningStudy points: 10
Jan Terje Kvaløy
- Other elective courses 1st, 2nd and 3rd semester
- Introduction to data science Year 1 / Semester 1
The course will provide a knowledge and experience in data engineering tasks and will accustom students with data science project lifecycle.
Read more about Introduction to data scienceStudy points: 10
- Numerical Modeling, Basic Course Year 1 / Semester 1
The course is divided in two parts. Part I is an introduction to numerical mathematics, with basic theory and methods for solving numerical computational problems with and without the use of computers. Part II is a data laboratory course, which gives an introduction to the practical use of computers for scientific and technical computations.
Read more about Numerical Modeling, Basic CourseStudy points: 10
- Differential Equations Year 1 / Semester 1
Introduction to ordinary and partial differential equations.
Read more about Differential EquationsStudy points: 10
- Computational Reservoir and Well Modeling Year 1 / Semester 1
This course gives an introduction to how mathematical models and computational methods can be used to describe flow processes taking place in reservoirs and wells. The student will get an introduction to how such models can be solved by analytical and numerical techniques. Models in general are very much used in the petroleum industry.
Read more about Computational Reservoir and Well ModelingStudy points: 10
- Probability and Statistics 2 Year 1 / Semester 1
Basic issues in probability. Presentation of a number of commonly used probability distributions. Short introduction to extreme-value statistic. Estimation, in particular the maximum likelihood principle,and confidence intervals in various situations. Brief introduction to Bayesian statistics.Stochastic processes, in particular Poisson processes and Markov processes. Theory and areas for applications of the various methods will be covered.
Read more about Probability and Statistics 2Study points: 10
Tore Selland Kleppe
- Discrete Simulation and Performance Analysis Year 1 / Semester 2
This course first introduces Petri net theory; then, the theory is used for modeling, simulation and performance analysis of discrete event systems.
Read more about Discrete Simulation and Performance AnalysisStudy points: 10
- Machine learning Year 1 / Semester 2
The course focuses on methods for learning the underlying structures from data and to train models that can make predictions when presented with new data. Such predictions can typically involve the discrimination between different categories of data, or pattern classification, which will be the main focus of this course.
Read more about Machine learningStudy points: 10
Trygve Christian Eftestøl
- Statistical modeling and simulation Year 2 / Semester 3
This course provides a foundation for problem solving in technology, science and economy using statistical modeling, simulation and analysis.
Read more about Statistical modeling and simulationStudy points: 10
Stein Andreas Bethuelsen
This is the study programme for 2019/2020. It is subject to change.