Modeling and Control for Automation Processes (PET575)

Automated drilling and modeling course provides a detailed knowledge of mathematical drilling models, drilling data, drilling control systems and automated drilling methods.

Course description for study year 2024-2025. Please note that changes may occur.


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Semester tution start


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NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by January 20th.

The course offers comprehensive instruction on the development of both static and dynamic physics-based models. It enhances skills in programming and introduces digital tools for machine learning applications, data analytics techniques, and data management methods. Additionally, it covers control techniques, including various controller designs, automation tools, and industrial control systems. The course also includes practical demonstrations of laboratory systems and digital twin technology for automated systems and real-time automation operations.

Adjustments to the plan may occur.

Learning outcome

Students who successfully complete the course should achieve:

  • understand modeling procedures
  • know about data analytics and machine learning techniques
  • know about control theories and observer design methods
  • know about automation tools and automated process
  • be able to perform basic programming in Matlab or Python
  • be able to process and analyze data
  • be able to implement control strategies for automated systems
  • be able to perform realtime operations through simulators
  • have a general understanding about high-level automated and digital drilling techniques which also can be relevant to other areas
  • have insight into how automated laboratorial systems and software tools can be used for increased understanding and research
  • general programming competence which can be useful in different disciplines

Required prerequisite knowledge


Recommended prerequisites

Physics, mathematics, cybernetic engineering, mechanical engineering, drilling subjects and basic programming in e.g. Matlab or Python


Form of assessment Weight Duration Marks Aid
Written schoolexam 1/1 4 Hours Letter grades Standard calculator

Coursework requirements

Mandatory homework

Course teacher(s)

Course teacher:

Jan Einar Gravdal

Course coordinator:

Dan Sui

Head of Department:

Øystein Arild

Method of work

Classroom teaching, individual assignments/group work, exercises, presentations, homework, projects, visits to laboratory

Overlapping courses

Course Reduction (SP)
Advanced Drilling Technology and Engineering (PET525_1) 5

Open for

Admission to Single Courses at the Faculty of Science and Technology
Computational Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


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