Modeling and Control for Automation and Digitalization (PET575)

The course offers comprehensive knowledge of mathematical models, data analytics, control systems, and advanced digital and automation methods applied in industrial contexts. The course emphasizes digital and intelligent technologies, enabling the optimization of processes through automation, predictive modeling, and real-time data-driven techniques, with applications extending beyond drilling systems to other engineering and operational fields.


Course description for study year 2025-2026. Please note that changes may occur.

Facts

Course code

PET575

Version

1

Credits (ECTS)

10

Semester tution start

Spring

Number of semesters

1

Exam semester

Spring

Language of instruction

English

Offered by
Time table

View course schedule

Content

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

The course provides comprehensive instruction on the development of both static and dynamic physics-based models, while also deepening skills in programming. It covers control techniques, including various controller designs, automation processes, and industrial control systems. Additionally, It introduces advanced digital tools for machine learning applications, artificial intelligence, and data analytics techniques. Students will gain knowledge of data management methods crucial for handling industrial data, as well as the implementation of AI-driven solutions. The course integrates practical demonstrations of digital twin technology for automated systems, real-time automation operations, and AI-powered optimization processes.

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

None

Recommended prerequisites

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

Exam

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

Written exam with pen and paper

Coursework requirements

Mandatory homework

Course teacher(s)

Course coordinator:

Dan Sui

Head of Department:

Øystein Arild

Method of work

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

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 course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

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