Course content

Traditional education in petroleum engineering does not provide sufficient exposure to digitalization and automation technologies. A strong foundation in these areas enables petroleum engineers and decision-makers to recognize opportunities for digital transformation within their domains, communicate effectively with technology developers and vendors, maximize the value of these innovations, assess their business impact, and identify and mitigate associated risks. This course aims to bridge this gap for engineers, researchers, and decision-makers by integrating digital and automation concepts into drilling operations.

The course covers the following topics, with a focus on applying digitalization, automation, and data-driven approaches in the oil and gas industry:

• Measurement and collection of drilling data, including real-time and raw data acquisition.
• Data transmission and downhole measurement techniques.
• Introduction to Python programming for drilling applications.
• Application of Python and the NumPy library for various drilling calculations.
• Utilizing the Pandas library for data handling in drilling processes, including well hydraulics, managed pressure drilling (MPD), casing load analysis (CML), drilling problem detection, well control, and trajectory design.
• Visualization and analysis of drilling data using Matplotlib.
• Automation of drilling sequences and analysis using Seaborn and Scikit-Learn libraries.
• Machine learning techniques relevant to drilling, such as linear regression, random forests, decision trees, and gradient boosting.
• Implementation of machine learning models for drilling and geoscience applications, including rate of penetration (ROP) optimization and formation prediction.

Learning outcome

The objective is to turn petroleum engineers and decision makers into educated and efficient users of digital and automation technologies, understanding their foundations, benefits and limitations. After completing the course, the participants should be able to identify areas for potential applications of automation and digital technologies in upstream oil and gas industry, suggest an appropriate type of digital/automation technology (Python programming and AI application), critically review its implementation and operation plans, identify risks and risk mitigating actions and evaluate its business impact.

Learning methods and activities

The course is part of NTNU's Continuing Education courses and has a course fee. See NTNU Continuing Education. The course will be given under the condition of sufficient number of participants. The course is session-based, with two sessions per semester, of 3 days each, with self-study, exercises, and a project between the sessions. The course will be given in English.

Recommended previous knowledge

Experience in upstream petroleum technology.

Required previous knowledge

Engineer/Bachelor's/Master's degree in a technical discipline.

Course materials

Lecture notes, selected papers and publications.