Integrated Reservoir Management: From data to decisions (GEO608)

Content

NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20th for the autumn semester/January 20th for the spring semester.

The course will introduce an integrated reservoir management workflow that starts with data and ends with reservoir management decisions. Students will learn, understand, and apply the following concepts and approaches/methods:

• probabilistic thinking,
• multidisciplinary thinking,
• probabilistic history matching (aka. data assimilation),
• robust optimization under uncertainty,
• closed loop reservoir management,
• big loop model conditioning,
• integrated workflows, and
• value-of-information analysis: data gathering for value creation.

The main topics that will be covered in the course are:

• statistics fundamentals, including uncertainty modeling and quantification and Bayes’ theorem,
• fundamentals of probabilistic history matching,
• fundamentals of robust optimization,
• the integrated workflow: data to models and models to decisions,
• multidisciplinary environment: geologists, geophysicists and reservoir engineers’ roles in the integrated workflow, and
• a case study involving programming.

Learning outcome

Knowledge:

After taking the course, students should understand:

• the importance of taking account of uncertainty in reservoir management,
• the importance of multidisciplinary thinking in reservoir management,
• the essential building blocks of the whole integrated reservoir management workflow and the links and communications between these blocks, and
• how data are used for decision support and value creation.

Skills:

After taking the course, students should be able to:

• quantify, analyze and communicate uncertainties,
• apply Bayesian approaches for probabilistic history matching,
• apply gradient-based algorithms for robust optimization,
• apply the closed loop workflow for reservoir management,
• assess the value of additional data/information in reservoir management contexts, and
• use computer programming to implement the methods and workflows.

General competence:

After taking the course, students should be able to:

• work effectively in a multidisciplinary environment for reservoir management and communicate and discuss reservoir management analyses with geologists, geophysicists, petrophysicists, reservoir engineers, drilling engineers, etc.
• take account of uncertainty in reservoir management and apply probabilistic methods,
• solve reservoir management problems using integrated workflows,
• use data for decision support and to create value from data in reservoir management contexts, and
• analyze and communicate results and findings and write reports on them.

Required prerequisite knowledge

Recommended prerequisites

Reservoir Simulation

Programming in Python, Matlab, etc.

Statistics

Linear Algebra

Exam

Course teacher(s)

Course teacher:

Course coordinator:

Study Program Director:

Study Adviser:

Head of Department:

Method of work

The work will consist of 6 hours of regular lectures and scheduled tutorials per week. Students are expected to spend an additional 6-8 hours per week on self-study and assignments. Programming will be used for assignments. The individual assignments will be based on a reservoir case study.

The course will provide a very short and basic introduction to programming in Matlab and/or Python. Students will need to consult other resources for learning programming if they are not already familiar with it.

Open for

Course assessment

Literature