Integrated Reservoir Management: From data to decisions (GEO608)

After having built a reservoir simulation model based on your knowledge of geology/geophysics and static and dynamic reservoir modeling, how would you update the model when additional data (e.g., production data and 4D seismic data) become available, and how would you use the updated model to optimize the injection/production strategy with the consideration of maximizing economic benefits whilst minimizing environmental impacts such as CO2 footprint and water/chemical injection? These two topics - history matching and injection/production optimization are the cores of reservoir management. Reservoir management is a multidisciplinary decision process, aiming to maximize the value creation from the utilization of geological resources (e.g., oil and gas production and CO2 storage). Because we never have complete information about the subsurface, we are always uncertain about the subsurface. Probabilistic thinking is required, and relevant and material uncertainties should be taken account for in reservoir management. In addition, a multidisciplinary environment, where geologists, geophysicists and reservoir engineers can efficiently communicate and work with each other, is essential for reservoir management. This course will focus on a general integrated workflow, which has been implemented in the oil and gas industry (e.g., Equinor), for making high-quality reservoir management decisions, including taking account for uncertainty, using data to update models, and optimizing decisions under uncertainty. 

Course description for study year 2022-2023


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Semester tution start


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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 
  • 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, 
  • a case study involving programming. 

Learning outcome


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, 
  • how data are used for decision support and value creation. 


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, 
  • 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, 
  • analyze and communicate results and findings and write reports on them. 

Required prerequisite knowledge


Recommended prerequisites

Reservoir Simulation

Programming in Python, Matlab, etc.


Linear Algebra


Project reports and oral exam

Form of assessment Weight Duration Marks Aid
Project report 1 - History matching project 3/10 1 Months Letter grades All
Project report 2 - Optimization project 3/10 1 Months Letter grades All
Oral exam 4/10 30 Minutes Letter grades None permitted

This course has an oral exam. It is not offered a continuation exam in this course, and students who fail or want to improve their grade must retake the course the next time it is offered.

Coursework requirements

Students must complete a collection of assignments to access the oral exam.

Course teacher(s)

Course teacher:

Remus Gabriel Hanea

Course coordinator:

Pål Østebø Andersen

Study Program Director:

Lisa Jean Watson

Programme coordinator:

Karina Sanni

Head of Department:

Alejandro Escalona Varela

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.

Overlapping courses

Course Reduction (SP)
Integrated Reservoir Management From Seismic Field Development Planning (PET585_1) 10

Open for

Admission to Single Courses at the Faculty of Science and Technology Exchange programme at Faculty of Science and Technology

Course assessment

Form and/or discussion.


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