Course description for study year 2021-2022. Please note that changes may occur.
Faculty of Science and Technology, Department of Energy Resources
Understand the main methods used in geostatistics and how the can and cannot support decisions
Know how to measure and model spatial continuity (variograms)
Understand grid types, design and their relation to reservoir features and model purpose
Geostatistical estimation including kriging
Estimation of dependent variables
Simulation versus estimation
Understand sensitivity analysis and the information it provides
Conduct probabilistic analysis to general additional insights and understand the impact of uncertainty
Identify how to effectively communicate the insights derived from your model
Have the skills needed to build a good geostatistical model and to use it in generating powerful insights into the decision situation
Have the skills needed to implement the basic geostatistical methods to analyze data and to estimate and simulate reservoir properties conditioned on data by using Python and other programming tools
Students should understand fundamental logical principles and analyses and be able to communicate their choices and recommendations clearly.
Statistics as a traditional science has considerable limitations when you apply it in the earth and environmental sciences. Key to these fields is the spatial aspect or nature of the data. A sample or measurement is often attached to a spatial coordinate (x,y,z) describing where the sample was taken. Traditional statistics very often neglects this spatial context and simply works with the data as they are. However, from our own geological and environmental experiences, we know that samples located close together are more "related" to each other, and this relationship may be useful to us when interpreting our data. Geostatistics is a field that deals explicitly with data distributed in space or time and aims at explicitly modeling the spatial relationship between data.
The focus of this course is the basic science, technology and related assumptions involved in applying geostatistics. The emphasis is on providing students with knowledge of the fundamentals of geostatistics. The core of the course is around data analysis and constructing static geology models. Data sources, quality, relevance and choice of modeling techniques will be covered. This is followed by classical gridding, mapping and contouring. Kriging is introduced as a data-driven (variograms) form of classical mapping (estimation) and a means of data integration. Simulation techniques are introduced as a means of modeling heterogeneity and uncertainty. Scaling (grids and properties) for the purpose of reservoir simulation is the final topic. Python and other programming tools will be used for geostatistical modeling, preparing spatial data, scripting geostatistical workflows, and constructing visualizations to communicate spatial data.
What are the benefits of building and using geostatistical models, as opposed to relying on mental models or just "gut feel?" The primary purpose of modeling is to generate decision insight; by which we mean an improved understanding of the decision situation at hand. While mathematical models consist of numbers and symbols, the real benefit of using them is to make better decisions. Better decisions results from improved understanding, not just the numbers themselves.
Required prerequisite knowledge
Eksamen / vurdering
Written exam, portfolio evaluation and project report
A - F
A - F
A - F
The overall course grade will be based on continuous evaluation which includes a final exam, a modeling Project, and a Portfolio. Each element is percentage-based whilst the overall course grade is letter-based. The final grade is made up of:
• 30% Written exam
• 40% Project report*
• 30% Portfolio which consist of 4 written assignments
*The project is an extended analysis which must be presented in a written report over no more than 20 pages A resit exam is offered for students who do not pass the written exam. Students who do not pass or want to improve their grade in the project report or portfolio must take these assessment parts when the course is offered again.
Lectures and compulsory exercises.
Reidar Brumer Bratvold
Head of Department:
Alejandro Escalona Varela
Method of work
The work will consist of 6 hours of lecture and scheduled tutorials per week. Students are expected to spend an additional 6-8 hours a week on self-study, assignments, and project.