MENY
This is the study programme for 2020/2021. It is subject to change.

### Learning outcome

Knowledge:
• 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
• Geostatistical simulation
• Simulation versus estimation
• Up-gridding & up-scaling
• 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

Skills:
• 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

General qualifications:
Students should understand fundamental logical principles and analyses and be able to communicate their choices and recommendations clearly.

### Contents

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.

None.

### Exam

Weight Duration Marks Aid
Folder evaluation1/1 A - F
The overall course grade will be based on folder evaluation which includes a final exam (30%), a modeling project (40%), and exercises (30%). Each element is percentage-based whilst the overall course grade is letter-based.
• 30% exam (standard calculator is accepted at the exam)
• 40% project
• 30% exercises

Having turned in and passed all required exercises and the compulsory project is a requirement for being evaluated/graded in the course. If a student fails or want to improve the grade, she/he have to take the whole course again the following year.

### Course teacher(s)

Course coordinator
Reidar Brumer Bratvold
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.
Lectures and compulsory exercises. The overall course grade will be based on folder evaluation which includes a final exam (30%), a modeling project (40%), and exercises (30%). Each element is percentage-based whilst the overall course grade is letter-based.
External candidates

### Literature

Literatur will be published as soon as it has been prepared by the course coordinator/teacher

This is the study programme for 2020/2021. It is subject to change.

Sist oppdatert: 05.06.2020