- 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
- 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.
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
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- 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.
Method of work
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.
Sist oppdatert: 05.06.2020