2.6 Conclusion

Geostatistics techniques are powerful because they take into account both the statistics and the spatial variability of the data. They are an essential part of every reservoir modeling workflow.

Geostatistics are a vast topic that is impossible to cover in a short introduction paper. Aspects of vertical, horizontal and 3D trends as well as the declustering of input data will be discussed in the chapters on geology (chapter 3), petrophysics (chapter 4) and geophysics (chapter 5).
Several important categories of geostatistical techniques could not be presented either by lack of space. Readers interested in plurigaussian simulations can refer to (Armstrong and als, 2011), while those eager to know more about multipoint geostatistics should have a loot at (Mariethoz and Caers, 2014).
(Isaaks and Srivastava, 1990) is a good introduction on geostatistics, as are the different courses on the topic that the CSPG offers every year (www.cspg.org).
Lastly, Alberta has the chance to host one of the world’s leading teams in geostatistics: the Center for Computational Geostatistics in Edmonton, led by Professor Clayton Deutsch (www.ccgalberta.com). Each of their publications is a valuable source of information and of new ideas on geostatistics.

Having reviewed the reservoir modeling workflow in chapter 1 and in chapter 2, the next three chapters will focus on the interaction between reservoir modeling and geology (chapter 3), petrophysics (chapter 4) and geophysics (chapter 5). After this, the focus will shift to the interaction between reservoir modeling and engineering.

Table of contents


Chapter 1 - Overview of the Geomodeling Workflow

Chapter 2 - Geostatistics

Chapter 3 - Geologists and Geomodeling

Chapter 4 - Petrophysicists and Geomodeling

Chapter 5 - Geophysicists and Geomodeling

Chapter 6 - Reservoir Engineers and Geomodeling

Chapter 7 - Reserve Engineers and Geomodeling

Chapter 8 - Production Engineers and Geomodeling

Chapter 9 - Managers and Geomodeling


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