Geostatistical Reservoir Modeling

Instructor: Dario Grana (University of Wyoming, United States)

Level: Intermediate

CPD Points: 3

Duration: 6 hours 

Format: 8 video lectures

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Certificate

A certificate of attendance will be available upon completion of all course requirements. After the end of the course, your certificate will remain available for download in your Profile page.

Course description

Reservoir modeling provides a set of techniques to create three-dimensional numerical earth models in terms of elastic, petrophysical and dynamic properties of reservoir rocks. The course focuses on modeling of facies and rock properties from geophysical properties and on quantification of uncertainty of these models. Mathematical and physical models of the reservoir are generally uncertain due to the lack of information, noise in data measurements, approximations and assumptions. Hence, building a reservoir model requires the integration of several disciplines, such as seismic inversion, rock physics, and geostatistics. Seismic inversion aims to transform the measured seismic data into elastic parameters that can be interpreted to determine rock and fluid properties. Rock physics describes a reservoir rock by physical properties such as porosity and compressibility, that affect the seismic response in porous rocks. Rock physics aims to establish relations between these rock and fluid properties and the observed seismic data. Geostatistics aims to provide realistic representations of the reservoirs in terms of structure and spatial distribution of rock and fluid properties by combining geological knowledge and statistical methods. The course covers the fundamental theory of statistical methods for reservoir modeling and uncertainty quantification techniques for reservoir predictions. It is divided into four main parts: fundamentals of statistics, rock physics, geostatistics, and geophysical inverse problems for reservoir characterization. Uncertainty propagation from measured data, through physical models to model predictions will be studied with a focus on seismic data inversion and static reservoir characterization.

Course objectives

After this course, participants will be able to …
1. Generate geostatistical reservoir models
2. Understand physical relations between reservoir parameters and geophysical data
3. Evaluate the uncertainty of model predictions.

Course outline

Chapter 1: Fundamentals of probability and statistics
Chapter 2: Random variables 
Chapter 3: Rock physics relations
Chapter 4: Petro-elastic models
Chapter 5: Spatial correlation
Chapter 6: Geostatistical methods
Chapter 7: Seismic Inversion
Chapter 8: Petrophysical inversion

Participant profile

Geologists and geophysicists in hydrocarbon exploration and production
Researchers in geoscience disciplines
College and graduate students

Pre-requisites

Basic knowledge of geophysics theory and methods

About the instructor

Dario Grana is an associate professor in the Department of Geology and Geophysics at the University of Wyoming. He received a MS in Mathematics at University of Pavia (Italy) in 2005, a MS in Applied Mathematics at University of Milano Bicocca (Italy) in 2006, and a Ph.D. in Geophysics at Stanford University in 2013. He worked four years at Eni Exploration and Production in Milan. He joined the University of Wyoming in 2013. He is author of the book ‘Seismic Reservoir Modeling’, published by Wiley in 2021. He is the recipient of the 2017 EAGE Van Weelden Award, the 2016 SEG Karcher Award, the 2015 Best Paper Award in Mathematical Geosciences, and the 2014 Eni award with Gary Mavko, Tapan Mukerji, and Jack Dvorkin for “pioneering innovations in theoretical and practical rock physics for seismic reservoir characterization”. His main research interests are rock physics, seismic reservoir characterization, geostatistics, data-assimilation, and inverse problems for subsurface modeling.