Geostatistical Reservoir Modeling and Uncertainty Quantification
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. Mathematical/physical models of the reservoir are generally uncertain due to the lack of information, noise in data measurements, approximations and assumptions. The course focuses on geostatistical methods for reservoir modeling and uncertainty quantification techniques for reservoir predictions. It is divided into four main parts: Geostatistical methods for interpolation and simulation; Rock physics modeling; Geophysical inverse problems; Uncertainty quantification. Uncertainty propagation from measured data, through physical models to model predictions will be studied with a focus on seismic data inversion, static reservoir characterization, structural modeling, dynamic fluid simulation, and time-lapse monitoring. Real case studies will be presented for each topic to illustrate the proposed workflows.
Course Objectives
Upon completion of the course, participants will be able to: • Generate multiple reservoir models; • Understand physical relations between reservoir and geophysical parameters; • Evaluate the uncertainty of model predictions.
Course Outline
The one-day short course will have the following schedule: Introduction • Part 1: Review of statistical analysis and probability • Part 2: Geostatistics and spatial uncertainty • Part 3: Rock physics • Part 4: Geostatistics and spatial uncertainty • Part 5: Seismic inversion • Part 6: Uncertainty quantification • Part 7: Visualizing uncertainty Case studies will be presented for each section.
Participants’ Profile
The course is designed for employees of oil companies in geophysics and reservoir modeling.
Prerequisites
Participants should have knowledge of basic reservoir modeling concepts and of common geophysical data.
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 coauthor of the book ‘Seismic Reflections of Rock Properties’, published by Cambridge University Press in 2014. He is the recipient of the 2017 EAGE Van Weelden Award, the 2016 SEG Karcher Award, and the 2015 Best Paper Award in Mathematical Geosciences. His main research interests are rock physics, seismic reservoir characterization, geostatistics, data-assimilation and inverse problems for subsurface modeling.