Inverse Theory for Petroleum Reservoir Characterization and History Matching
- Dean S. Oliver, University of Oklahoma
- Albert C. Reynolds, University of Tulsa
- Ning Liu, Chevron Energy Technology Company, California
This book is a guide to the use of inverse theory for estimation and conditional simulation of flow and transport parameters in porous media. It describes the theory and practice of estimating properties of underground petroleum reservoirs from measurements of flow in wells, and it explains how to characterize the uncertainty in such estimates. Early chapters present the reader with the necessary background in inverse theory, probability and spatial statistics. The book demonstrates how to calculate sensitivity coefficients and the linearized relationship between models and production data. It also shows how to develop iterative methods for generating estimates and conditional realizations. The text is written for researchers and graduates in petroleum engineering and groundwater hydrology, and can be used as a textbook for advanced courses on inverse theory in petroleum engineering. It includes many worked examples to demonstrate the methodologies and a selection of exercises.Read more
- Includes introductory background material on random fields and probability, bringing all readers up to the necessary level of understanding
- Provides many worked examples, allowing the reader to easily comprehend the methodology
- Includes numerous applications to fluid flow in porous media, providing enough information for the reader to easily develop specific applications
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- Date Published: May 2008
- format: Adobe eBook Reader
- isbn: 9780511402371
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
2. Examples of inverse problems
3. Estimation for linear inverse problems
4. Probability and estimation
5. Descriptive geostatistics
7. The maximum a posteriori estimate
8. Optimization for nonlinear problems using sensitivities
9. Sensitivity coefficients
10. Quantifying uncertainty
11. Recursive methods
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