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Inverse Theory for Petroleum Reservoir Characterization and History Matching

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

By: Dean S Oliver (Author), Albert K Reynolds (Author), Ning Liu (Author)

352 pages, 132 b/w photos, 468 illustrations

Cambridge University Press

Hardback | May 2008 | #174934 | ISBN-13: 9780521881517
Availability: Usually dispatched within 6 days Details
NHBS Price: £103.00 $126/€116 approx

About this book

Inverse Theory for Petroleum Reservoir Characterization and History Matching 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.

Inverse Theory for Petroleum Reservoir Characterization and History Matching 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.



1. Introduction
2. Examples of inverse problems
3. Estimation for linear inverse problems
4. Probability and estimation
5. Descriptive geostatistics
6. Data
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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Dean Oliver is the Mewbourne Chair Professor in the Mewbourne School of Petroleum and Geological Engineering at the University of Oklahoma, where he was the Director for four years. Prior to joining the University of Oklahoma, he worked for seventeen years as a research geophysicist, staff reservoir engineer, and research scientist in reservoir characterization for Chevron and for Saudi Aramco. He also spent six years as a professor in the Petroleum Engineering Department at the University of Tulsa. Professor Oliver has been awarded 'best paper of the year' awards from two journals and received the SPE Reservoir Description and Dynamics Award in 2004. He is currently the Executive Editor of SPE Journal. His research interests are in inverse theory, reservoir characterization, uncertainty quantification, and optimization.

Albert Reynolds is Professor of Petroleum Engineering and Mathematics, holder of the McMan chair in Petroleum Engineering, and Director of the TUPREP Research Consortium at the University of Tulsa. He has published over 100 technical articles and one previous book, and is well known for his contributions to pressure transient analysis and optimization based history matching. Professor Reynolds has won the Society of Petroleum Engineers (SPE) Distinguished Achievement Award for Petroleum Engineering Faculty, the SPE Reservoir Description and Dynamics Award and the SPE Formation Award. He became an SPE Distinguished Member in 1999.

Ning Liu holds a Ph.D. from the University of Oklahoma in petroleum engineering and now works as a Reservoir Simulation Consultant at Chevron Energy Technology Company. Dr Liu is a recipient of the Outstanding PhD Scholarship Award at the University of Oklahoma and the Student Research Award from the International Association for Mathematical Geology (IAMG). Her areas of interest are history matching, uncertainty forecasting, production optimization, and reservoir management.

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