Uncertainty in Reservoir Characterization and Modeling. S. Geiger, M.J. Blunt, P. Corbett, F. Doster, M. Jackson (Eds.), Springer 2024

Abstract

This book review evaluates Uncertainty in Reservoir Characterization and Modeling, a comprehensive and technically rigorous volume that underscores the indispensable role of uncertainty quantification (UQ) in reservoir engineering. As subsurface resource management grows increasingly complex amid dwindling reserves and environmental scrutiny, the need for robust reservoir models becomes paramount. The book systematically explores key domains of uncertainty—including geological, geophysical, petrophysical, flow modeling, and history matching—while emphasizing integrated, multidisciplinary workflows. It also delves into decision-making frameworks under uncertainty, highlighting probabilistic approaches such as Monte Carlo simulations and stochastic optimization. A major strength of the book lies in its balanced theoretical and practical approach, enhanced by expert contributions and detailed mathematical formulations. While the discussion of emerging technologies such as machine learning is promising, expanded coverage on environmental applications, geomechanics, and software tools would further enrich its value. Despite minor gaps, the book succeeds in presenting uncertainty not as a hindrance but as a manageable dimension in predictive modeling and operational strategy. This review concludes that the book is an essential reference for researchers, students, and practitioners seeking to navigate the uncertainty-laden landscape of modern reservoir management with greater confidence and clarity.

Keywords. Uncertainty Quantification; Reservoir Modeling; Subsurface Resource Management.

JEL. Q32; Q40, C63.

SDGs. SDG7, SDG9.