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Seismic Reservoir Modeling
Theory, Examples, and Algorithms
Dario Grana
University of Wyoming
Laramie, WY, USA
Tapan Mukerji
Stanford University
Stanford, CA, USA
Philippe Doyen
Independent Consultant
London, UK
This edition first published 2021
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Preface
Geophysical data are commonly acquired to understand the structure of the subsurface for a variety of applications, such as energy resource exploration, natural hazards, groundwater, and surface processes. Seismic reservoir modeling is an interdisciplinary field that integrates physics, mathematics, geology, statistics, and computer science. The goal of seismic reservoir characterization studies is to predict three‐dimensional models of rock and fluid properties in the subsurface based on the available geophysical data, such as borehole measurements and surface geophysical surveys. Mathematical methods are required to solve the so‐called inverse problem in which the properties of interest are estimated from their geophysical response. The solution to this problem is generally non‐unique owing to the data errors, their limited resolution, the physics approximations, and the natural variability and heterogeneity of porous rocks. Multiple model realizations of rock and fluid properties, constrained by geophysical measurements and prior geological information, can be generated by combining statistical methods and computer science algorithms.
The main goal of this book is to bring together in one place basic and advanced methodologies of the current state of the art in seismic reservoir characterization. This work finds inspiration in the book Seismic Reservoir Characterization by Philippe Doyen. For the rock physics part, it strongly relies on The Rock Physics Handbook by Gary Mavko, Tapan Mukerji, and Jack Dvorkin, whereas for the geostatistics part, it relies on Geostatistical Reservoir Modeling by Michael Pyrcz and Clayton Deutsch, and Multiple‐Point Geostatistics by Gregoire Mariethoz and Jef Caers. Unlike other textbooks on seismic reservoir modeling, this book offers a detailed description of the mathematical–physical methods used for quantitative seismic interpretation. Indeed, it focuses on mathematical methods for the estimation of petrophysical properties, such as porosity, mineral volumes, and fluid saturations from geophysical data and attributes. Owing to the non‐uniqueness of the solution, we present a set of probabilistic methods that aim to estimate the most likely model as well as the uncertainty associated with the predictions. The model uncertainty can be quantified using probability distributions, confidence intervals, or a set
