Modeling Complex Reservoirs with Multiple Conditional Techniques: A Practical Approach to Reservoir Characterization
Published:January 01, 2006
H. Beucher-Darricau, B. Doligez, J. M. Yarus, 2006. "Modeling Complex Reservoirs with Multiple Conditional Techniques: A Practical Approach to Reservoir Characterization", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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Modeling petroleum reservoirs and the intricacies of their inherent morphological and structural characteristics requires sophisticated tools. These tools may consist of deterministic or stochastic methods that allow the interwell space to be filled with representative reservoir properties. Integrating a variety of modeling tools may be necessary for characterizing complex reservoirs. In such cases, the relationships among the lithofacies, the shapes and sizes of the sedimentary bodies, and the depositional mode of the litostratigraphic units must be defined.
It is also important to incorporate other types of information. Including conceptual geological information or three-dimensional seismic data enhances the model particularly when borehole and core data are limited. This chapter demonstrates the use of object-based, pixel-based, and hybrid stochastic-simulation techniques using a case study involving data from the Ravenscar Group located along the east coast of Yorkshire in the United Kingdom.
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Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II
Since publication of the first volume of Stochastic Modeling and Geostatistics in 1994, there has been an explosion of interest and activity in geostatistical methods and spatial stochastic modeling techniques. Many of the computational algorithms and methodological approaches that were available then have greatly matured, and new, even better ones have come to the forefront. Advances in computing and increased focus on software commercialization have resulted in improved access to, and usability of, the available tools and techniques. Against this backdrop, Stochastic Modeling and Geostatistics Volume II provides a much-needed update on this important technology. As in the case of the first volume, it largely focuses on applications and case studies from the petroleum and related fields, but it also contains an appropriate mix of the theory and methods developed throughout the past decade. Geologists, petroleum engineers, and other individuals working in the earth and environmental sciences will find Stochastic Modeling and Geostatistics Volume II to be an important addition to their technical information resources.