Feature-based Probabilistic Interpretation of Geobodies from Seismic Amplitudes
Published:January 01, 2006
J. Caers, B. G. Arpat, C. A. Garcia, 2006. "Feature-based Probabilistic Interpretation of Geobodies from Seismic Amplitudes", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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Manual interpretation of large volumes of seismic data is a tedious and timeconsuming process. Seismic interpretation relies on extensive expert knowledge of geological rules, as well as strong geophysical interpretation skills. Moreover, in modern approaches to reservoir characterization, a single, deterministic reservoir model interpreted from seismic information is commonly of less interest than the multiple potential interpretations that can arise from the data.Hence, an understanding of the uncertainty associated with seismic interpretation and its quantification are extremely important to reservoir management and decision analysis. In this chapter, several new tools are presented, most of them based on statistical pattern recognition, that can aid the interpreter in constructing a seismic-based reservoir model and provide some uncertainty quantification. Two groups of methods based on unsupervised and supervised pattern detection are discussed. A new geostatistical approach termed feature-based geostatistics is introduced, the aim of which is to accurately reproduce facies shapes. All methods are validated using an interpreted seismic data set representing channel facies from a turbidite sequence in Gabon, west Africa.
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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.