A Comparison between Geostatistical Inversion and Conventional Geostatistical-simulation Practices for Reservoir Delineation
C. Torres-Verdín, A. Grijalba-Cuenca, H. W. J. Debeye, 2006. "A Comparison between Geostatistical Inversion and Conventional Geostatistical-simulation Practices for Reservoir Delineation", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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Geostatistical inversion provides a quantitative way to integrate the high vertical resolution of well logs with the dense aerial coverage of poststack threedimensional seismic amplitude data. A systematic field study is presented in this chapter to understand the relative merits of geostatistical inversion over standard geostatistical-simulation procedures that do not make explicit use of threedimensional seismic amplitude data. It is shown that, by making quantitative use of the poststack seismic amplitude data, geostatistical inversion considerably reduces the space of stochastic realizations that honor both the well-log data and the spatial semivariograms. Sensitivity analysis also shows that geostatistical inversion remains less affected by a perturbation of semivariogram parameters than standard geostatistical simulation. Tests of cross-validation against well-log data show that geostatistical inversion yields additional information over the average trends otherwise obtained with stochastic simulation. In the vicinity of existing wells, geostatistical inversion can potentially infer vertical variations of resolution similar to that of well logs and, at worst, of vertical resolution equal to that of the seismic amplitude data at locations distant from wells. A drawback of geostatistical inversion is the need to convert well-log data from depth to seismic traveltime. In addition, geostatistical inversion may be rendered computationally prohibitive when applied to large seismic and well-log data sets.
Present address: Occidental Petroleum Corporation, Houston, Texas, U.S.A.
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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.