Incorporating Secondary Information Using Direct Sequential Cosimulation
A. Soares, J. A. Almeida, L. Guerreiro, 2006. "Incorporating Secondary Information Using Direct Sequential Cosimulation", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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Direct sequential simulation is a new approach to stochastic simulation that does not require any prior transformation of the original variable or any posterior transformation of simulated values. To simulate continuous variables, the algorithm uses the estimated local mean and variance to sample from a global cumulative distribution function. An advantage of this approach over sequential Gaussian simulation or sequential indicator simulation is that it accommodates the joint simulation of several variables under the direct simulation principle; that is, coregionalization modeling is performed with the original variables. The direct joint simulation methodology is presented in this chapter, and a representative set of examples is provided to illustrate the potential of the method to incorporate secondary information in the characterization of oil reservoirs.
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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.