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- W2740621368 abstract "A longstanding problem in geostatistics is the integration of multiple secondary data in the construction of high resolution models. In the presence of multiple secondary data, a workflow for modeling would involve: (1) integrate secondary data into unit of the primary variable, then (2) integrate secondary derived estimates with the well data. Results from the first integration step will be a probability map/cube or estimate of the continuous variable. These intermediate results are integrated with well data through spatial modeling. Variants of kriging are well established techniques to account for spatial variability. A large variety of methods are used for integrating the secondary data. Probability combination schemes have received much attention recently for this. The main challenge with these probability combination schemes (PCS) is fair consideration of redundant secondary data. Several combination models to meet this challenge are reviewed. Their limitations and possible applications are described. As an alternative to combining probability, direct modeling the multivariate distribution between secondary and primary variables is advanced. In this method, joint distribution is modeled in a nonparametric way and it is refined under marginal constraints. A simple and fast algorithm is developed to impose marginal conditions on initial joint distributions. The effectiveness of the discussed methods is demonstrated through some examples with related references." @default.
- W2740621368 created "2017-08-08" @default.
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- W2740621368 date "2009-01-01" @default.
- W2740621368 modified "2023-09-23" @default.
- W2740621368 title "On Secondary Data Integration" @default.
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