Recent Advancements to Nonparametric Modeling of Interactions Between Reservoir Parameters
We demonstrate recent advances in nonparametric density estimation and illustrate their potential in the petroleum industry. Here, traditional parametric models and standard kernel methodology may often prove too limited. This is especially the case for data possessing certain complex structures, such as pinch-outs, nonlinearity, and heteroscedasticity. In this paper, we will focus on the Cloud Transform (CT) with directional smoothing and Local Gaussian Density Estimator (LGDE). These are flexible nonparametric methods for density (and conditional distribution) estimation that are well suited for data types commonly encountered in reservoir modeling. Both methods are illustrated with real and synthetic data sets.
KeywordsKernel Density Kernel Density Estimation Kernel Smoothing Kernel Density Estimator Standard Kernel
We thank Arne Skorstad at Emerson Process Management – Roxar AS for providing the real data case. We also thank Dag Tjøstheim and Håkon Otneim for an early draft of the LGDE papers and for providing R code.
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