Vitenskapelig artikkel   2017

Goodwin, Håvard; Hermansen, Gudmund Horn

Publikasjonsdetaljer

Tidsskrift:

Quantitative Geology and Geostatistics, vol. 19, p. 653–669, 2017

Utgiver:

Springer

Internasjonale standardnumre:

Trykt: 0924-1973

Lenker:

DOI: doi.org/10.1007/978-3-319-46819-8_44

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.