S-WoPEc
 
Scandinavian Working Papers in Economics
HomeAboutSeriesSubject/JEL codesAdvanced Search
Department of Business and Management Science, Norwegian School of Economics (NHH) Discussion Papers, Department of Business and Management Science, Norwegian School of Economics (NHH)

No 2017/16:
Creaming - and the depletion of resources: A Bayesian data analysis

Jostein Lillestøl () and Richard Sinding-Larsen ()

Abstract: This paper considers sampling in proportion to size from a partly unknown distribution. The applied context is the exploration for undiscovered resources, like oil accumulations in different deposits, where the most promising deposits are likely to be drilled first, based on some geologic size indicators (“creaming”). A Log-normal size model with exponentially decaying creaming factor turns out to have nice analytical features in this context, and fits well available data, as demonstrated in Lillestøl and Sinding-Larsen (2017). This paper is a Bayesian follow-up, which provides posterior parameter densities and predictive densities of future discoveries, in the case of uninformative prior distributions. The theory is applied to the prediction of remaining petroleum accumulations to be found on the mature part of the Norwegian Continental Shelf.

Keywords: Log-normal distribution; sampling proportional to size; resource prediction; (follow links to similar papers)

JEL-Codes: C00; C10; C11; C13; (follow links to similar papers)

29 pages, November 16, 2017

Before downloading any of the electronic versions below you should read our statement on copyright.
Download GhostScript for viewing Postscript files and the Acrobat Reader for viewing and printing pdf files.

Full text versions of the paper:

2466710    PDF-file
Download Statistics

Questions (including download problems) about the papers in this series should be directed to Stein Fossen ()
Report other problems with accessing this service to Sune Karlsson () or Helena Lundin ().

Programing by
Design by Joachim Ekebom

Handle: RePEc:hhs:nhhfms:2017_016 This page was generated on 2017-11-16 14:47:18