Model prediction under uncertainty using hierarchical models

Behzad Nezhad Karim Nobakht, M. Christie

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

Modelling a physical system such as oil reservoir, however accurate, is subject to uncertainty due to an unrealistic assumption about the model, uncertainty in measured data, and computer model incapability. A realistic assessment of all sources of uncertainty is a challenging task, especially in oil and gas industry. On the other hand, unrealistic assumptions about model/data can lead to biased estimation of model parameters in a history matching progress. It may also be that the practitioners fail to reliably predict the true model behaviour and oilfield properties in case the uncertainty is not modelled appropriately. In this paper, we model the uncertainty using two hierarchical models, maximum likelihood model and a full Bayesian hierarchical model. Moreover, we examine the predictive capability of our real reservoir model based on the modelled uncertainty with regards to the true model. Doing multiple history match trials, a full hierarchical model approach yields better results for our case study than the maximum likelihood approach.

Original languageEnglish
DOIs
Publication statusPublished - 12 Jun 2017
Event79th EAGE Conference and Exhibition 2017 - Paris, France
Duration: 12 Jun 201715 Jun 2017
http://events.eage.org/en/2017/79th-eage-conference-and-exhibition-2017

Conference

Conference79th EAGE Conference and Exhibition 2017
CountryFrance
CityParis
Period12/06/1715/06/17
Internet address

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics

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    Nezhad Karim Nobakht, B., & Christie, M. (2017). Model prediction under uncertainty using hierarchical models. Paper presented at 79th EAGE Conference and Exhibition 2017 , Paris, France. https://doi.org/10.3997/2214-4609.201701024