Risk management for petroleum reservoir production

A simulation-based study of prediction

James Glimm, Shuling Hou, Hongjoong Kim, Yoon Ha Lee, David H. Sharp, Qian K. Ye, Qisu Zou

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

We consider numerical solutions of the Darcy and Buckley-Leverett equations for flow in porous media. These solutions depend on a realization of a random field that describes the reservoir permeability. The main content of this paper is to formulate and analyze a probability model for the numerical coarse grid solution error. We explore the extent to which the coarse grid oil production rate is sufficient to predict future oil production rates. We find that very early oil production data is sufficient to reduce the prediction error in oil production by about 30%, relative to the prior probability prediction.

Original languageEnglish (US)
Pages (from-to)173-197
Number of pages25
JournalComputational Geosciences
Volume5
Issue number3
DOIs
StatePublished - 2001
Externally publishedYes

Fingerprint

Petroleum reservoirs
Petroleum
Risk Management
Risk management
oil production
petroleum
Prediction
prediction
simulation
Simulation
Sufficient
Grid
Prior Probability
Flow in Porous Media
Probability Model
Prediction Error
Permeability
Random Field
Porous materials
porous medium

Keywords

  • Bayes' prediction
  • Error model
  • Porous media flow

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Risk management for petroleum reservoir production : A simulation-based study of prediction. / Glimm, James; Hou, Shuling; Kim, Hongjoong; Lee, Yoon Ha; Sharp, David H.; Ye, Qian K.; Zou, Qisu.

In: Computational Geosciences, Vol. 5, No. 3, 2001, p. 173-197.

Research output: Contribution to journalArticle

Glimm, James ; Hou, Shuling ; Kim, Hongjoong ; Lee, Yoon Ha ; Sharp, David H. ; Ye, Qian K. ; Zou, Qisu. / Risk management for petroleum reservoir production : A simulation-based study of prediction. In: Computational Geosciences. 2001 ; Vol. 5, No. 3. pp. 173-197.
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