Risk management for petroleum reservoir production: A simulation-based study of prediction

James Glimm; Shuling Hou; Hongjoong Kim; Yoon Ha Lee; David H. Sharp; Kenny Ye; Qisu Zou

doi:10.1023/A:1013715604079

Risk management for petroleum reservoir production: A simulation-based study of prediction

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

Research output: Contribution to journal › Article › peer-review

20 Scopus citations

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 language	English (US)
Pages (from-to)	173-197
Number of pages	25
Journal	Computational Geosciences
Volume	5
Issue number	3
DOIs	https://doi.org/10.1023/A:1013715604079
State	Published - 2001
Externally published	Yes

Keywords

Bayes' prediction
Error model
Porous media flow

ASJC Scopus subject areas

Computer Science Applications
Computers in Earth Sciences
Computational Theory and Mathematics
Computational Mathematics

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title = "Risk management for petroleum reservoir production: A simulation-based study of prediction",

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.",

keywords = "Bayes' prediction, Error model, Porous media flow",

author = "James Glimm and Shuling Hou and Hongjoong Kim and Lee, {Yoon Ha} and Sharp, {David H.} and Kenny Ye and Qisu Zou",

note = "Funding Information: The work of J.G. is supported in part by the NSF Grant DMS-0102480, the Army Research Office Grant DAAG559810313, the Department of Energy Grants DE-FG02-98ER25363 and DE-FG02-90ER25084, and Los Alamos National Laboratories under Contract #C738100182X. S.H. and D.S. are supported by the U.S. Department of Energy under contract W-7405-ENG-36. Y.L. is supported by the Department of Energy grant DE-FG02-90ER25084. H.K. and K.Y. are supported in part by Los Alamos National Laboratories under Contract # C738100182X.",

year = "2001",

doi = "10.1023/A:1013715604079",

language = "English (US)",

volume = "5",

pages = "173--197",

journal = "Computational Geosciences",

issn = "1420-0597",

publisher = "Springer Netherlands",

number = "3",

}

TY - JOUR

T1 - Risk management for petroleum reservoir production

T2 - A simulation-based study of prediction

AU - Glimm, James

AU - Hou, Shuling

AU - Kim, Hongjoong

AU - Lee, Yoon Ha

AU - Sharp, David H.

AU - Ye, Kenny

AU - Zou, Qisu

N1 - Funding Information: The work of J.G. is supported in part by the NSF Grant DMS-0102480, the Army Research Office Grant DAAG559810313, the Department of Energy Grants DE-FG02-98ER25363 and DE-FG02-90ER25084, and Los Alamos National Laboratories under Contract #C738100182X. S.H. and D.S. are supported by the U.S. Department of Energy under contract W-7405-ENG-36. Y.L. is supported by the Department of Energy grant DE-FG02-90ER25084. H.K. and K.Y. are supported in part by Los Alamos National Laboratories under Contract # C738100182X.

PY - 2001

Y1 - 2001

N2 - 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.

AB - 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.

KW - Bayes' prediction

KW - Error model

KW - Porous media flow

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JO - Computational Geosciences

JF - Computational Geosciences

IS - 3

ER -

Risk management for petroleum reservoir production: A simulation-based study of prediction

Abstract

Keywords

ASJC Scopus subject areas

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