Dynamic data integration in stochastic reservoir models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Developing geostatistical reservoir models that are geologically realistic and correctly reflect production history is important for accurately assessing the uncertainty associated with production forecasts. Conditioning reservoir models to dynamic data is challenging due to the non-linear relationship between the measured flow response data and the model parameters (porosity, permeability etc.). Recently, a novel methodology was presented to integrate geological as well as production information into reservoir models in a probabilistic manner (CIM paper 2002-125). In this paper we investigate the convergence aspects of the algorithm and propose an extension to account for multiple flow domains in a reservoir and locally varying deformation parameters. The improved methodology is validated on a complex reservoir model.

Original languageEnglish (US)
Title of host publicationCanadian International Petroleum Conference 2003, CIPC 2003
PublisherPetroleum Society of Canada (PETSOC)
ISBN (Print)9781613991107
StatePublished - Jan 1 2018
EventCanadian International Petroleum Conference 2003, CIPC 2003 - Calgary, Canada
Duration: Jun 10 2003Jun 12 2003

Other

OtherCanadian International Petroleum Conference 2003, CIPC 2003
CountryCanada
CityCalgary
Period6/10/036/12/03

Fingerprint

Data integration
methodology
conditioning
Porosity
porosity
permeability
history
parameter

All Science Journal Classification (ASJC) codes

  • Geochemistry and Petrology
  • Energy Engineering and Power Technology

Cite this

Kashib, T., & Srinivasan, S. (2018). Dynamic data integration in stochastic reservoir models. In Canadian International Petroleum Conference 2003, CIPC 2003 Petroleum Society of Canada (PETSOC).
Kashib, T. ; Srinivasan, Sanjay. / Dynamic data integration in stochastic reservoir models. Canadian International Petroleum Conference 2003, CIPC 2003. Petroleum Society of Canada (PETSOC), 2018.
@inproceedings{ae7e081474f04f37bddedd8dcb225b4d,
title = "Dynamic data integration in stochastic reservoir models",
abstract = "Developing geostatistical reservoir models that are geologically realistic and correctly reflect production history is important for accurately assessing the uncertainty associated with production forecasts. Conditioning reservoir models to dynamic data is challenging due to the non-linear relationship between the measured flow response data and the model parameters (porosity, permeability etc.). Recently, a novel methodology was presented to integrate geological as well as production information into reservoir models in a probabilistic manner (CIM paper 2002-125). In this paper we investigate the convergence aspects of the algorithm and propose an extension to account for multiple flow domains in a reservoir and locally varying deformation parameters. The improved methodology is validated on a complex reservoir model.",
author = "T. Kashib and Sanjay Srinivasan",
year = "2018",
month = "1",
day = "1",
language = "English (US)",
isbn = "9781613991107",
booktitle = "Canadian International Petroleum Conference 2003, CIPC 2003",
publisher = "Petroleum Society of Canada (PETSOC)",

}

Kashib, T & Srinivasan, S 2018, Dynamic data integration in stochastic reservoir models. in Canadian International Petroleum Conference 2003, CIPC 2003. Petroleum Society of Canada (PETSOC), Canadian International Petroleum Conference 2003, CIPC 2003, Calgary, Canada, 6/10/03.

Dynamic data integration in stochastic reservoir models. / Kashib, T.; Srinivasan, Sanjay.

Canadian International Petroleum Conference 2003, CIPC 2003. Petroleum Society of Canada (PETSOC), 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Dynamic data integration in stochastic reservoir models

AU - Kashib, T.

AU - Srinivasan, Sanjay

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Developing geostatistical reservoir models that are geologically realistic and correctly reflect production history is important for accurately assessing the uncertainty associated with production forecasts. Conditioning reservoir models to dynamic data is challenging due to the non-linear relationship between the measured flow response data and the model parameters (porosity, permeability etc.). Recently, a novel methodology was presented to integrate geological as well as production information into reservoir models in a probabilistic manner (CIM paper 2002-125). In this paper we investigate the convergence aspects of the algorithm and propose an extension to account for multiple flow domains in a reservoir and locally varying deformation parameters. The improved methodology is validated on a complex reservoir model.

AB - Developing geostatistical reservoir models that are geologically realistic and correctly reflect production history is important for accurately assessing the uncertainty associated with production forecasts. Conditioning reservoir models to dynamic data is challenging due to the non-linear relationship between the measured flow response data and the model parameters (porosity, permeability etc.). Recently, a novel methodology was presented to integrate geological as well as production information into reservoir models in a probabilistic manner (CIM paper 2002-125). In this paper we investigate the convergence aspects of the algorithm and propose an extension to account for multiple flow domains in a reservoir and locally varying deformation parameters. The improved methodology is validated on a complex reservoir model.

UR - http://www.scopus.com/inward/record.url?scp=85057428297&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85057428297&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85057428297

SN - 9781613991107

BT - Canadian International Petroleum Conference 2003, CIPC 2003

PB - Petroleum Society of Canada (PETSOC)

ER -

Kashib T, Srinivasan S. Dynamic data integration in stochastic reservoir models. In Canadian International Petroleum Conference 2003, CIPC 2003. Petroleum Society of Canada (PETSOC). 2018