Indicator-based data assimilation with localization for updating models using limited ensemble size

D. Kumar, S. Srinivasan

Research output: Contribution to conferencePaper

Abstract

Ensemble-based methods of data assimilation rely on the deduction of statistics from an ensemble of models to perform model updates. The reliability of the updates depend upon the size of ensemble being used. A modified version of indicator-based data assimilation (InDA) with localization, is presented for reliable assimilation of production data to update an ensemble of models, of limited size. This affords the benefits of InDA to handle the non-Gaussian distribution of the parameters and non-linear relationships between observed data and update parameters, while using a limited ensemble size to achieve reliable updates. The modified InDA method relies on a localization technique which limits the update of model parameters beyond a certain spatial lag away from observed data location, thus limiting the updates caused by spurious correlations which appear because of the limited size of the ensemble. The localization method weighs the information obtained from different observation data points based on their respective spatial distances to the location where updates are performed. Because of the localization scheme, the observations at larger distances from the update locations have smaller contributions to the final updates. This method can be used to reliably ascertain the residual uncertainty in model parameters, as sequential data assimilation proceeds. The knowledge of uncertainty can be used in the prediction of future state of the reservoir.

Original languageEnglish (US)
Pages278-282
Number of pages5
StatePublished - Jan 1 2019
Event20th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2019 - State College, United States
Duration: Aug 10 2019Aug 16 2019

Conference

Conference20th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2019
CountryUnited States
CityState College
Period8/10/198/16/19

Fingerprint

Model Updating
Data Assimilation
data assimilation
Ensemble
Update
indicator
Uncertainty
Model
parameter
method
Deduction
prediction
Limiting
Statistics

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)
  • Mathematics (miscellaneous)

Cite this

Kumar, D., & Srinivasan, S. (2019). Indicator-based data assimilation with localization for updating models using limited ensemble size. 278-282. Paper presented at 20th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2019, State College, United States.
Kumar, D. ; Srinivasan, S. / Indicator-based data assimilation with localization for updating models using limited ensemble size. Paper presented at 20th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2019, State College, United States.5 p.
@conference{eb5cd6b36c514f7b98634aca588790e4,
title = "Indicator-based data assimilation with localization for updating models using limited ensemble size",
abstract = "Ensemble-based methods of data assimilation rely on the deduction of statistics from an ensemble of models to perform model updates. The reliability of the updates depend upon the size of ensemble being used. A modified version of indicator-based data assimilation (InDA) with localization, is presented for reliable assimilation of production data to update an ensemble of models, of limited size. This affords the benefits of InDA to handle the non-Gaussian distribution of the parameters and non-linear relationships between observed data and update parameters, while using a limited ensemble size to achieve reliable updates. The modified InDA method relies on a localization technique which limits the update of model parameters beyond a certain spatial lag away from observed data location, thus limiting the updates caused by spurious correlations which appear because of the limited size of the ensemble. The localization method weighs the information obtained from different observation data points based on their respective spatial distances to the location where updates are performed. Because of the localization scheme, the observations at larger distances from the update locations have smaller contributions to the final updates. This method can be used to reliably ascertain the residual uncertainty in model parameters, as sequential data assimilation proceeds. The knowledge of uncertainty can be used in the prediction of future state of the reservoir.",
author = "D. Kumar and S. Srinivasan",
year = "2019",
month = "1",
day = "1",
language = "English (US)",
pages = "278--282",
note = "20th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2019 ; Conference date: 10-08-2019 Through 16-08-2019",

}

Kumar, D & Srinivasan, S 2019, 'Indicator-based data assimilation with localization for updating models using limited ensemble size', Paper presented at 20th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2019, State College, United States, 8/10/19 - 8/16/19 pp. 278-282.

Indicator-based data assimilation with localization for updating models using limited ensemble size. / Kumar, D.; Srinivasan, S.

2019. 278-282 Paper presented at 20th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2019, State College, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Indicator-based data assimilation with localization for updating models using limited ensemble size

AU - Kumar, D.

AU - Srinivasan, S.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Ensemble-based methods of data assimilation rely on the deduction of statistics from an ensemble of models to perform model updates. The reliability of the updates depend upon the size of ensemble being used. A modified version of indicator-based data assimilation (InDA) with localization, is presented for reliable assimilation of production data to update an ensemble of models, of limited size. This affords the benefits of InDA to handle the non-Gaussian distribution of the parameters and non-linear relationships between observed data and update parameters, while using a limited ensemble size to achieve reliable updates. The modified InDA method relies on a localization technique which limits the update of model parameters beyond a certain spatial lag away from observed data location, thus limiting the updates caused by spurious correlations which appear because of the limited size of the ensemble. The localization method weighs the information obtained from different observation data points based on their respective spatial distances to the location where updates are performed. Because of the localization scheme, the observations at larger distances from the update locations have smaller contributions to the final updates. This method can be used to reliably ascertain the residual uncertainty in model parameters, as sequential data assimilation proceeds. The knowledge of uncertainty can be used in the prediction of future state of the reservoir.

AB - Ensemble-based methods of data assimilation rely on the deduction of statistics from an ensemble of models to perform model updates. The reliability of the updates depend upon the size of ensemble being used. A modified version of indicator-based data assimilation (InDA) with localization, is presented for reliable assimilation of production data to update an ensemble of models, of limited size. This affords the benefits of InDA to handle the non-Gaussian distribution of the parameters and non-linear relationships between observed data and update parameters, while using a limited ensemble size to achieve reliable updates. The modified InDA method relies on a localization technique which limits the update of model parameters beyond a certain spatial lag away from observed data location, thus limiting the updates caused by spurious correlations which appear because of the limited size of the ensemble. The localization method weighs the information obtained from different observation data points based on their respective spatial distances to the location where updates are performed. Because of the localization scheme, the observations at larger distances from the update locations have smaller contributions to the final updates. This method can be used to reliably ascertain the residual uncertainty in model parameters, as sequential data assimilation proceeds. The knowledge of uncertainty can be used in the prediction of future state of the reservoir.

UR - http://www.scopus.com/inward/record.url?scp=85075012829&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85075012829&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85075012829

SP - 278

EP - 282

ER -

Kumar D, Srinivasan S. Indicator-based data assimilation with localization for updating models using limited ensemble size. 2019. Paper presented at 20th Annual Conference of the International Association for Mathematical Geosciences, IAMG 2019, State College, United States.