Parameter identification in large-scale models for oil and gas production

J.F.M. Doren, van, P.M.J. Hof, Van den, J.D. Jansen, O.H. Bosgra

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)

Abstract

Models used for model-based (long-term) operations as monitoring, control and optimization of oil and gas reservoirs are often first principles models. They are the result of partial differential equations being discretized, leading to nonlinear models that are largescale in terms of number of states and parameters. Estimating a large number of parameters from measurement data leads to problems of identifiability and consequently to inaccurate identification results. This is problematic since the models are used for model predictions and control strategies. In this paper options are given to deal with the lack of identifiability: approximating the model structure while retaining the physical interpretation of the parameters, redefining the model structure using parameters with e.g. a geological meaning, and thirdly adding additional prior information to the identification problem. These options are illustrated with examples taken from oil and gas reservoir engineering
Original languageEnglish
Title of host publicationProceedings of the 18th IFAC World Congress, August 28 - September 2, 2011, Milano, Italy
EditorsS. Bittanti, A. Cenedese, S. Zampieri
Place of PublicationOxford
PublisherPergamon
Pages10857-10862
Publication statusPublished - 2011
Event18th World Congress of the International Federation of Automatic Control (IFAC 2011 World Congress) - Milano, Italy
Duration: 28 Aug 20112 Sep 2011
Conference number: 18
http://www.ifac2011.org/

Conference

Conference18th World Congress of the International Federation of Automatic Control (IFAC 2011 World Congress)
Abbreviated titleIFAC 2011
CountryItaly
CityMilano
Period28/08/112/09/11
Internet address

Fingerprint Dive into the research topics of 'Parameter identification in large-scale models for oil and gas production'. Together they form a unique fingerprint.

Cite this