Conditioning sedimentary models to layer thickness data - An application of ensemble kalman filter

A. Barrera, R. Rmaileh, A. Harikesavanallur, Sanjay Srinivasan, C. Huh

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

Abstract

Geological models consistent with the physics of sediment transport and deposition are increasingly in vogue for the evaluation and development of water, oil and gas reservoirs. Flow-based sedimentary models that consider depositional and physical principles for the distribution of sediments in space can be used to help generate geologically realistic models. However, these models have an important limitation, in that, it is impossible to assimilate reservoir specific information in these models. In order to overcome this limitation, an approach utilizing the Ensemble Kalman Filter (EnKF) technique is presented on this paper. The EnFK technique allows the conditioning of sedimentary models to petrophysical and poro-elastic acoustic measurements from seismic and well-logs. This approach has been tested with a 1-Dimensional sedimentary deposit model under quasi-steady state turbidity flow conditions and the results look promising.

Original languageEnglish (US)
Title of host publicationIAMG 2006 - 11th International Congress for Mathematical Geology: Quantitative Geology from Multiple Sources
PublisherInternational Association for Mathematical Geology, IAMG 2006
ISBN (Print)9782960064407
StatePublished - 2006
Event11th International Congress for Mathematical Geology: Quantitative Geology from Multiple Sources, IAMG 2006 - Liege, Belgium
Duration: Sep 3 2006Sep 8 2006

Other

Other11th International Congress for Mathematical Geology: Quantitative Geology from Multiple Sources, IAMG 2006
CountryBelgium
CityLiege
Period9/3/069/8/06

Fingerprint

Ensemble Kalman Filter
Kalman filter
Conditioning
Kalman filters
conditioning
Model
Sediment Transport
Turbidity
Sediment transport
Sediment
sediment transport
turbidity
Acoustics
Sediments
physics
acoustics
Deposits
Physics
Water
oil

All Science Journal Classification (ASJC) codes

  • Geology
  • Computational Mathematics

Cite this

Barrera, A., Rmaileh, R., Harikesavanallur, A., Srinivasan, S., & Huh, C. (2006). Conditioning sedimentary models to layer thickness data - An application of ensemble kalman filter. In IAMG 2006 - 11th International Congress for Mathematical Geology: Quantitative Geology from Multiple Sources International Association for Mathematical Geology, IAMG 2006.
Barrera, A. ; Rmaileh, R. ; Harikesavanallur, A. ; Srinivasan, Sanjay ; Huh, C. / Conditioning sedimentary models to layer thickness data - An application of ensemble kalman filter. IAMG 2006 - 11th International Congress for Mathematical Geology: Quantitative Geology from Multiple Sources. International Association for Mathematical Geology, IAMG 2006, 2006.
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Barrera, A, Rmaileh, R, Harikesavanallur, A, Srinivasan, S & Huh, C 2006, Conditioning sedimentary models to layer thickness data - An application of ensemble kalman filter. in IAMG 2006 - 11th International Congress for Mathematical Geology: Quantitative Geology from Multiple Sources. International Association for Mathematical Geology, IAMG 2006, 11th International Congress for Mathematical Geology: Quantitative Geology from Multiple Sources, IAMG 2006, Liege, Belgium, 9/3/06.

Conditioning sedimentary models to layer thickness data - An application of ensemble kalman filter. / Barrera, A.; Rmaileh, R.; Harikesavanallur, A.; Srinivasan, Sanjay; Huh, C.

IAMG 2006 - 11th International Congress for Mathematical Geology: Quantitative Geology from Multiple Sources. International Association for Mathematical Geology, IAMG 2006, 2006.

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

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Barrera A, Rmaileh R, Harikesavanallur A, Srinivasan S, Huh C. Conditioning sedimentary models to layer thickness data - An application of ensemble kalman filter. In IAMG 2006 - 11th International Congress for Mathematical Geology: Quantitative Geology from Multiple Sources. International Association for Mathematical Geology, IAMG 2006. 2006