Cristin-resultat-ID: 1750182
Sist endret: 7. februar 2020, 15:13
NVI-rapporteringsår: 2019
Resultat
Vitenskapelig artikkel
2019

Simultaneous assimilation of production and seismic data: application to the Norne field

Bidragsytere:
  • Rolf Johan Lorentzen
  • Tuhin Bhakta
  • Dario Grana
  • Xiaodong Luo
  • Randi Valestrand og
  • Geir Nævdal

Tidsskrift

Computational Geosciences
ISSN 1420-0597
e-ISSN 1573-1499
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2019
Publisert online: 2019

Importkilder

Scopus-ID: 2-s2.0-85075908979

Klassifisering

Vitenskapsdisipliner

Matematikk og naturvitenskap

Emneord

History matching • Norne • Seismisk inversjon/avbildning

Beskrivelse Beskrivelse

Tittel

Simultaneous assimilation of production and seismic data: application to the Norne field

Sammendrag

Automatic history matching using production and seismic data is still challenging due to the size of seismic datasets. The most severe problem, when applying ensemble-based methods for assimilating large datasets, is that the uncertainty is usually underestimated due to the limited number of models in the ensemble compared with the dimension of the data, which inevitably leads to an ensemble collapse. Localization and data reduction methods are promising approaches mitigating this problem. In this paper, we present a new robust and flexible workflow for assimilating seismic attributes and production data. The methodology is based on sparse representation of the seismic data, using methods developed for image denoising. We propose to assimilate production and seismic data simultaneously, and to ensure equal weight on these data types, we apply scaling based on the initial data match. Further, a newly developed flexible correlation-based localization technique is used for both data types. The workflow is successfully implemented for the released Norne benchmark dataset, and an iterative ensemble smoother is used for the simultaneous assimilation of production and seismic data. We show that the methodology is robust and ensemble collapse is avoided. Furthermore, the proposed workflow is flexible, as it can be applied to seismic data or inverted seismic properties, and the methodology requires only moderate computer memory. The results show that through this method, we can successfully reduce the data mismatch for both production data and seismic data.

Bidragsytere

Rolf Johan Lorentzen

  • Tilknyttet:
    Forfatter
    ved NORCE Energi og teknologi ved NORCE Norwegian Research Centre AS

Tuhin Bhakta

  • Tilknyttet:
    Forfatter
    ved NORCE Energi og teknologi ved NORCE Norwegian Research Centre AS

Dario Grana

  • Tilknyttet:
    Forfatter
    ved University of Wyoming

Xiaodong Luo

  • Tilknyttet:
    Forfatter
    ved NORCE Energi og teknologi ved NORCE Norwegian Research Centre AS

Randi Valestrand

  • Tilknyttet:
    Forfatter
    ved NORCE Energi og teknologi ved NORCE Norwegian Research Centre AS
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