Cristin-resultat-ID: 1837442
Sist endret: 19. februar 2021, 14:07
NVI-rapporteringsår: 2020
Resultat
Vitenskapelig artikkel
2020

4D seismic history matching: Assessing the use of a dictionary learning based sparse representation method

Bidragsytere:
  • Ricardo Soares
  • Xiaodong Luo
  • Geir Evensen og
  • Tuhin Bhakta

Tidsskrift

Journal of Petroleum Science and Engineering
ISSN 0920-4105
e-ISSN 1873-4715
NVI-nivå 2

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2020
Publisert online: 2020
Volum: 195
Sider: 1 - 22
Artikkelnummer: 107763

Importkilder

Scopus-ID: 2-s2.0-85090233954

Klassifisering

Vitenskapsdisipliner

Petroleumsteknologi

Emneord

Ensemble based methods • History matching • Big Data

Beskrivelse Beskrivelse

Tittel

4D seismic history matching: Assessing the use of a dictionary learning based sparse representation method

Sammendrag

It is possible to improve oil-reservoir simulation models by conditioning them on 4D seismic data. Computational issues may arise related to both storage and CPU time due to the size of the 4D seismic dataset. An approach to reducing the computational requirements is to use a sparse representation method, e.g., Dictionary Learning, to select only the main features of the 4D seismic data. However, the introduction of a sparse representation method incurs an additional computational cost. Alternatively, if one uses ensemble-based methods, it is possible to reduce storage and CPU time by projecting the full seismic dataset on a smaller subspace. This paper evaluates the potential of sparsely representing the seismic data. We compare two experiments, one where we condition on the full dataset projected on a smaller subspace, and one where we use Dictionary Learning to represent the data sparsely. We use Dictionary Learning both on the complete 4D seismic dataset and also on a denoised version of the data. We perform the data assimilation in a slightly different formulation of the Iterative Ensemble Smoother Regularized Levenberg–Marquardt together with correlation-based adaptive localization. We apply these methods to the Brugge benchmark case. Experiment results show that sparse representation methods lead to a final ensemble that is closer to the reference solution, and denoising the seismic data before applying the sparse representation allows us to capture the 4D effect better. Thus, using a sparse representation method in 4D-seismic history matching leads to improved results compared to what we obtain when conditioning the models on the projected 4D seismic dataset.

Bidragsytere

Ricardo Vascondellos Soares

Bidragsyterens navn vises på dette resultatet som Ricardo Soares
  • Tilknyttet:
    Forfatter
    ved Matematisk institutt ved Universitetet i Bergen
  • Tilknyttet:
    Forfatter
    ved NORCE Energi og teknologi ved NORCE Norwegian Research Centre AS

Xiaodong Luo

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

Geir Evensen

  • Tilknyttet:
    Forfatter
    ved NORCE Energi og teknologi ved NORCE Norwegian Research Centre AS
  • Tilknyttet:
    Forfatter
    ved Nansen Senter for Miljø og Fjernmåling

Tuhin Bhakta

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