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


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.

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The authors thank Equinor (operator of Norne field) and its license partners ENI and Petoro for the release of the Norne data. Further, the authors acknowledge the IOR Center for Integrated Operations at NTNU for cooperation and coordination of the Norne Cases. We also thank Schlumberger for providing academic software licenses to ECLIPSE and Petrel and CGG for providing an academic software license for HampsonRussell.

Finally, the authors acknowledge Ivar Sandø for very useful geophysical discussions.

Funding information

The authors acknowledge financial support from the CIPR/ NORCE cooperative research project “4D Seismic History Matching” which is funded by industry partners Eni, Petrobras, and Total E&P NORGE, as well as the Research Council of Norway (PETROMAKS2). We also acknowledge the Research Council of Norway and the industry partners, ConocoPhillips Skandinavia AS, Aker BP ASA, Eni Norge AS, Total E&P Norge AS, Equinor ASA, Neptune Energy Norge AS, Lundin Norway AS, Halliburton AS, Schlumberger Norge AS, and Wintershall DEA, of The National IOR Centre of Norway for support.


The views expressed in this paper are the views of the authors and do not necessarily reflect the views of Equinor and the Norne license partners.

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Correspondence to Rolf J. Lorentzen.

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Lorentzen, R.J., Bhakta, T., Grana, D. et al. Simultaneous assimilation of production and seismic data: application to the Norne field. Comput Geosci 24, 907–920 (2020).

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  • History matching
  • Norne field
  • Seismic inversion
  • Petro-elastic models
  • Fluid flow
  • Ensemble smoother
  • Sparse representation