Abstract
The Edvard Grieg field is a highly complex and heterogeneous reservoir with an extensive fault structure and a mixture of sandstone, conglomerate, and shale. In this paper, we present a complete workflow for history matching the Edvard Grieg field using an ensemble smoother for Bayesian inference. An important aspect of the workflow is a methodology to check that the prior assumptions are suitable for assimilating the data, and procedures to verify that the posterior results are plausible and credible. We thoroughly describe several tools and visualization techniques for these purposes. Using these methods we show how to identify important parameters of the model. Furthermore, we utilize new compression methods for better handling large datasets. Simulating fluid flow and seismic response for reservoirs of this size and complexity requires high numerical resolution and accurate seismic models. We present a novel dual-model concept for a better representation of seismic data and attributes, that deploy different models for the underground depending on simulated properties. Results from history matching show that we can improve data matches for both production data and different seismic attributes. Updated parameters give new insight into the reservoir dynamics, and are calibrated to better represent water movement and pressure.
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Acknowledgements
The authors acknowledge financial support from the NORCE research project “Assimilating 4D Seismic Data: Big Data Into Big Models” which is funded by industry partners, Equinor Energy AS, Repsol Norge AS, Shell Global Solutions International B.V., TotalEnergies EP Norge AS, Wintershall Dea Norge AS, and Aker BP ASA, as well as the Research Council of Norway (PETROMAKS2). We also thank the Edvard Grieg Unit with operator Aker BP ASA and their partners, Wintershall Dea Norge AS and OMW for providing access to their data.
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Open access funding provided by NORCE Norwegian Research Centre AS Open access funding provided by NORCE Norwegian Research Centre AS.
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This study is based on data from an operative field (Edvard Grieg). The data contains sensitive information that cannot be shared without violating non-disclosure agreements. The data assimilation methodology and fluid flow simulator are open and available on the GitHub repositories referred to in this manuscript.
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Lorentzen, R.J., Bhakta, T., Fossum, K. et al. Ensemble-based history matching of the Edvard Grieg field using 4D seismic data. Comput Geosci 28, 129–156 (2024). https://doi.org/10.1007/s10596-024-10275-0
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DOI: https://doi.org/10.1007/s10596-024-10275-0