Ensemble-based seismic history matching with data reparameterization using convolutional autoencoder
- Mingliang Liu (University of Wyoming) | Dario Grana (University of Wyoming)
- Document ID
- Society of Exploration Geophysicists
- 2018 SEG International Exposition and Annual Meeting, 14-19 October, Anaheim, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Exploration Geophysicists
- Reservoir characterization, Time-lapse, Machine learning, Reservoir engineering
- 2 in the last 30 days
- 64 since 2007
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In this work, we propose an ensemble-based seismic history matching approach to predict reservoir properties, i.e. porosity and permeability, with uncertainty quantification, using both production and time lapse seismic data. To avoid the common underestimation of uncertainty in ensemblebased optimization approaches, and to make the computation feasible, we introduce the convolutional autoencoder to reparameterize seismic data into a lower dimensional space. We then apply the Ensemble Smoother with Multiple Data Assimilation to optimize an ensemble of reservoir models using the production and re-parameterized seismic data. The proposed methodology is tested on a 2D synthetic case. The inversion results indicate that the method can largely improve the characterization of reservoir models compared to the history-matching scenario with production data only.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 209A (Anaheim Convention Center)
Presentation Type: Oral
|File Size||1 MB||Number of Pages||5|
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