Enhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filter
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionEarth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Electrical Engineering Program
Physical Science and Engineering (PSE) Division
Date
2015-11-12Online Publication Date
2015-11-12Print Publication Date
2015-07Permanent link to this record
http://hdl.handle.net/10754/583292
Metadata
Show full item recordAbstract
Recovering information on subsurface geological features, such as flow channels, holds significant importance for optimizing the productivity of oil reservoirs. The flow channels exhibit high permeability in contrast to low permeability rock formations in their surroundings, enabling formulation of a sparse field recovery problem. The Ensemble Kalman filter (EnKF) is a widely used technique for the estimation of subsurface parameters, such as permeability. However, the EnKF often fails to recover and preserve the channel structures during the estimation process. Compressed Sensing (CS) has shown to significantly improve the reconstruction quality when dealing with such problems. We propose a new scheme based on CS principles to enhance the reconstruction of subsurface geological features by transforming the EnKF estimation process to a sparse domain representing diverse geological structures. Numerical experiments suggest that the proposed scheme provides an efficient mechanism to incorporate and preserve structural information in the estimation process and results in significant enhancement in the recovery of flow channel structures.Citation
Sana, F., Katterbauer, K., Al-Naffouri, T., & Hoteit, I. (2015). Enhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filter. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). doi:10.1109/igarss.2015.7326474Conference/Event name
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)ae974a485f413a2113503eed53cd6c53
10.1109/IGARSS.2015.7326474