Estimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The Ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of Compressed Sensing (CS) have shown that estimating in a sparse domain, using algo
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.