Publications 2014

​Constraining a compositional flow model with flow-chemical data using an ensemble Kalman filter
M. Elgharamti, A. Kadoura, S. Sun, J. Valstar, and I. Hoteit
Water Resources Research, Volume 50, Issue 3, Pages 2444–2467, 2014
M. Elgharamti, A. Kadoura, S. Sun, J. Valstar, and I. Hoteit
Chemical composition data; Compositional flow models; Ensemble Kalman filtering; Joint and dual updating schemes; State and parameter estimation
2014
​sothermal compositional flow models require coupling transient compressible flows and advective transport systems of various chemical species in subsurface porous media. Building such numerical models is quite challenging and may be subject to many sources of uncertainties because of possible incomplete representation of some geological parameters that characterize the system's processes. Advanced data assimilation methods, such as the ensemble Kalman filter (EnKF), can be used to calibrate these models by incorporating available data. In this work, we consider the problem of estimating reservoir permeability using information about phase pressure as well as the chemical properties of fluid components. We carry out state-parameter estimation experiments using joint and dual updating schemes in the context of the EnKF with a two-dimensional single-phase compositional flow model (CFM). Quantitative and statistical analyses are performed to evaluate and compare the performance of the assimilation schemes. Our results indicate that including chemical composition data significantly enhances the accuracy of the permeability estimates. In addition, composition data provide more information to estimate system states and parameters than do standard pressure data. The dual state-parameter estimation scheme provides about 10% more accurate permeability estimates on average than the joint scheme when implemented with the same ensemble members, at the cost of twice more forward model integrations. At similar computational cost, the dual approach becomes only beneficial after using large enough ensembles.


DOI: 10.1002/2013WR014830