Effects of variable-density flow on the value-of-information of pressure and concentration data for aquifer characterization. (January 2020)

• Record Type: Journal Article
• Authors: Yoon, Seonkyoo; Lee, Seunghak; Williams, John R.; Kang, Peter K.
• Is Part Of: Advances in water resources. Volume 135(2020)
• Publication Date: 2020-01
• Subjects: Variable-density flow -- Joint inversion -- Mutual information -- Value of information -- Ensemble Kalman filter -- Aquifer characterization; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2019.103468
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: Variable-density flow controls the coupling between pressure and concentration data. The coupling effect is formally quantified by the analysis of mutual information. We analyze the value-of-information of joint pressure-concentration inversion. The advantage of joint inversion decreases as the coupling effect increases. The amount of shared information decreases as permeability heterogeneity increases. Abstract: Predicting variable-density flow and transport in aquifers is critical for the management of many coastal saline aquifers. Accurate characterization of hydrogeological parameters is critical for prediction, and the characterization is often conducted by assimilating data into models. However, few studies have investigated the underlying physics controlling the value-of-information (VOI) of data for aquifer characterization. In this study, we show how a greater understanding of the underlying physics controlling pressure and concentration data coupling can lead to improved characterization. In variable-density flow, the key physics that controls the VOI of pressure and concentration data is the non-linear coupling between flow and transport via fluid density which causes the pressure field to experience transient changes according to the evolution of salinity distribution. We first demonstrate the coupling between pressure and concentration data using information theory, and then systematically investigate how the variable-density flow impacts the VOI of these data in relation to permeability estimation. Using an ensemble Kalman filter, we estimate the permeability field of saline aquifer systems in two scenarios of data usage: pressure data only, and pressure and concentration data jointly. This study demonstrates that, regardless of the data usage scenario, the maximum VOI of data is obtained when free convection and forced convection are balanced. We further show that the advantage of joint inversion of pressure and concentration data decreases as the coupling effect between flow and transport increases. Finally, we study how the level of permeability field heterogeneity affects the coupling, which in turn controls VOI of pressure and concentration data.