Effects of variable-density flow on the value-of-information of pressure and concentration data for aquifer characterization

Seonkyoo Yoon, Seunghak Lee, John R. Williams, Peter K. Kang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Article number103468
JournalAdvances in Water Resources
Volume135
DOIs
StatePublished - Jan 2020

Bibliographical note

Funding Information:
The authors acknowledge a grant (17AWMP-B066761-05) from AWMP Program funded by Ministry of Land, Infrastructure and Transport of Korean government and the support from Future Research Program (2E27030) funded by the Korea Institute of Science and Technology (KIST). PKK also acknowledges the College of Science & Engineering at the University of Minnesota and the George and Orpha Gibson Endowment for its generous support of Hydrogeology, and the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR). We thank the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for computational resources and support.

Funding Information:
The authors acknowledge a grant ( 17AWMP-B066761-05 ) from AWMP Program funded by Ministry of Land, Infrastructure and Transport of Korean government and the support from Future Research Program ( 2E27030 ) funded by the Korea Institute of Science and Technology (KIST). PKK also acknowledges the College of Science & Engineering at the University of Minnesota and the George and Orpha Gibson Endowment for its generous support of Hydrogeology, and the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR). We thank the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for computational resources and support.

Publisher Copyright:
© 2019 Elsevier Ltd

Keywords

  • Aquifer characterization
  • Ensemble Kalman filter
  • Joint inversion
  • Mutual information
  • Value of information
  • Variable-density flow

Fingerprint

Dive into the research topics of 'Effects of variable-density flow on the value-of-information of pressure and concentration data for aquifer characterization'. Together they form a unique fingerprint.

Cite this