Maximizing the value of pressure data in saline aquifer characterization

Seonkyoo Yoon, John R. Williams, Ruben Juanes, Peter K. Kang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The injection and storage of freshwater in saline aquifers for the purpose of managed aquifer recharge is an important technology that can help ensure sustainable water resources. As a result of the density difference between the injected freshwater and ambient saline groundwater, the pressure field is coupled to the spatial salinity distribution, and therefore experiences transient changes. The effect of variable density can be quantified by the mixed convection ratio, which is a ratio between the strength of two convection processes: free convection due to the density differences and forced convection due to hydraulic gradients. We combine a density-dependent flow and transport simulator with an ensemble Kalman filter (EnKF) to analyze the effects of freshwater injection rates on the value-of-information of transient pressure data for saline aquifer characterization. The EnKF is applied to sequentially estimate heterogeneous aquifer permeability fields using real-time pressure data. The performance of the permeability estimation is analyzed in terms of the accuracy and the uncertainty of the estimated permeability fields as well as the predictability of breakthrough curve arrival times in a realistic push-pull setting. This study demonstrates that injecting fluids at a rate that balances the two characteristic convections can maximize the value of pressure data for saline aquifer characterization.

Original languageEnglish (US)
Pages (from-to)14-28
Number of pages15
JournalAdvances in Water Resources
Volume109
DOIs
StatePublished - Nov 2017
Externally publishedYes

Bibliographical note

Funding Information:
Seonkyoo Yoon acknowledges support from the Massachusetts Institute of Technology (MIT) Energy Initiative (MITEI) Seed Fund Program. Peter K. Kang acknowledges a grant (code 17AWMP-B066761-05 ) from the AWMP Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government and the support from Future Research Program ( 2E27030 ) funded by the Korea Institute of Science and Technology (KIST).

Publisher Copyright:
© 2017 Elsevier Ltd

Keywords

  • Density-dependent flow
  • Ensemble Kalman filter
  • Inverse modeling
  • Managed aquifer recharge
  • Permeability estimation
  • Value of information

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