| Title | An integrated methodology for hydrogeological assessment around industrial installations |
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| Author | Claprood, M; Couegnas, C; Gloaguen, E; Krimissa, M; Paradis, D |
| Source | 160826, 2019 p. 1-4, https://doi.org/10.3997/2214-4609.201902388 |
| Image |  |
| Year | 2019 |
| Alt Series | Natural Resources Canada, Contribution Series 20200306 |
| Publisher | European Association of Geoscientists and Engineers, EAGEo |
| Meeting | 25th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2019, NSG 2019; The Hague; NL; September 8-12, 2019 |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | hydrogeology; Science and Technology |
| Illustrations | diagrams |
| Released | 2019 09 01 |
| Abstract | We present a nested hydrogeological characterization methodology to optimize the use of existing data and better plan the acquisition of new data around man-made installations. The workflow is presented
at an industrial site where the construction of deep infrastructures has disturbed the local hydrogeology settings. The first step is to lever historical data coming from hydrogeological tests and civil engineering operations before and during the
construction of the industrial installations to build the frame of hydrogeological model. Based on the review of this information, new geophysical data acquisition can be scheduled to refine the interfaces between geological units. This initial model
serves has a training image to simulate multiple equiprobable scenarios of the site geology while preserving the well information and the location of the buildings as, obviously, deterministic. These geological scenarios are populated with
anisotropic hydraulic conductivity fields using sequential Gaussian simulation. These heterogeneous hydraulic conductivity models are ran with a flow and transport simulation algorithm to constitute an ensemble of realizations that is used in an
ensemble Kalman time series assimilation scheme. |
| GEOSCAN ID | 326950 |
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