Combining Clustering Methods With MPS to Estimate Structural Uncertainty for Hydrological Models

Troels Norvin Vilhelmsen*, Esben Auken, Anders Vest Christiansen, Adrian Sanchez Barfod, Pernille Aabye Marker, Peter Bauer-Gottwein

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

This study presents a novel expansion of the clay-fraction (CF)/resistivity clustering method aiming at developing realizations of subsurface structures based on multiple point statistics (MPS). The CF-resistivity clustering method is used to define a data driven training image (TI) for MPS simulations. By combining this TI with uncertainty estimates obtained from correlation between the resistivity models and the unique categories in the TI, subsurface realizations are generated honoring geophysical and lithological data. The generated subsurface realizations were calibrated in a steady state groundwater model. Forecasts of well catchment zones were derived based on two wells located in areas with different levels of structural uncertainty. The catchment probability maps derived from the structural realizations were compared with the well catchment forecasted by a deterministic subsurface structure, and we are able to capture this catchment within the estimated uncertainties. We believe that this study is the first to combine MPS methods with a complete data driven workflow going directly from lithological and geophysical data to realizations of the subsurface structures. The main benefits of this is that it is data driven, fast, reproducible, and transparent.
Original languageEnglish
Article number181
JournalFrontiers in Earth Science
Volume7
Number of pages15
ISSN2296-6463
DOIs
Publication statusPublished - 2019

Cite this

Vilhelmsen, Troels Norvin ; Auken, Esben ; Christiansen, Anders Vest ; Barfod, Adrian Sanchez ; Marker, Pernille Aabye ; Bauer-Gottwein, Peter. / Combining Clustering Methods With MPS to Estimate Structural Uncertainty for Hydrological Models. In: Frontiers in Earth Science. 2019 ; Vol. 7.
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title = "Combining Clustering Methods With MPS to Estimate Structural Uncertainty for Hydrological Models",
abstract = "This study presents a novel expansion of the clay-fraction (CF)/resistivity clustering method aiming at developing realizations of subsurface structures based on multiple point statistics (MPS). The CF-resistivity clustering method is used to define a data driven training image (TI) for MPS simulations. By combining this TI with uncertainty estimates obtained from correlation between the resistivity models and the unique categories in the TI, subsurface realizations are generated honoring geophysical and lithological data. The generated subsurface realizations were calibrated in a steady state groundwater model. Forecasts of well catchment zones were derived based on two wells located in areas with different levels of structural uncertainty. The catchment probability maps derived from the structural realizations were compared with the well catchment forecasted by a deterministic subsurface structure, and we are able to capture this catchment within the estimated uncertainties. We believe that this study is the first to combine MPS methods with a complete data driven workflow going directly from lithological and geophysical data to realizations of the subsurface structures. The main benefits of this is that it is data driven, fast, reproducible, and transparent.",
author = "Vilhelmsen, {Troels Norvin} and Esben Auken and Christiansen, {Anders Vest} and Barfod, {Adrian Sanchez} and Marker, {Pernille Aabye} and Peter Bauer-Gottwein",
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language = "English",
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journal = "Frontiers in Earth Science",
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Combining Clustering Methods With MPS to Estimate Structural Uncertainty for Hydrological Models. / Vilhelmsen, Troels Norvin; Auken, Esben; Christiansen, Anders Vest; Barfod, Adrian Sanchez; Marker, Pernille Aabye; Bauer-Gottwein, Peter.

In: Frontiers in Earth Science, Vol. 7, 181, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - Combining Clustering Methods With MPS to Estimate Structural Uncertainty for Hydrological Models

AU - Vilhelmsen, Troels Norvin

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AU - Christiansen, Anders Vest

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AU - Marker, Pernille Aabye

AU - Bauer-Gottwein, Peter

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AB - This study presents a novel expansion of the clay-fraction (CF)/resistivity clustering method aiming at developing realizations of subsurface structures based on multiple point statistics (MPS). The CF-resistivity clustering method is used to define a data driven training image (TI) for MPS simulations. By combining this TI with uncertainty estimates obtained from correlation between the resistivity models and the unique categories in the TI, subsurface realizations are generated honoring geophysical and lithological data. The generated subsurface realizations were calibrated in a steady state groundwater model. Forecasts of well catchment zones were derived based on two wells located in areas with different levels of structural uncertainty. The catchment probability maps derived from the structural realizations were compared with the well catchment forecasted by a deterministic subsurface structure, and we are able to capture this catchment within the estimated uncertainties. We believe that this study is the first to combine MPS methods with a complete data driven workflow going directly from lithological and geophysical data to realizations of the subsurface structures. The main benefits of this is that it is data driven, fast, reproducible, and transparent.

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