Linsel, A. ; Wiesler, S. ; Haas, J. ; Bär, K. ; Hinderer, M. (2020)
Accounting for Local Geological Variability in Sequential Simulations—Concept and Application.
In: International Journal of Geo-Information, 9 (6)
doi: 10.3390/ijgi9060409
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Heterogeneity-preserving property models of subsurface regions are commonly constructed by means of sequential simulations. Sequential Gaussian simulation (SGS) and direct sequential simulation (DSS) draw values from a local probability density function that is described by the simple kriging estimate and the local simple kriging variance at unsampled locations. The local simple kriging variance, however, does not necessarily reflect the geological variability being present at subsets of the target domain. In order to address that issue, we propose a new workflow that implements two modified versions of the popular SGS and DSS algorithms. Both modifications, namely, LVM-DSS and LVM-SGS, aim at simulating values by means of introducing a local variance model (LVM). The LVM is a measurement-constrained and geology-driven global representation of the locally observable variance of a property. The proposed modified algorithms construct the local probability density function with the LVM instead of using the simple kriging variance, while still using the simple kriging estimate as the best linear unbiased estimator. In an outcrop analog study, we can demonstrate that the local simple kriging variance in sequential simulations tends to underestimate the locally observed geological variability in the target domain and certainly does not account for the spatial distribution of the geological heterogeneity. The proposed simulation algorithms reproduce the global histogram, the global heterogeneity, and the considered variogram model in the range of ergodic fluctuations. LVM-SGS outperforms the other algorithms regarding the reproduction of the variogram model. While DSS and SGS generate a randomly distributed heterogeneity, the modified algorithms reproduce a geologically reasonable spatial distribution of heterogeneity instead. The new workflow allows for the integration of continuous geological trends into sequential simulations rather than using class-based approaches such as the indicator simulation technique.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Linsel, A. ; Wiesler, S. ; Haas, J. ; Bär, K. ; Hinderer, M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Accounting for Local Geological Variability in Sequential Simulations—Concept and Application |
| Sprache: | Englisch |
| Publikationsjahr: | 26 Juni 2020 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Geo-Information |
| Jahrgang/Volume einer Zeitschrift: | 9 |
| (Heft-)Nummer: | 6 |
| DOI: | 10.3390/ijgi9060409 |
| URL / URN: | https://www.mdpi.com/2220-9964/9/6/409 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Heterogeneity-preserving property models of subsurface regions are commonly constructed by means of sequential simulations. Sequential Gaussian simulation (SGS) and direct sequential simulation (DSS) draw values from a local probability density function that is described by the simple kriging estimate and the local simple kriging variance at unsampled locations. The local simple kriging variance, however, does not necessarily reflect the geological variability being present at subsets of the target domain. In order to address that issue, we propose a new workflow that implements two modified versions of the popular SGS and DSS algorithms. Both modifications, namely, LVM-DSS and LVM-SGS, aim at simulating values by means of introducing a local variance model (LVM). The LVM is a measurement-constrained and geology-driven global representation of the locally observable variance of a property. The proposed modified algorithms construct the local probability density function with the LVM instead of using the simple kriging variance, while still using the simple kriging estimate as the best linear unbiased estimator. In an outcrop analog study, we can demonstrate that the local simple kriging variance in sequential simulations tends to underestimate the locally observed geological variability in the target domain and certainly does not account for the spatial distribution of the geological heterogeneity. The proposed simulation algorithms reproduce the global histogram, the global heterogeneity, and the considered variogram model in the range of ergodic fluctuations. LVM-SGS outperforms the other algorithms regarding the reproduction of the variogram model. While DSS and SGS generate a randomly distributed heterogeneity, the modified algorithms reproduce a geologically reasonable spatial distribution of heterogeneity instead. The new workflow allows for the integration of continuous geological trends into sequential simulations rather than using class-based approaches such as the indicator simulation technique. |
| Freie Schlagworte: | sequential simulation; local variance model; geological heterogeneity; uncertainty estimation; subset variability |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Geowissenschaften > Fachgebiet Angewandte Geothermie 11 Fachbereich Material- und Geowissenschaften > Geowissenschaften > Fachgebiet Angewandte Sedimentgeologie |
| Hinterlegungsdatum: | 29 Jun 2020 05:43 |
| Letzte Änderung: | 03 Jul 2024 02:45 |
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Accounting for Local Geological Variability in Sequential Simulations—Concept and Application. (deposited 30 Jul 2021 08:03)
- Accounting for Local Geological Variability in Sequential Simulations—Concept and Application. (deposited 29 Jun 2020 05:43) [Gegenwärtig angezeigt]
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