Rühaak, W. ; Bär, K. ; Sass, I. (2014)
Combining numerical modeling with geostatistical analysis for an improved reservoir exploration.
In: Energy Procedia, 59
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Subsurface temperature is one of the key parameters in geothermal exploration. The estimation of the reservoir temperature is of high importance and usually done either by interpolation of temperature data or numerical modeling. However, temperature measurements of depths larger than a few hundred meters are generally very sparse. A pure interpolation of such sparse data always involves big uncertainties and usually neglects knowledge of the reservoir geometry or reservoir properties.
Kriging with trend does allow including secondary data to improve the interpolation of the primary one. Using this approach temperature measurements of depths larger than 1,000 m of the federal state of Hessen/Germany have been interpolated in 3D. A conductive numerical 3D temperature model was used as secondary information. This way the interpolation result reflects also the geological structure. As a result the quality of the estimation improves considerably.
| Typ des Eintrags: | Artikel | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Erschienen: | 2014 | ||||||||
| Autor(en): | Rühaak, W. ; Bär, K. ; Sass, I. | ||||||||
| Art des Eintrags: | Bibliographie | ||||||||
| Titel: | Combining numerical modeling with geostatistical analysis for an improved reservoir exploration | ||||||||
| Sprache: | Englisch | ||||||||
| Publikationsjahr: | 2014 | ||||||||
| Verlag: | Elsevier | ||||||||
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Energy Procedia | ||||||||
| Jahrgang/Volume einer Zeitschrift: | 59 | ||||||||
| URL / URN: | http://www.sciencedirect.com/science/article/pii/S1876610214... | ||||||||
| Zugehörige Links: | |||||||||
| Kurzbeschreibung (Abstract): | Subsurface temperature is one of the key parameters in geothermal exploration. The estimation of the reservoir temperature is of high importance and usually done either by interpolation of temperature data or numerical modeling. However, temperature measurements of depths larger than a few hundred meters are generally very sparse. A pure interpolation of such sparse data always involves big uncertainties and usually neglects knowledge of the reservoir geometry or reservoir properties. Kriging with trend does allow including secondary data to improve the interpolation of the primary one. Using this approach temperature measurements of depths larger than 1,000 m of the federal state of Hessen/Germany have been interpolated in 3D. A conductive numerical 3D temperature model was used as secondary information. This way the interpolation result reflects also the geological structure. As a result the quality of the estimation improves considerably. |
||||||||
| Schlagworte: |
|
||||||||
| Zusätzliche Informationen: | European Geosciences Union General Assembly 2014, EGU Division Energy, Resources & the Environment (ERE) |
||||||||
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Geowissenschaften > Fachgebiet Angewandte Geothermie |
||||||||
| Hinterlegungsdatum: | 11 Nov 2015 18:14 | ||||||||
| Letzte Änderung: | 03 Jun 2018 21:26 | ||||||||
| PPN: | |||||||||
| Schlagworte: |
|
||||||||
| Export: | |||||||||
| Suche nach Titel in: | TUfind oder in Google | ||||||||
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum