Version 1
: Received: 14 September 2018 / Approved: 14 September 2018 / Online: 14 September 2018 (16:31:59 CEST)
How to cite:
Rühaak, W.; Bär, K.; Sass, I. Geostatistical Interpolation of Subsurface Properties by Combining Measurements and Models. Preprints2018, 2018090270. https://doi.org/10.20944/preprints201809.0270.v1
Rühaak, W.; Bär, K.; Sass, I. Geostatistical Interpolation of Subsurface Properties by Combining Measurements and Models. Preprints 2018, 2018090270. https://doi.org/10.20944/preprints201809.0270.v1
Rühaak, W.; Bär, K.; Sass, I. Geostatistical Interpolation of Subsurface Properties by Combining Measurements and Models. Preprints2018, 2018090270. https://doi.org/10.20944/preprints201809.0270.v1
APA Style
Rühaak, W., Bär, K., & Sass, I. (2018). Geostatistical Interpolation of Subsurface Properties by Combining Measurements and Models. Preprints. https://doi.org/10.20944/preprints201809.0270.v1
Chicago/Turabian Style
Rühaak, W., Kristian Bär and Ingo Sass. 2018 "Geostatistical Interpolation of Subsurface Properties by Combining Measurements and Models" Preprints. https://doi.org/10.20944/preprints201809.0270.v1
Abstract
Subsurface temperature is the key parameter in geothermal exploration. An accurate estimation of the reservoir temperature is of high importance and usually done either by interpolation of borehole temperature measurement data or numerical modeling. However, temperature measurements at depths which are of interest for deep geothermal applications (usually deeper than 2 km) are generally sparse. A pure interpolation of such sparse data always involves large uncertainties and usually neglects knowledge of the 3D reservoir geometry or the rock and reservoir properties governing the heat transport. Classical numerical modeling approaches at regional scale usually only include conductive heat transport and do not reflect thermal anomalies along faults created by convective transport. These thermal anomalies however are usually the target of geothermal exploitation. 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 3D numerical conductive temperature model was used as secondary information. This way the interpolation result reflects thermal anomalies detected by direct temperature measurements as well as the geological structure. This results in a considerable quality increase of the subsurface temperature estimation.
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
