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Uncertainty Assessment of Corrected Bottom-Hole Temperatures Based on Monte Carlo Techniques

Author

Listed:
  • Felix Schölderle

    (Chair of Hydrogeology, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany)

  • Gregor Götzl

    (Department Hydrogeology & Geothermal Energy, Geological Survey of Austria, Neulinggasse 38, 1030 Vienna, Austria)

  • Florian Einsiedl

    (Chair of Hydrogeology, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany)

  • Kai Zosseder

    (Chair of Hydrogeology, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany)

Abstract

Most temperature predictions for deep geothermal applications rely on correcting bottom-hole temperatures (BHTs) to undisturbed or static formation temperatures (SFTs). The data used for BHT correction are usually of low quality due to a lack of information and poor documentation, and the uncertainty of the corrected SFT is therefore unknown. It is supposed that the error within the input data exceeds the error due to the uncertainty of the different correction schemes. To verify this, we combined a global sensitivity study with Sobol indices of six easy-to-use conventional correction schemes of the BHT data set of the Bavarian Molasse Basin with an uncertainty study and developed a workflow that aims at presenting a valid error range of the corrected SFTs depending on the quality of their input data. The results give an indication of which of the investigated correction methods should be used depending on the input data, as well as show that the unknown error in the input parameters exceeds the error of the individual BHT correction methods as such. The developed a priori uncertainty-based BHT correction helps to provide a real estimate of the subsurface temperatures needed for geothermal prospecting and probabilistic risk assessment.

Suggested Citation

  • Felix Schölderle & Gregor Götzl & Florian Einsiedl & Kai Zosseder, 2022. "Uncertainty Assessment of Corrected Bottom-Hole Temperatures Based on Monte Carlo Techniques," Energies, MDPI, vol. 15(17), pages 1-27, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6367-:d:903350
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    References listed on IDEAS

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    1. Marco Ratto, 2008. "Analysing DSGE Models with Global Sensitivity Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 31(2), pages 115-139, March.
    2. Changwei Liu & Kewen Li & Youguang Chen & Lin Jia & Dong Ma, 2016. "Static Formation Temperature Prediction Based on Bottom Hole Temperature," Energies, MDPI, vol. 9(8), pages 1-14, August.
    3. Eyerer, S. & Schifflechner, C. & Hofbauer, S. & Bauer, W. & Wieland, C. & Spliethoff, H., 2020. "Combined heat and power from hydrothermal geothermal resources in Germany: An assessment of the potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
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