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

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.